Archaeological Information ManagementE.S.Lohse

Archaeological Collections: Electronic DatabasesContemporary archaeology must move from simple artifact collection to a charge to

collect, organize and distribute archaeological information. Excavations, testing and

survey produce artifacts and documentation that constitutes archaeological data. This

data must be harvested, salvaged and used for research and education purposes if

archaeology is to fulfill its potential of informing us about ourselves in the past and in the

present. The traditional practice of carefully storing special artifacts away in secure

drawers behind locked doors is no longer viable. Archaeologists are no longer working

on shoe-string budgets on individually designed research projects. Archaeology is now

funded by nation states and corporations, with contracted work mandated by laws and

regulations. Research requires permits and consultation with governmental and

advocacy groups. The scope of research is directed by contractual agreements and

resulting information is required to be published and distributed within approved venues.

Old work must be integrated in new work, and old and new collections must be securely

and carefully stored for future generations. This paper addresses the current state of

archaeological knowledge handling, and emphasizes that archaeologists must move

aggressively to computer information systems as the primary way to curate and

distribute archaeological knowledge.

Stewart (1997), in summarizing the course of archaeological data moving to

information, identified the “Great Chain of Being” in archaeological computing, moving

inexorably in logical stages from data collection, to data management, to data analysis,

and on to dissemination. Stewart notes, however, that we can appraise archaeological

use of IT and information systems as more of a complex, multi-stranded web than as a

linear feature on the computing landscape. Archaeological IT venues now cover

quantitative methods, statistics and classification, archaeometry, visualization (including

imaging, CAD, multimedia and virtual reality), expert systems, artificial intelligence, and

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GIS. All depend on effective design of usable digital archives and databases. This

assessment should not ignore enhanced education and publication venues as well (for

a history of the development of archaeological computing, see Booth 1995; Hansen

1993; Lock 1995; Reilly and Rahtz 1992; Webb 1986).

The term “database” became common in archaeology by the 1980s, supplanting the

earlier label “databanks” (Moffett 1984). Wilcox (1978) made one of the strongest early

pleas for use of personal computing and microcomputers to aid in site recording and

data retrieval, data analysis, and publication. Booth (1982) and Stewart (1980)

published some of the first articles on use of microcomputers and relational database

management systems. By 1988, Nick Ryan published a bibliography of over seven

hundred publications in eleven different categories on computer applications in

archaeology (Ryan 1988). By the 1990s, numbers of articles on a wide range of

computing venues expanded dramatically, causing Ucko (1992) to speculate on the

dramatic impact of computers and IT on archaeology (cf. Cheetham and Haigh 1992;

Hansen 1993). Today, computerization is a given, and there has been a decided shift

toward use of industry standard software and evolving standards for data recording and

retrieval. At the same time, government organizations are investigating moves toward

encompassing strategies for digital data management (Booth 1995; Clubb and Startin

1995; Clubb and Lang 1996; Murray 1995). The shift is away from straightforward data

collection to increasing public access (Booth 1996).

Stewart (1997) argues that the central issue to emerge in over twenty-five years of

archaeological computing is whether there has developed greater physical and

intellectual access to archaeology. We still are struggling to bring archaeological

collections as digital archives into arenas of ever greater professional and public

access. The backlog of information seems daunting, and though successful pilot

projects can be cited, we still are unsure of our level of success (cf. Stewart 1995;

Niccolucci and D’Anrea 2002).

The Need for Informatics and Data Theory

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These and other efforts are attempting to bring information online to support

scientific communication across disciplines and across the world. Accessible databases

are key, and accessibility is directed by success in defining standards and guidelines for

practice. Archaeological databases today cannot be static compilations of text fields and

numbers. Databases must strive to bring collections and documentation information to

users in dynamic environments. We must also design these databases to be

sustainable over the long term.

The use of electronic media to enhance communication is a major shift in the

conduct of science. Pure access to information is a boon to scientists as is their ability

to handle massive amounts of information. Cross-disciplinary and international

collaborations are booming. Keys to nurturing this shift in the computer realm are

building adequate metadata, migrating data and controlling access to information. Kling

and McKim (1998) express concern over unsupportable risks rising if this transformation

in scientific communication occurs in a pure laissez-faire environment. They point out

that we cannot assume that “everyone will catch on” to using e-media structures

eventually. They also argue that we cannot simply assume that various e-media

initiatives reflect a creative period of problem-solving. Perhaps these developments

instead reflect runaway agendas and proprietary interests that will eventually retard use

of powerful electronic venues. Kling and McKim are informatics analysts, and view a

lack of theory guiding this process as potentially hazardous. They note that huge

amounts of money, resources, and effort are being committed by government agencies,

by private firms and organizations, by academic departments, by publishers, by

professional societies, and by individual researchers for the development, maintenance

and promotion of variable communications technologies for the sciences. At the same

time, scientists and policy-makers have no accepted theory of how scholarly fields

should adopt and shape technology. Producers and users instead tend to work within

context-free models. Often, the result is ongoing prototyping and fledgling projects with

high promise and withered funding (e.g., Eiteljorg 2003). This result wastes funding,

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denies the efforts of researchers, and highlights the serious plight of data orphaned and

dying in marginal, decaying, dead systems and formats.

A powerful response to this lack of an information paradigm is the so-called

electronic communication reform movement, which has been recognized in the field of

e-publishing, but these concerns extend across all e-media(cf. Kling and Iacono1995;

Iacono and Kling 1996). Core instigators include a range of well known scientists (e.g.

Paul Ginsparg and Paul Harnard). Harnard is an outspoken advocate of radically

decentralized scholarly publishing that may or may not be peer-reviewed (cf. Brent

1995), and is the editor of the electronic journal “Psycholoquy.” He is also the originator

of the concept “scholarly skywriting,” where scientific communication is confined to

short, discursive, iterative bursts of e-communication (Harnad 1991). Ginsparg was a

developer of the Los Alamos National Labs Physics E-Print Server, a working paper

server used by high-energy physicists. This venue has found acceptance in the

communications system of that field (Odlyzko 1996). Many in the scientific e-

communication audience are accepting the admonishments of outside-the-box thinkers

like Harnad and Ginsparg, prompting analysts like Morton (1997) to identify a paradigm

shift toward electronic communication and away from hard-copy journals and archives,

in all forms, whether centralized or decentralized.

There is a shared ideology in this movement. The basic precept is that electronic

media is better than traditional media: e-communication will be less expensive, access

to e-media will be easier and wider, and systematic use of e-media will dramatically

speed up scientific communication (Kling and McKim 1998:2; Brent 1995). Examples of

actions subversive to the established publication industry abound in the field of e-

communication. The editors and organizers of the “Electronic Transactions on Artificial

Intelligence” (ETAI) have created an open article review process (ETAI 1997; Sandewall

1998). ETAI reviews in two phases: after submission, the article is open to public online

discussion for a period of three months; after author response, the article is reviewed for

acceptance using confidential peer review and journal level quality criteria. Another AI

journal, the “Journal of Artificial Intelligence” (JAIR), emphasizes online appendices and

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discussions of published articles. Scholars are encouraged to cite JAIR’s articles as

they would articles in paper journals, but JAIR is distributed without charge on the

Internet (Kling and Covi 1995).

The overriding characteristics of the e-media explosion in science communication

are differences in structures, roles and uses from one discipline or field to another.

Designing electronic applications in this new age will require close attention to the social

contexts in which scientists operate. Scientists in different arenas use e-resources very

differently in the conduct of their basic research and communication (Kling and Covi

1995; Walsh and Bayma 1996; Finholt and Brooks 1997; Kling and Covi 1997; Walsh

and Bayma 1997). Examples abound. The discipline of Particle Physics uses the E-Print

server at Los Alamos National Labs, and preprint servers at CERN, DESY and the

American Physical Society. Biologists tend to depend on broader access supplied by

publication of papers in archival journals, but researchers use digital databases like the

Protein Data Bank, Flybase, and Saccharomyces Genome Database, as repositories for

genomic sequences published in refereed journals (Letovsky 1998, 1999). The

discipline of information systems has created ISWORLD, an extensive, distributed web-

based collection of links, papers, course syllabi, tools and resources, sponsored by

“MIS Quarterly.”

Not all disciplines have followed the lead of particle physics in opening up papers

online. The American Psychological Association (1996) admonishes authors not to

place papers online at any stage. The American Chemical Society (1996) has a similar

policy regarding papers considered for publication in its journal. Not all scientific

societies are as strict with authors. The Association for Computing Machinery (1995)

copyright policy states that authors retain rights to their work including unlimited reuse

of the work with citation of ACM. The ACM sees its role not as sole provider but as

facilitator of information access. Similar policies are held in many computer applications

journals and newsletters in archaeology, such as “Internet Archaeology” (Internet

Archaeology 2003; Richards, Heyworth and Winters 2000; Winters 2002), and in the

major professional journal “Antiquity” (cf. Champion 1997).

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All e-forms, including pure e-journals, databases, and preprint servers are highly

valuable for communication within and across disciplines. Disciplines have different

strictures but access to data is a primary concern (cf. Mark Bide and Associates 2000).

For our discussion, it is important to note that friction arises between disciplines

emphasizing use of shared, static-but-growing databases like archaeologists and

biologists and disciplines that do not like computer science. Commonality is in wanting

to access data in shared electronic environments and in rapidly and easily

communicating with other scholars. These kinds of questions represent a growing body

of research about information technology and social change called Social Informatics

(MacKenzie and Wajcman 1985; Silverstone and Hirsch 1992; Williams 1997).

The social shaping of scientific communication systems includes: access to

resources including data; speed of work and results-sharing; selection of target

audiences for research, allocation of credit for work performed, and allocation of

professional status (cf. DiMaggio and Powell 1991). Kling and McKim (1998)

acknowledge that trust plays a central role in the articulation of all issues within all

disciplines. Scientists will only use a report if they are assured that it is legitimate.

Formal peer review is only one traditional process of legitimation. To be willing to share

information, scholars must have confidence that dissemination will not harm future

access to resources or their career enhancement.

Important issues in determining how actors in social systems work focus on required

research project costs, mutual visibility of the ongoing work, the degree of industrial or

corporate integration, and the degree of concentration of communication channels.

Costs can affect communication structures by requiring greater collaboration. Costs

may also increase visibility at the expense of imposition of greater and greater control.

Visibility is always a concern. Practitioners in some disciplines like Particle Physics

share research results online before publication. In other disciplines, researchers are

not aware of colleagues’ work because of sanctions requiring publication in formal peer-

reviewed settings. Increasingly transparent distribution systems may cause actors to

perceive lower risks correlated with sharing reports and data. Market trends buck this

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revelation, however, since industrial or corporate support, while a commendable boon to

accelerated research and development, typically creates authoritative, owner-driven

sanctions on information dissemination. Here, systems are opaque, hidden behind

secure doors. Release is carefully controlled, if allowed, and timing is geared to

corporate advantage and profit margin. Much of what we now do lies between these two

poles: open access and controlled access (cf. Williams 1997).

Database projects have been particularly prone to boom and bust cycles.

Development is prompted by high promise and demise is driven by low scholarly

acceptance or limited funding accrual. Star and Ruhleder (1996) record the creation of

an online “Worm Community System” for molecular biologists, which proved too

complicated and technical for the larger community of its users. It was abandoned and

recast as the web-based system “A.C. Elegans DataBase (ACEDB),” which has won

greater acceptance. Letovsky (1998) reported that many biologists initially invested in

the “Genome Database” (The Genome Database 2003) Human Genome Project,” only

to see financial support withdrawn because funders did not see adequate value for their

constituencies. The demise of the heralded “Archaeological Data Archive Project”

(Eiteljorg 2003) is an example for our own discipline.

Archaeological Data: Practical ConstructionsPerring and Vince, in an online project outline titled “Liberating Archaeological Data”

(1999), set out an online guide for bringing archaeological data out to view. The

expressed aim of their proposal is to describe ways of using IT to facilitate presentation

and dissemination of complex archaeological data. They admonish that this will unlock

research potential for classes of data and will encourage more amibitious approaches to

the study of archaeological sites, landscapes and assemblages. They cite Hodder

(1998) on how the Internet impacts organization of archaeological knowledge, allowing

a shift from hierarchical structures to open networks and flows. Though these impacts

have been documented, the majority of the archaeological community seems unaware

of the implications of these new approaches. This may be a direct result of the reality

that most archaeological research is initiated under national, federal and state

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mandates that do not favor innovative approaches. Archaeologists would seem to be

running an appreciable risk: that establishing a commercial basis for archaeological

research will fossilize research practice along traditional lines.

Archaeological typology practices and classification methods are obvious areas

where innovations in thinking will have to take place, gearing to producing optimal

structures in the organization of archaeological data that will be accessed and

distributed in electronic networks. Interpretive structures will have to be devised which

permit variable ways of grouping data. For instance, higher order groupings structuring

archaeological data (phase compilations and typologies) will have to be supplemented

by alternative analytical divisions of the data (e.g., functional classes irrespective of

material type; deposition classes; competing spatial and sequential boundaries).

Different conceptualizations of data structure will be required to develop flexible and

analytical data structures.

Perring and Vince (1999) list obvious shortfalls in traditional database constructions.

Results from excavations of complex archaeological sites are notoriously difficult to

study, and both published and “grey literature” reports commonly report on only a

fraction of the available evidence. Traditional interpretations typically follow a rigid linear

framework based on chronological groupings cast from stratigraphic analyses, providing

concrete narratives for successful publication, in which structures, sequences and

assemblages are built on reconstructed landscapes. Problems inherent in recasting

these structures to electronic databases include: post-excavation procedures are limited

by intractable data sets; important data remain inaccessible to the research community

because revising the structure is simply so expensive; there is a lack of integration

allowing descriptions of different data classes published in specialist reports to be linked

in the overall data structure; data structures are specific to individual investigations of

specific sites and data classes, limiting potential for synthesis with other investigations

using different data structures; research potential is effectively curtailed by narrowly

defined data structures.

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There are new methodologies that can be imposed in resurrecting and organizing

effective data structures but without new conceptual organizing approaches these will

only compound, not transcend the limitations noted. We cannot simply enter new data in

old structures into IT developments (HTML, interrelational databases, and GIS), and

expect to have working databases. The theory driven structure of the data must be

revised, coupled with advances in IT.

The 2000 Society for American Archaeology session “Digital Data: Preservation and

Re-Use” saw many of these issues addressed. Robinson (2000) summarized work on

the “Digital Archiving Pilot Project for Excavation Records” (DAPPER). DAPPER was a

collaborative venture between the Archaeology Data Service (ADS), English Heritage,

the Museum of London Archaeology Service (MOLAS) and the Oxford Archaeological

Unit. It focused on how digital project information could be most effectively archived,

how best to deliver data over the Internet, what was the best costed model for archiving

and delivery, how to assess user reaction to digital project archives, provide examples

of best practice in archiving, and explore the close relationship between digital archiving

and publication. Robinson points out that a traditional archive documenting an

archaeological project would be transferred to a museum, although Swain’s (1999)

survey of archaeological archives in England concludes that most museums do not

have the technology to store, access and curate archives containing computer files.

Condron et al. (1999) reinforces this assessment. DAPPER was a pragmatic approach

to the imminent threat of systematic loss of electronic archaeological data.

Two large high profile collections were chosen as the ADS pilot study: the Royal

Opera House excavated by the Museum of London Archaeology Service and Eysham

Abbey excavated by the Oxford Archaeological Unit. Both projects were at the

dissemination stage and had different sorts of digital archives to deposit. The projects

were done by two different archaeological units with different working practices.

The Eynsham Abby digital archive contained text files, databases in comma

delimited text, JPG images, a 3-D reconstruction of the medieval abbey, and digitized

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drawings. The Royal Opera House archive consisted of database files and GIS files

summarizing context, artifact and ecofact attribute sets (Robinson 2000).

These data sets were designed to be accessed using the Archaeology Data Service

ArchSearch Catalog. Site metadata records can now be searched using keywords.

Flagged records can then be followed to more detailed catalog records. A Project

Archive button in the left hand frame of the ArchSearch window allows the user to list

projects with downloadable resources. Both the MOLAS and OAU have hotlinks to

connect their own web pages to the DAPPER project archives.

A central issue in resource delivery was the user interface, and whether this should

be aesthetically pleasing or simply allow clean access to raw data. Emphasis on

usability was abandoned in the development phase because it was deemed very

expensive and perhaps would have created problems in the realm of data migration.

The compromise was to present data in standard formats with online support

documentation to spur data reuse. This was seen as sustainable and more cost-

effective.

The Archaeology Data Service receives core funding from the UK Higher Education

sector and aims its data resources at the scholarly community. The ADS has sought

funding to repurpose the DAPPER raw data into Internet deliverable teaching modules

to enhance future use of the digital excavation archives.

Cost was summarized by Robinson (2000) and is an interesting footnote to our

discussion of digital archiving. Robinson reports that digital archiving of Eynsham

Abbey cost 1.2% of the excavation and post-excavation budgets. Digital archiving of the

Royal Opera House collections cost .1% of the total project cost. The cost difference is

attributed to differences in analysis during the post-excavation stage on the projects.

For example, digital stratigraphic drawings are available for both sites, but the ROH-

MOLAS excavation used GIS, creating three deposit files, while Eynsham Abbey, done

in CAD, deposited 404 site plan files, 80 structure plans, 15 phase plans, two sections,

a trench plan, a composite plan, and a 3-D reconstruction. Robinson notes, however,

that it is not a question of whether GIS or CAD is most cost-effective. The GIS archive

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requires special training to use. The CAD archive is constructed like a digital

publication, with separate structure and phase plans. The GIS database is a more

powerful research venue while the CAD archive is the better nonspecialist venue.

The Archaeology Data Service has recognized that the Royal Opera House and

Eynsham Abbey projects are showy, high profile, well funded excavation projects, and

that many archaeological projects will not merit this level of attention and expenditure.

ADS-DAPPER defined four different levels of digital archive. The index level archive or

minimum digital archive, where the project did not merit further assessment work. This

contains an index record for the ADS catalog and a site summary document. The

assessment level archive or larger digital archive, which maintains an index record, the

project design, the assessment report, specialist level databases, and a site matrix. The

research level archive, a large, complex digital archive, warranted when the project is

seen to be significant for analysis beyond the assessment performed. This archive is

not integrated into the final project report and holds the results of analytical and

publication process. It will contain an index record, a project design, the research

database with stratigraphic databases, digital plans, site matrix, artifactual and

ecofactual databases, the site, and artifactual and ecofactual reports. Finally, the

integrated archive, which holds scholarship resulting from ongoing analysis of projects

published as traditional archaeological monographs, linking texts seamlessly with the

digital site archive. Users should be able to query the range of site data through various

interfaces, including searchable relational databases and web-based GIS. The potential

is to bring unwieldy technical data out of the excavation report and locate it in a

universally accessible digital environment.

DAPPER constitutes the first functional online digital project archive. It created a

costed model for the ADS in working with commercial archaeology. It also resulted in

reassessment of post-excavation procedures and documentation methods implemented

by the Museum of London Archaeological Service and the Oxford Archaeological Unit.

DAPPER suggests that digital archiving can proceed for less than 5-1% of the total

archaeological project budget. DAPPER has also received high numbers of visitors who

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have downloaded thousands of digital files. The DAPPER resources have also been

used as teaching data sets in the Archaeological Information Systems Masters degree

developed by the University of York.

Eitlejorg (2000) admonishes that the real goal of any digital archive project is to

ensure reuse of the data. He considers three kind of digital data: databases, CAD

models, and GIS data sets, chosen because they require access in a highly interactive

digital environment. Eiteljorg in particular is concerned with the skills needed by users to

access these materials, identifying four distinct levels: skills needed to use data files if

the appropriate software is available; skills required if the files are not in the format

required by the user; skills needed to evaluate the quality of the data; and skills needed

to aggregate the data.

It is axiomatic that data will be stored in formats devised by other scholars. This data

will have been organized according to the needs and perceptions of this original

scholar, so the user must know something about how the original data were gathered,

organized, entered and stored. For example, in a CAD model the user must know how

the model has been segmented and how the data segments have been incorporated

into the CAD layer names. For GIS data sets, the user must know the scales used for

the map data, and how the data tables were constructed.

Data translations will be tricky for the user of the digital archive. An example cited by

Eitlejorg is the DBF format used in dBase. Moving the data tables is straightforward but

the user will encounter problems in complex databases with related or linked tables.

These relationships are not included in the DBF format and must be specified in

accompanying documentation. The user must have access to this documentation.

Data quality evaluations will be a constant concerns for digital archive users.

Questions about whether the data is primary or secondary in nature will be obvious. The

necessary scholarship framed in careful attributions will be required documentation.

Questions concerning whether all potential data were reported on are pertinent. Was

collection methodology systematic? How were corrections to the data made? Have

these modifications to the original data sets been tracked? Evaluation of the digital data

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is a time consuming and complex task requiring high levels of professional expertise.

Without adequate documentation the information needed for users of the digital archive

is lost. As Eiteljorg (2000) notes, data producers and users must instruct the archives,

even carefully designed projects like the Archaeology Data Service. Producers and

users must provide the parameters for data manipulation.

Metadata that documents the data resource, specifying the information that the user

needs to adequately use and explore the digital resource, is the required bridge

between data and data use (Cartney and Robertson 2000; Michener et al. 1997).

MetadataMetadata can be defined as data about data, and provides information essential to

use of any database (cf. Quine 2001 on role of data standards). Metadata can refer to

an agreed upon set of fields and associated lexicon or it can be a detailed description of

measurements systems and rules for application. Metadata are required so that the

user can make intelligent decisions in selecting, using, adding to, or translating a

database. A library card catalogue holds metadata that allows users to find particular

books. Maps include metadata as scales, dates of survey, and dates of publication. For

electronic resources, there are an increasingly large number of standards. MARC, a

Machine Readable Catalog, is used in library cataloguing (British Library 1980; Library

of Congress 1994). The Text Encoding Initiative (TEI) allows standardized description of

electronic texts. The Directory Interchange Format (DIF) provides metadata for satellite

imagery (GCMD 1996). The U.S. National Spatial Data Infrastructure (NSDI)

approaches complex descriptions of spatial data. Content standards have been defined

for U.S. geospatial metadata (CSDGM 2003; FGDC 1997) The U.K. National Geospatial

Database (Nanson et al. 1995) integrates governmental and nongovernmental spatial

data. NASA has produced the Global Change Master Directory, which presents a

writer’s guide for DIF - Directory Interchange Format (NASA 2003).

Miller (1997:4) characterizes these kinds of schemes as extremely complex and

geared explicitly to creation by experts and interpretation by computers, rather than

designed to facilitate information dispersal to as wide a range of users as possible. They

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operate within a narrowly defined field of work and are not suited to describe wide

ranges of resources. Miller applauds eLib projects such ROADS and ADAM. The Arts

and Humanities Data Service (AHDS) for archaeology, history, text and the performing

arts is seen as particularly productive. Both AHDS and ADAM use the Dublin Core

Metadata Element Set (cf. Miller 1996, 1997a, 1997b).

The Dublin Core has been developed to supply metadata descriptions between the

crude metadata of search engines and the complex systems developed for MARC and

the Federal Geographic Data Committee (FGDC 1994) (Dempsey 1996; Dublin Core

2002). The Dublin Core model can describe resources available on the Internet and can

be used to insert a range of file types from simple HyperText Markup Language (HTML)

to Postscript files and other image formats (Miller 1996; Weibel 1996). The Dublin Core

consists of thirteen core elements, each of which can be extended by use of Scheme

and Type qualifiers.

Scheme and Type qualifiers are used to better describe the resource. The Scheme

qualifier identifies any recognized coding system used in the description of a specific

Dublin Core element, and allows consistency and standardization. Scheme should only

refer to an existing coding system such as the Internet Media Type (IMT) or to the

International Standards Organization standard on dates (ISO31). The Type qualifier is

used to identify, as in a name, email address, or the like.

Designers feel that any necessary extensions of the Dublin Core should be included

in a separate framework as in the Warwick Framework (Lagoze et al. 1996).

Descriptions stored there may be from a different metadata scheme such as DIF or

FGDC, or could be simple extensions for the thirteen Dublin Core elements.

Databases

Metadata associated with a database should act to improve or restrict access to

data, facilitate sharing and interoperability, and characterize and index data. Metadata

designed to support data quality and longevity is the inherent concern of any database

construction.

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Rothenberg (1995) offers a succinct overview of the factors to be considered in

database design and development, and focuses specifically on inherent problems in

maintaining digital records as software becomes .

Rothenberg (1995) admonishes that data are a model of the real world, a description

that is arbitrary and biased. Data models incorporate very different data views. For

example, the speed of an object through a medium might be measured as slow -

medium - fast, as a single numerical value as in 20mph, or as a table of numerical

values. The choice of the kind of data to produce is decided prior to the building of the

data model.

Rothenberg (1997), in discussing verification, validation and certification of data

quality, notes that the assessment cannot be a binary value as in good or bad. It must

instead be evaluated in the specific context of use, and seen as a desire to move from

evaluation to improved quality. He notes two quality attributes: objective correctness

(accuracy and consistency) and appropriateness for the intended purpose. It is

axiomatic that data users will not be able to control data quality if data are taken from

outside or intermediate sources. All data must be augmented with metadata to record

information needed to assess data quality, record the results of assessments, and

support process control. Producers must perform explicit verification, validation, and

certification on the data, using metadata to direct this activity and to record results.

Producers must also establish control over the data processes to improve data

transformations, using metadata to support this activity and to record the results.

Rothenberg (1996:6-8) lists contextual categories for data quality that include

adequate description and meaning, specification of intended use and range of purposes

and constraints, requirements for access and use, description and rationale for structure

or design, global relationships to other databases, and update cycle information. Source

information includes identification of source and assessment of source credibility,

characterization of classification, accessibility and reproducibility, and clear notice of

release authority. Rothenberg (1996:6-8) lists other criteria, including data-element and

data-value metadata.

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Limited media life and rapid obsolescence of software and hardware highlight the

need for concern with longevity of electronic data records. Increasing use of graphics,

hypertext, linked structures and multimedia only accelerates this projected

obsolescence. Data records are becoming increasingly dependent on specific software

for continued interpretation. Data files will become increasingly useless without software

to interpret the structure and meaning. Traditional hard copy documents and records

have linear content that is relatively independent of this concern. For electronic media,

record keeping paradigms are essential, and are evolving in direct response to

accelerating software paradigm changes (Michelson and Rothenberg 1992).

We should save original records, and importantly, we need to save application

software and descriptions of the required hardware environment as prerequisites of

constructing emulators in the future. Compression should not be considered an option.

Saved bit-streams should be copied verbatim. Annotated metadata must be transparent

and act as a “bootstrap standard” (remember that ASCII will change to Unicode to some

other standard) (cf. Rothenberg 1996).

Information StandardsMuch of the current discussion on information standards in archaeology is headed

by the Getty Information Institute and the International Committee for Documentation of

the International Council of Museums. These two organizations sponsored an initial

meeting on the urgent need for standards in Canterbury, England, in September 1991.

Results of this overview were published in a brochure titled “Developments in

International Museum and Cultural Heritage Information Standards,” first published in

1993 and updated in July 1995.

Standards are models that organizations, projects, and vendors can use as the basis

for creating information systems and guidelines. These are rules for structuring

information, enabling data entered into the system to be reliably read, sorted, indexed,

retrieved and distributed between systems. The most compelling reason for standards is

to ensure that the data will have long term value. It should be acknowledged that the

largest investment in building any database is in the cost of assembling the data and the

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time required to enter them into the system. Further, all computer technology will

change routinely and all systems will need to be upgraded and data moved to different

hardware and software. Standards ensure that the database is consistent internally and

permit data to be formatted and stored for export to other systems.

Standards can vary from strict forms to flexible guidelines developed for specific

institutions. Three standards are commonly defined: technical standards are exacting

(e.g.,

The ASCII character set of 128 codes defines alphabet, numbers, punctuation, and

control codes for text processing and data communication); conventions are more

flexible than technical standards (e.g., the MARC formats and Museum Documentation

Association Data Standard); guidelines are broad sets of practical criteria against which

products are measured (e.g., style manuals).

International and information standards recognized by museums and cultural

heritage organizations constitute four main groups: information system standards define

the functional components of the information systems used; data standards define the

structure, content, and values that collections information comprises; procedural

standards define the documentation procedures required for system management;

Information interchange standards define the technical framework for exchanging

information. (e.g., ISO 8879 or Standard Generalized Markup Language - SGML).

An infrastructure of agreed upon standards will make development of shared text

and image databases much easier, will ensure quality, allow reuse of information, and

create effective transfers of information. National bodies like the International

Organization for Standards (ISO), the American National Standards Institute (ANSI) or

the British Standards Institute (BSI) will publish and maintain standards once these are

approved. Groups like the Getty Information Institute and CIDOC will focus interest and

lobby for standards for particular disciplines and applications.

Selected guidelines for museum records development have been recognized by

CIDOC as part of “International Guidelines for Museum Object Information: The CIDOC

Information Categories” (CIDOC 1995, 2003a, 2003b). ICOM-CIDOC also maintains

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lists of museums and cultural heritage information organiations that concentrate on

standards, and importantly, successful museum documentation initiatives or projects

(ICOM-CIDOC 2002). The list of important international and national projects and

organizations is growing routinely (cf. ICOMOS Documentation Centre 2003).

Australia: Australian Museums and Galleries Online (AMOL) is Australia’s online

cultural heritage resource. AMOL offers details of museums, art galleries and historical

societies across Australia. Over four hundred thousand item records represent thirty-six

cultural heritage collections. Users are offered online museum forums, an online peer

reviewed journal, and consultations in museum resource areas.

Canada:The Bureau of Canadian Archivists’ Planning Committee on Descriptive

Standards has issued a data content standard, “Rules for Archival Description” (RAD)

for Canadian archives. RAD is based on the International Standard for Bibliographic

Description, and archival descriptive records based on RAD can produce records in

MARC format. The Canadian Heritage Information Network (CHIN), a branch of the Arts

and Heritage Sector of Communications Canada, maintains the national inventory of

Canadian collections and offers services to museums, including automated collections

management and advice on documentation standards and new technology. CHIN offers

international access to various specialized databases on natural and cultural heritage

through partnerships like the Conservation Information Network (CIN). CHIN is a

participant in the International Committee for Documentation (CIDOC) of the

International Council of Museums (ICOM).

United Kingdom and Europe: The Archaeology Data Service (ADS), a branch of the

Arts and Humanities Data Service (AHDS), supports research, learning and teaching

through dissemination of high quality digital resources in archaeology, offers technical

advice to the research community, and produces guides for good practice (ADS 2003a,

2003b; AHDS 2003; Bergrie and Greenstein 1998; Burnard and Short 1994; Greenstein

and Trant 1996; Richards 1996; Richards and Robinson 2000; Wissenburg 1997). The

ADS maintains an ARCHSearch Catalog, an ARCHway of journal holdings from twenty-

five UK research libraries, the HEIRPORT or Historical Environment Portal with

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resources from ADS (cf. Baker et al. 2000; Cultural Heritage Consortium 2002; Fernie

2003; HEIRNET 2002), the Royal Commission on the Ancient and Historic Monuments

of Scotland (see Mowat 2002 for an overview of SCRAN), and the Portable Antiquities

Scheme, and the Society of Antiquaries Library Catalog. ADS projects include “Digital

Archiving Pilot Project: Excavation Records”(DAPPER) for English Heritage (1999),

“Archaeological Records of Europe Network Access Project” (ARENA) for the European

Commission, “Online Access to the Index of Archaeological Investigations” (OASIS) for

RSLP and English Heritage, and “Publications and Archives in Teaching with Online

Information Sources” (PATOIS) for JISC. Hunter and Ralston (1997) offer a cogent

summary of archaeological resource management in the United Kingdom.

The International Committee for Documentation of the International Council of

Museums (CIDOC) has over 700 members in sixty-five countries. It supports many

working groups concerned with standards issues (Crofts et al. 2003). The

Archaeological Sites Working Group collaborates with national sites and monuments

organizations and the Council of Europe in developing standards for site documentation

(Council of Europe 1995). The Documentation Working Group compares museum data

standards and is refining a Data Model incorporating practical standards and reviews of

terminology resources. An example project using this standard is the “Network of Art

Research Computer Image Systems in Europe (NARCISSE). Other important working

groups include the Iconography Working Group examining classification schemes for

iconography and the Multimedia Working Group examining standards for application of

multimedia technology. The Museum Documentation Association has produced

SPECTRUM, the UK museum documentation standard (MDA 1997).

The Inventaire General des Monuments et des Richesses Artistiques de la France,

under the French Ministry of Culture, is responsible for recording all cultural property in

two databases: I-ARCHI, which inventories architecture and the built environment, and

I-OBJET, which holds information on movable objects. The Inventaire has developed

terminology authorities for Architecture, Objects Civils Domestiques, Le Mobilier

Domestique, La Sculpture, Tapisserie and Vitrail.

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The Istituto Centrale per il Catalago e la Documentazione (ICCD), part of the Italian

Ministry for Cultural and Environmental Property, maintains a database holding the

patrimony of Italy. The ICCD has produced catalog manuals and terminology

dictionaries outlining data content and data value standards for documenting Italian

heritage collections.

The Royal Commission on the Historical Monuments of England (RCHME) is

responsible for surveys and records on the historic environment of England, and

provides users with advice and information from publications and database records

(RCHME 1993, 1998; RCHME and English Heritage 1995). RCHME has developed the

“National Monuments Record” database, which indexes and correlates information on

architectural and archaeological sites and archives. RCHME has developed and

published information and data standards and guidelines for information levels

recording historic buildings and archaeological sites in England. RCHME also publishes

thesauri of architectural and archaeological terminology, and works closely with the

Council of Europe Division for Cultural Heritage on development and promotion of core

data standards for recording architectural and archaeological information in Europe.

United States: The Getty Information Institute seeks to make cultural heritage

information more accessible through computer networks in collaboration with domestic

and international institutions and organizations. The focus is on policy, standards, and

practice. Initiatives have been developed to define issues of access and distribution of

cultural information in networked environments. The Institute maintains research

databases offering content on effective creation, maintenance and retrieval of

information.

The Museum Computer Network (MCN) is a consortium of museums and individuals

promoting excellence in museum automated information systems. MCN formed the

“Computer Interchange of Museum Information” (CIMI) in 1990 to develop a standards

framework for exchange and sharing of data in museum environments (Moen 1998).

The Research Libraries Group (RLG) maintains a project to develop a “Cultural

Heritage and Museum Object Information Resource,” an initiative to improve access to

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information on works of art, architecture, visual culture, and materials culture held in

museums, historical societies and other cultural heritage institutions. The goal is a web-

accessible database of object records linked to visual images and associated texts.

Data is loaded from the “Reach Project, the Getty’s “Provenance Index,” and the “Vision

Project.” The “Reach Project” is an effort to create a testbed database of museum

object records, where machine-readable data from heterogeneous museum collection

management systems can be exported to analyze research value. The “Vision Project”

for shared visual resource records, is a joint effort of the RLG, the Visual Resources

Association, and the Getty Information Institute. Enhanced searching will use Getty

vocabularies: “The Art & Architecture Thesaurus” and the “Union List of Artist Names.”

The Council on Library and Information Resources, Library of Congress, has published

a national strategy for digital archiving (NDIIP 2002).

The Hammer of Federal and State Regulation: United StatesU.S. archaeological resource managers over the past decade have habitually

referred to a “curation crisis” wherein many museums and repositories cannot accept

new collections because of lack of proper funds and storage space (e.g., Thornbury

2002). The failure of ADAP and its electronic archive is a mirror of collection profiles in

general in the U.S. Even securely stored collections of artifacts and documentation

often do not meet Federal standards. They may not be stored properly, they may be at

immediate risk for deterioration, or they may never have been completely inventoried,

studied, or reported on. From our perspective, these collections cannot be easily

gathered up as orphans and entered into an electronic database. Careful salvage of

these collections is required, object and hard copy documentation needs to be secured

and stabilized, metadata written, and the whole transferred to electronic form. This is

salvage archaeology or data reclamation in its truest form, and the need for action is

paramount.

Legislation pertaining to archaeology in the United States does create a priority in

preserving the archaeological record but interpretation of State and Federal laws and

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regulations does allow considerable ambiguity. The Antiquities Act of 1906 asserts that

all objects must be “properly cared for” but contemporary archaeological research

recognizes the importance of records and metadata as well (Thompson 2000). Other

legislation like the Reservoir Salvage Act of 1960, the National Historic Preservation Act

of 1960, and the National Environmental Policy Act of 1969, installed further mandates

on the collection and care of archaeological objects and collections, though not

necessarily emphasizing maintenance and accessibility of hard copy records. Electronic

archives were certainly not highlighted nor even envisioned. King (2000) admonishes

that this body of legislation prompted a huge volume of archaeological research as part

of large and small scale projects performed to mitigate development activities. At the

same time, legislation and accompanying precedents and protocols failed to provide

effective procedures for protecting the collected artifacts and the growing

documentation.

The Archaeological Data Preservation Act (ADPA) of 1974 approached this problem

by urging concerns regarding protection of these collections. ADPA stated that the

Secretary of the Interior must consult with groups with the goal of determining

ownership of archaeological materials and decisions on deposit of these materials in an

appropriate repository. It also called for the Secretary of the Interior to issue regulations

re: curation of federal archaeological collections (National Park Service 2000). The

following Archaeological Resources Protection Act (ARPA) of 1979 strengthened

procedures requiring permits to conduct fieldwork, asserted federal ownership of

artifacts removed from federal lands, required collections to be stored in “federally

compliant” repositories, emphasized written agreements, and asserted that the

Secretary of the Interior should issue regulations on the care and management of

archaeological collections (Carnett 1991; Cheek 1991; National Park Service 2000).

Even with this emphasis, in 1987 the U.S. Government Accounting Office issued a

report with the following findings: many repositories had no collection inventories,

repositories had lost or destroyed records, collections had never been inspected for

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conservation needs, many had catalog backlogs, and there was insufficient storage

space to match anticipated future needs (USGAO 1987).

The Code of Federal Regulations Title 36 Part 79 (36 CFR 79), “Curation of

Federally-owned and Administrated Archaeological Collections,” 1990, mandated

guidelines for preserving and handling archaeological materials and associated

documentation Code of Federal Regulations 1990). It specifically charged federal

agencies with determining the capabilities of curation facilities. The Society for

American Archaeology responded in 1991 with a “Task Force for Curation,” which

resulted in the document “Urgent Preservation Needs for the Nation’s Archaeological

Collections, Records, and Reports” in 1993 (Childs 1995). These efforts were followed

with formation of an Advisory Committee on Curation who publicly presented the

curation issue in “Crisis in Curation: Problems and Solutions,” presented at the 65 th

Annual Meeting of the Society for American Archaeology (Bustard 2000). The

Committee highlighted continuing problems in curation in the March 2001 issue of the

“Society for American Archaeology Archaeological Record.” Immediate needs were

cited: better integrated field collection strategies, enhanced dependable collections

funding, improved long term care and maintenance of collections, priority in

deaccessioning collections, accreditation of repositories, improved access and use of

collections, and emphasis on public outreach and education through better access to

collections and “grey literature” (Childs 2001).

Complicating institutional improvements in establishing care of collections and

improvements in professional and public access to archaeological data was passage of

the “Native American Graves Protection and Repatriation Act” (NAGPRA) in 1990.

NAGPRA requires comprehensive inventory and repatriation of Native American human

remains and associated funerary and sacred objects and objects of “cultural patrimony.”

This legislation affected all museums receiving federal funding, and specified deadlines

for compliance and inventory assessment as well as penalties for noncompliance

(McManamon 1992). Repatriation issues moved to the fore, and federal and state

agencies, universities, museums and other repositories laid aside many curation and

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conservation issues to immediately deal with this strident political charge. Issues of

ownership and access restrictions served to derail much of the mandate established in

36 CFR 79 (cf. National Park Service 1997a,1997b, 2002; US Army Corps of Engineers

1999, 2003).

Thornbury (2002) summarizes State laws following changes in Federal legislation.

By 1991, fifteen states had passed laws, regulations or policies regarding management

of archaeological collections (Carnett 1991, 1995). By 1999, thirty-seven states cited

curatorial issues as paramount, highlighting required improvements in museum

accessioning and deaccessioning, imposition of curation fee schedules, and explicit

loan policies. Both 36 CFR 79 and NAGPRA have resulted in museums carefully

evaluating whether or not to assume responsibility for new accessions. Museums are

now raising costs and requirements for basic curation. Collections are becoming more

and more expensive to maintain. Unfortunately, dead storage or very limited access has

been a popular solution en lieu of adequate funding. Bustard (2000) cites lack of funds

as the major problem in the United States, noting that 36 CFR 79 provided standards for

curation but did not secure sources of federal funding. Costs have risen in all

categories: staffing, record updating, storage of artifacts, processing of loan requests,

purchase of archival quality boxes and polyethylene bags and acid-free paper, purchase

of computers and software, development of computer databases, and improvements in

physical structures and climatic controls.

Access to CollectionsMuseums in the United States, and elsewhere, are challenged by the prospect of

moving collection information online to provide greater access (Dunn 2000). Moving

information online is a logical reaction to rising costs of curation, display, and onsite

exhibitions. To be made available collections must be placed in management

databases, these databases must include item level metadata for public access, and all

improvements must aim at enhanced “resource discovery.” In a well designed

environment the actual database should be invisible to most users. Research and

development issues are many: collection level descriptions, standard terminology,

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secure but transparent access between organizations, across disciplines, among

resources with different content, and for audiences with varying expertise.

Many museums and organizations have now brought collections online, often

designing and managing their own websites. Many times these individual sites are

linked through principal gateways. These contain item-level descriptions and rich

images within distributed networks as in the Archaeological Data Service framework.

The emphasis is on data sharing among organizations that are interoperable at local,

national and global levels. The ideal is that collection-level descriptions follow a well

designed standard, that these databases are automatically, dynamically created to

match user requirements, and that they strive to be multi-lingual and provide semantic

links between object and class. Collection-level description should provide access for

both general and specific requests, regardless of user knowledge level, discipline, data

requirements, or language of users.

Significant resources are being produced by the Consortium for Interchange of

Museum Information (CIMI), which lists standards for museum resource description and

sponsored the CIMI “Dublin Core Testbed Project” in 1998. In this effort, seventeen

CIMI member organizations created object-level description using the Dublin Core

standard. Primary problems were found in characterizing resources as item-level or

collection-level. A “Guide to Best Practice” for museum collections was produced by

CIMI in 2000. Examination of resource description frameworks have lead to goals to

enable interoperability between applications exchanging metadata, and focused on

enhanced resource discovery, cataloging, and collection-level descriptions. In this vein,

use of Extensible Markup Language (XML) has been documented to offer significant

improvements toward achieving cross-domain interoperability (Dunn 2000; Crescioli,

D’Andrea and Niccolucci 2002; Schloen 2001). There is also a continuing emphasis on

developing sound knowledge representation tools like thesauri for specific disciplines.

Other fine efforts are being produced by the Canadian Heritage Information Network

(CHIN). CHIN is working toward developing terminology in collections-level descriptions

that is specific enough to allow users to decide whether they have found an appropriate

25

resource, but general and descriptive enough so that people from a wide range of

disciplines and knowledge levels can discover the resource. For over twenty-five years,

Canadian museums have been contributing object-level metadata to collective resource

management led by CHIN. “Artefacts Canada” holds information on over two million

objects accessed through “The Great Canadian Guide,” using specialized and general

terms in English and French developed as the “Revised Nomenclature for Museum

Cataloging” (Blackaby, Greeno and the Nomenclature Committee 1988). To avoid

inconsistent nomenclature, CHIN has intergated Getty’s “Art & Architecture Thesaurus”

with “Artefacts Canada.” The AC records are visible to online search engines by

providing CHIN web pages with collections-level descriptions from the “Great Canadian

Guide.” The Guide serves as an online gateway to over twenty-four hundred Canadian

cultural institutions. It allows users to link collections-level descriptions to corresponding

object records in “Artefacts Canada.” The linkages are not automatic and still not always

successfu because of vagaries of class definitions. Controlled vocabularies like

classification rools and thesauri are emphasized. CHIN has also produced “Learning

with Museums,” presenting online educational materials using thesauri of subject areas

based on Canadian school curricula. CHIN is currently developing an initiative on

“Virtual Museums of Canada.”

Preservation of Data: ADS GuidlinesArchaeologists are good are creating data but are not good at arranging and

preserving data in ordered, accessibly public archives (cf. Richards 1997).

Archaeologists also tend not to be very proficient nor interested in reusing other

peoples’ data. The United Kingdom heritage community has been a consistent leader in

attempting to preserve collections and information about the past. UK research in

archaeology over the past thirty years has been very concerned about the preservation

and reuse of archaeological data. This has culminated in the mission of the Archaeology

Data Service, a branch of the Arts and Humanities Data Service (AHDS). AHDS-ADS

emerged from a 1994 feasibility study conducted by an Information Services Sub-

Committee (ISSC) of the Joint Information Systems Committee (JISC) of the UK Higher

26

Education Funding Council (Burrand and Short 1994). This feasibility report

recommended creation of a central coordinating group to carry out management and

user-support functions for maintenance and operation of a comprehensive heritage

database. This organizational structure would support dispersed service providers for

particular disciplines or groups of disciplines.

The AHDS was formally established by the JISC in June 1995, based in King’s

College, London. Over twelve months, five discipline-based service providers were

recognized: Oxford Text Archive (OTA), Oxford University Computer Service; Historical

Data Service (HDS), The Data Archive, Essex University; Performing Arts Data Service

(PADS), University of Glasgow; Visual Arts Data Service (VADS), Surrey Institute of Art

and Design; and the Archaeology Data Service (ADS), University of York. Each of these

service providers were designated responsibility for their discipline’s data and were held

to develop standards definitions and guides for best practice for particular classes of

data. There was an AHDS-wide emphasis on spatial data, including GIS, which was

seen as essential for organizing all discipline’s data sets.

Envisioned was a distributed but integrated approach to data access. Metadata

needed to address discipline specific needs and problems, that economies of scale

required developing shared migration strategies for specific data types, and that a vision

needed to be developed for how researchers would conduct cross-disciplinary searches

from a desktop (text-objects-images). Reuse of data requires that users can locate the

data they require, and that this old data can be accessed in contemporary formats (cf.

Miller 1996; Wise and Miller 1997).

The explicit Archaeology Data Service goal was to collect, describe, catalog,

preserve, and provide user support for reuse of digital data generated in the course of

work by British archaeologists. The focus initially was on Britain but has shifted toward

worldwide application, since there were no stated geographic boundaries, and there

was an expressed interest in working with organizations in other countries to develop

reciprocal archiving policies. Directing development is the dispersed model, with

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archives linked by a single gateway to provide integrated access to distributed

collections.

ADS collections policy operates on two distinct fronts. First, facilitate access to

existing archives and second, accession orphan data sets. Orphan data sets were

common as residue of centuries of work recorded in national and regional Sites and

Monuments Records. Digital archiving on this ambitious scale requires development of

a national strategy. Various pilot studies have been developed in AHDS-ADS to work

with a range of data types to develop “costed models” for preservation of digital data.

These models must distinguish between issues of open public access versus stored

and maintained data that has not been released. Key issues include negotiated access,

copyright restrictions, and developer funding.

The Archaeology Data Service has moved to meet its mission through a strategy of

information sharing embodied in production of its “Guides to Good Practice” series,

which currently include guides to excavation and fieldwork, GIS, geophysical surveys,

and satellite imagery and aerial photography. Other guides will address databases and

sound and video images.

These guides to good practice perforce emphasize creation and preservation of

appropriate metadata, enabling the projected user to locate required data and to assess

data fitness. Metadata ranges from simply noting names of excavation directors to

detailed descriptions of equipment and software used to algorithms run in processing.

The overriding goal is to describe data content at a level sufficient to enable the

potential users to discover data resources. Indexing terms or keywords frame searches,

defining subjects, geographical areas, and chronological ranges. Keywords also help to

assess data quality and methods of recording, as well as reflecting changes in

intellectual and social context reflecting changes in theoretical frameworks. Recognizing

that there is no single set of standards for data description, the AHDS developed and

application of the Dublin Core (Weibel and Miller 1997) to enable searching of the

AHDS catalog through use of the Internet. Discipline-related fields were created through

application of the Warwick Framework (Dempsey and Weibel 1996). The core elements

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were extended through use of Scheme and Type qualifiers, which faciltiated description

through existing standards (e.g., thesauri of object and monument types).

Data standards, in accepted or draft form, are evolving to better handle huge

complex data sets and to effectively link archives around the world, however, Eiteljorg

(1997) highlights the difficulties inherent in managing electronic archives (cf. Wise and

Richards 2001). Collections of objects and hard copy records are often kept reasonably

well long term but electronic data presents serious hurdles. The collections were

created others, using idiosyncratic methods and forms, and the contents are disparate

with files including text, databases, images, CAD files, and GIS files. Eitlejorg notes that

storage is easy compared to making the electronic archive accessible over an extended

period of time. Data migration is a constant, and if accessibility is to be maintained,

electronic files must be routinely upgraded to current software and hardware standards.

Data transformation is problematic, though technically straightforward, because of the

complexity of archaeological data. Migrating an archaeological database, unlike for

instance CAD files, requires intimate knowledge of archaeological method and theory.

The data categories are seldom as obvious as creators think and datum points must be

routinely specified. Documentation is crucial for effective use and migration strategies.

The social contexts of archaeological research, whether in practical realms of

funding and legal agreements or in the shifting ground of accepted method and theory,

are important considerations in the development of electronic archives. Eiteljorg’s

(1997) article was written as he was managing the Archaeological Data Archive Project

(ADAP) at Bryn Mawr University in the United States. From 1997-2003 ADAP was a

developing model for electronic archiving. At the World Archaeology Congress, June

21-26, 2003, in Washington, D.C., Eiteljorg reported that ADAP was dead because

archaeologists would not submit their data to the archive. ADAP was terminated

because it was not economically viable, even though it had initial support through the

Archaeological Institute of America, the American Anthropological Association, and the

Society for American Archaeology. Eiteljorg framed his discussion by asking whether

archival preservation of digital information was a reasonable expectation in the United

29

States. He went on to speculate that a major issue for the demise of ADAP was the lack

of computer sophistication and computer training for U.S. scholars. Another issue it

would seem would be the lack of secure, ongoing funding directed by State and Federal

mandate for preservation of electronic data. Archaeological resource management in

the United States is a very different research context than that described for the United

Kingdom.

Databases: Research and CommunicationDatabases are digital collections. These are the computer equivalent of the

traditional object collections in museums and the records and provenience information

stored in archives. Computer databases, if designed correctly, bring objects and

documents and research records into a single, protected but accessible area where

users can manipulate authenticated data (cf. Ferguson and Murray 1997; Flecker 2002).

Database management systems required the researcher to apply systematic

categories and criteria in consistent ways to research a subject. Consistent categories

allow transparent communication and discussion. Data models are used to guide the

progression from data capture to data analysis to open dissemination. Creation of these

data models require explicit refinement of traditional archaeological theory and method

(e.g., Hadizlacos and Stoumbou 1995). Fields and measurements recorded are

conditioned by the researchers’ interests as these are guided by the larger perspectives

of the discipline. Bader (2000) declares firmly that any database application working

with a two- or three-dimensional visualization tool like GIS enables transparent research

and the promise for open communication. For Bader and other database designers and

users, effective database management is a key to making dramatic strides in

archaeological research and in communication and education (cf. Yang 1986).

Today’s computer-driven research is making significance strides but there is a major

hurdle evident in the day-to-day work of archaeologists, revolving around the computer

sophistication of archaeological producers and users (cf. Andresen and Madsen 1996;

Campana and Crabtree 1987; Desse and Chaix 1986; Harland et al. 2003; Johnson

1997; Lohse 1996; Lohse and Sammons 1998; Powesland, Clemence and Lyall 1998;

30

Rulf 1993; Ryan and van Leusen 2002; Winder 1994). Regional or national database

collections are usually supported by IT teams but primary data is still produced

principally by individual researchers with widely varying approaches, research

questions, and expertise. Most of these archaeologists are not programmers nor data

designers, they simply are using readily available, easy to use relational database

management systems like dBase for Windows, Paradox, or Access. These products are

usable in similar ways and data tables can be moved from one system to another

through dBase files or ASCII delimited files. There are complications in data

transformation and migration but most data producers and users operate above these

concerns.

Data models are the singular requirement in developing any database. A researcher

who wants to enter data must have designated a field. The fields are used to

systematically capture the data. The data captured is directly related to the research

question defined. Some fields are standard expectations, the kind of information all

archaeologists are expected to gather (e.d., site location, description, collector, level,

etc.). Other fields are unique to the methodology employed by the particular researcher

pursuing specific research questions. The field chosen will channel and limit and all

subsequent analysis. As Bader (1997) notes, it is commonplace still for researchers to

neglect defining a data model or identifying appropriate fields before they start gathering

data.

Data Transformation

Data analysis requires checking for errors, often using descriptive statistical

analyses and visualization techniques. These can also be used to transform raw data

into analytical data (cf. Fletcher and Lock 1991). For example, ordinal data can be

transformed into interval data using normalization. Interval data can be placed in fewer

intervals or to presence: absence categories. Factor analysis and cluster analysis are

common approaches to data transformation. Different statistical techniques will also

need transformations to produce different data characteristics. All transformations will

insert varying levels of bias but cannot be avoided, since data must be prepared for

31

subsequent analyses and for use of multivariate statistics like factor and cluster

analysis, Bayesian classification or construction of neural networks.

Commercial database packages are available to run data transformations. DBASE is

still a standard choice combined with various statistical packages like SPSS or

WinState. Even database programming is becoming simpler as with the Delphi

software package that is becoming increasingly popular for computer literate

archaeologists.

Data Comparison

Researchers will inevitably want to run comparisons between their data and that

constructed by others. Too often these comparisons generate debate without reference

to the underlying data models or the use of variable analytical techniques. Use of

shared databases enable archaeological discussions to move beyond opinion and into

the realm of actual data comparison. The prerequisite is definition of the data model and

effective capture of raw data.

Comparing two different relational database management systems will begin with

comparison of their field structures and table structures. The comparable data

categories can then be arranged in a common database model. Next, criteria lists are

developed for each data category, transforming these into a common criteria list with

the least raw data loss possible. Common data tables are then transformable into

analytical data through solutions developed by the cooperating researchers. One result

can be two different analytical data sets as well as a common shared data set. The

discussion again settles on proper filtering and transforming of data. Data table

comparison can be a tedious exercise but development of common platforms or

analytical tools like the Bonn Seriation Package may accelerate easy data comparisons.

Great potential is also presented in use of images in databases, which negates much of

the tedious discussion centering on accepted descriptors or scales of measurement.

Knowledge Representation: Expert System/AI DatabasesDevelopment of optimal knowledge representation is a productive venue for

maximizing data use (Adaptive Intelligent Systems 2003). Davis, Shrobe and Szolovits

32

(1993) describe five very important and distinctive roles for knowledge representation.

KR is a surrogate for the thing itself. It is a set of special commitments. It is a

fragmentary theory of intelligent reasoning in three components: representational

thinking; sets of inferences representing different sanctions; and sets of recommended

inferences. It is a medium for pragmatic efficient computation, guiding how information

is organized to maximize effective inference. It is an actual medium of human

expression, constituting a language for describing the world.

These KR roles provide the framework for data characterizations. This framework

drives realization that data capture must strive to represent maximum richness rather

than reducing complexity to arbitrary, truncated categories lending only sparse,

incomplete and inconsequential description. Richer data captures per force will feed

stronger logical inferences. Enhanced KR frameworks will require elaborated KR

technologies to be developed, including logical rules, frames, and semantic nets.

Representational thinking revolves around a fundamental observation: the intelligent

observor who wishes to make sense of the world needs to acknowledge that reasoning

is an internal process while the things reasoned about exist only externally. Neustupny

(1992) identifies this juxtaposition in describing how archaeologists can define data only

within a theoretical framework or knowledge base that then channels how information

flows are constructed. Researchers don’t find data, they may not even recover data.

They direct data capture based on categories established by their research interest

conditioned by accepted theoretical and methodological positions accepted within their

research field. To improve capture systems, we must accept the KR charge that we

enrich our capture nets to better reflect the complexities of our study domains.

This dichotomy between the researcher’s mind and the world observed is the

fundamental rationale and role for knowledge representation. Reasoning is seen as a

surrogate for external actions. Questions revolve around the identity of the surrogate or

what it is a substitute for and also the fidelity of the surrogate or what it includes and

what it omits. Inevitably, we must acknowledge that the only completely accurate

representation of any object is the object itself. Any representation short of this ideal is

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incomplete and must be explicitly defined and documented. By extension, we can argue

that the more imperfect the surrogate, the more imperfect the inferences drawn. All

surrogates must constitute distortions to some degree. Any description of the

archaeological object is a reflection of our own contemporary thinking, with

representations functioning only as surrogates for abstract notions of action, process,

belief, causality, and categorization. Enriching data capture models insulates our

research against an overriding truthful observation: the soundest reasoning will not

avoid reaching wrong conclusions about our study domain. All representations, logically,

are imperfect, and as such, lead to inevitable error. Good or best representations are

those that minimize errors and good data models are those that emphasize as complete

and representational a description as is theoretically and methodologically possible.

Knowledge representations are sets of ontological commitments. These reflect the

imperfect nature of representations of reality but act to focus attention on selected

features, characteristics or general descriptions that are seen to be useful by a

particular discipline. Different task domains inevitably build different ontologies. These

ontologies are written in different languages and reflect different theoretical and

methodological foundations. The ontological commitments made begin at the level of

the representation technologies and accrue at higher levels. Additional layers of

commitment are added as the technology is applied. This constitutes the application of

the constructed knowledge base. Layers and hierarchies are integral. The ontological

questions are fundamental to the construction and application of this knowledge base.

At each layer the choices made are about representations and not data structures. For

example, a semantic net is a representation but a graph is a data structure. One simply

implements the other. Every representation must be implemented in the computer by a

data structure.

A useful perspective is presented in Minsky’s (1974) description of frame theory. The

intelligent actor confronted with a new situation selects the frame from memory. A

remembered frame is adapted to fit reality by the selective changing of details. The

frame is simply a stereotyped situation that supplied context for resolvable, directed

34

actions. Frames are used by archaeologists as elements of classification, whether

types, stages, phases or narrative conventions. These frames are then defined as fields

in computer analyses, and guide decision making on the categorization or chopping of

data in the capture process.

Designers construct representations that offer a set of ideas about how to organize

information in ways that facilitate the inferences. For instance, in frame theory, any

frame will have attached to it several types of information. Frames are then used to

organize information, mirroring the thinking of the researchers. Stone projectile point

types, produced in the computer system as digital images, serve as frames with linked

information. These same frames or types can then be constructed as taxonomic

hierarchies with accompanying taxonomic reasoning and inference execution.

Obviously, representation and reasoning are inextricably intertwined. Building a

comprehensive knowledge base will require use of accepted representations. However,

the more creative aspect of database building and of data extraction involves designing

systems that will feed innovative and intuitive use of the data. Advances will be made

when new, more efficient representations are defined to reflect application of new

technological, theoretical and methodological developments.

AI in Archaeology

Van den Dries (1998:13-17) offers one of the few summaries of knowledge-based

applications developed for archaeology. He notes that archaeologists have been

interested in developing expert systems since the early 1970s (cf. Doran 1974, 1977;

Doran and Hodson 1975: 309-316; Lagrange and Vitali 1992). Expert systems have

been accepted as amplifying knowledge transmission (e.g., Ennals and Brough 1982)

and for instruction (van den Dries 1998). They have also been developed for improving

theoretical and methodological research (Francfort 1990; Gardin et al. 1988; Lagrange

and Renaud 1985;).

Expert systems in archaeology have been principally a means of formalizing and

modeling knowledge molded by theories and methods. They are used to evaluate

hypotheses, classify artifacts, predict site locations, standardize analytical frameworks,

35

and run simulations on archaeological problem areas. Van Den Dries (1998) extends

their use into teaching and knowledge transmission. It is important to note that experts

may construct such systems to develop and apply expertise, but the non-expert may

benefit in using them for consultation and communication.

Interpretive Context

Science, in itself, constructs mental maps. A goal of science is to explain the natural

world, ranging from simple causality to elaborate chains of explanation. One powerful

approach is reductionist, with its focus on internal structure: laws of motion, law of

gravity, laws of aerodynamics. The result are general principles that interpret complex

phenomena. Reductionism presents a clean cognitive map with meta-features and

carefully defined layers of escalating or descending complexity. Another powerful

approach is contextualist, where the view shifts from internal to external. For example,

what external constraints molded the object under examination. Archaeological

classification can be exceedingly reductionist. We draw up types based on morphology

for instance, or correlate morphological types with chronological layers. This type of

approach negates any explicit tie with prehistoric cognitive maps, and instead, is simply

a handy means of heuristic classification. This is comparable to John Searle’s thought

experiment of the “Chinese Room,” wherein a person seeking to understand Chinese

manipulates huge stacks of paper according to rigid pre-prepared instructions.

Questions in Chinese come in from the outside, pieces of paper get moved around, and

an answer in Chinese will eventually go back out. Yet, we know the person cannot

speak Chinese, so we have to assume that real intelligence cannot be reduced simply

to a set of underlying rules. The experiment rests, of course, on a false analogy. A

proper inference would be that the entire room, rules, paper-pusher, etc., is analogous

to a person who understands Chinese. The person in the room would be analogous to

one nerve cell in a Chinese speaker’s brain. According to Stewart and Cohen (1997:

180), since the room can carry on Chinese conversations flawlessly, the whole system

must be held to “understand” Chinese (analogue for that system or understanding in the

individual). A major flaw in the Searle mind experiment is that the fact that something is

36

possible in principle, is much less informative than the fact that it is impossible in

practice (Dennett 1991). For example, the instructions on paper would have to contain

contingency plans for every possible Chinese question. They cannot simply be a huge

catalog of questions and answers, no room would be large enough and no scheme

could be far reaching enough. The rules for moving the paper would have to be devised

by a fully fluent master of Chinese.

The “Chinese Room” metaphor warns us that logically developed classification

systems, databases, and retrieval systems can establish types of objects and create

solid statistical bases for discrimination but these do not inform us about their prehistoric

makers. As analysts, we must attempt to place artifacts into their contexts of design,

manufacture, use and re-use. The artifact forms should be viewed as constituting

mental templates that have embedded meaning related to cultural mores, standards,

economic decisions, and adaptive strategies.

Knowledge Elicitation

Creation of any knowledge-based application requires a trajectory of knowledge

elicitation, analysis, modeling, and application design. The first and critical step is

extraction of expert knowledge (cf. Burge 2003; Kidd 1987; Ford and Sterman 1997).

The designer must initiate a full analysis of the problem domain and of the knowledge

base required to develop an expert system that can perform a specific task successfully.

Once the knowledge base has been constructed, a model can be made for subsequent

design.

Designers have found that experts release an idealized view of their work (Payne

and McArthur 1990). They implicitly emphasize ideal and clear situations and overlook

problem scenarios. This lends a biased view, not fully representative of real world

situations.

Van den Dries (1998:44-51) reviews difficulties encountered in eliciting expert

knowledge for construction of WAVES-WARP, an expert system for lithic use-wear

analysis. van den Dries reports that information was unbalanced and often insufficiently

detailed, consisting predominantly of data on wear patterns that were considered

37

diagnostic and under representing deviations from these ideal patterns. This tendency

was particularly pronounced if working from published sources rather than in interviews

with experts. There were also gaps in different expert’s knowledge and inevitable

problems of cross-referencing different approaches and decision-making structures.

Experts’ transmission of knowledge is also complicated by formal training (facts and

theories) and their own subjected rules-of-thumb gained by experience.

The knowledge engineer (extractor) needs to construct systematic information in the

format: “If you observe wear attributes A, B and C then this is an indication with

certainty X that this tool was used in activity D or E, and not in activity F, because ....”

As van den Dries (1998:45) concludes, lack of complete logical constructions from

experts is due to practical factors: these are typically not required in reports of results

and because use-wear types or categories are not clear-cut, frequently grading one into

the other. This means that for most analysts, interpretation does not always occur within

the absolute structure of formal rules but as the result of complex sets of associations.

Knowledge Handling

Once acquisition of knowledge has been accomplished, and summarized in a formal

model, a conceptual map is made of what the application is going to look like, how tasks

will be carried out, how knowledge will be represented, the nature of inference

mechanisms, and what other aspects of transmission will be built in. Different types of

programming can be used, including linear programming, rapid prototyping, and

incremental programming. In linear programming, the application is implemented and

evaluated as a complete product. A limitation is that users are not integral to the full

development process and late discovery may reveal significant problems requiring fixes.

Prototyping necessitates longer development but systematically and efficiently utilizes

user input and patterned fixes (cf. Bratko 1989; Hayes-Roth et al. 1983; Kahn and

Brauer 1989; Whipp and Lewis 1989). Both programming approaches are combined in

incremental programming, wherein the system is divided into smaller parts, tasks or

modules and each of these is implemented in the linear method and evaluated through

prototyping. This has advantages: design can be quickly and efficiently updated, users

38

are involved constantly, and the resulting prototype has been significantly debugged (cf.

Hollnagel 1989).

Directionality, key to productive analysis, is keyed to problem solving within

patterned cultural perceptions. Large complex problems will routinely be broken down

into smaller less complex problems more amenable to resolution through use of

practiced activity chains or programmed responses. Segal (1994:25-26) asserts that

any analysis of problem solving has four components: identify the problem space as

that range between the initial and goal states (Newell and Simon 1972); identify

intermediate states between the initial and goal states (only trivial problems will allow

direct movement from the initial state to the goal state); identify what needs to be done

(movements as transformations); identify the resources (knowledge, skills, material,

time) needed to execute each move. Directionality is achieved/defined by tracking

moves from stage to stage.

Effective knowledge handling is measured as representational adequacy,

correctness, modularity and simplicity. Assessment of the user interfaces attached

involve measures of graphical possibilities, user friendliness and explanatory facilities.

All design decisions will be measured for flexibility, transparency and ease of

maintenance. In general, all knowledge handling systems should strive to be fully

representative of the extant knowledge base, and they should allow easy maintenance

of the constructed knowledge base. A modular approach is often the best solution,

dividing knowledge into orderly, coherent chunks. The structure must be transparent

with detailed data descriptions to ensure coherent use and ready upgradability.

Overriding all design is that the system be made as simple as possible. That the system

adhere to correctness is inescapable. It must exhibit all required reasoning facilities to

initiate and complete routine tasks.

WAVES and WARP

Van Gijn and Fullgar (1998:203-207) welcome van den Dries’ work on WAVES in an

addendum to his ground-breaking monograph (van den Dries 1998). They admonish

that the primary thing missing in lithic use-wear analysis has been a definitive key for

39

assessing stone tool function accepted by all analysts. In lieu of this key, analysts have

developed different systems for recording and interpreting microscopic observations.

Variable importance is then attached to different characteristics: scarring, striations,

polish, beveling and rounding of surfaces. Analysts also enter myriad descriptive

attributes, ranging from generally accepted standard terms to quite ideosynchratic

constructions: melting snowfield and comet tails. Van den Dries offers expert systems

as a means of formalizing and structuring the data capture and transmission process.

WAVES (Wear Analysing and Visualising Expert System) was developed to analyze

use-wear traces on flint implements. WARP is an associated prototype based in neural

networks. Van den Dries (1998) summarizes testing of both prototypes, assessing their

relative qualities under comparable constraints. WAVES was tested twice, with

evaluation by four analysts, with outcomes compared to other “blind tests.”

WAVES models expert knowledge development. The aim was two-fold: to

successfully standardize the knowledge acquisition and application process and to

refine the methodology of use-wear analysis. van den Dries (1998:43) reports that

collecting and analyzing expert knowledge revealed that training of apprentices involved

two levels: basic and advanced. Basic focuses on teaching methodological principles

and guiding analysis. Advanced students reach autonomous interpretations and are

more interested in verification. Hypothesis verification was seen to be a principle

concern. van den Dries assumed that the WAVES application should be divided into two

independent components: analysis and hypothesis validation. Of course, boundaries

between the two stages are often fuzzy. In each phase of analysis the work from the

previous phase is modified and refined and all ensuing steps condition decision-making

on the part of the analyst.

WAVES is a rule-driven expert system, depending upon IF-THEN decision rules

rather than on propositional logic, semantic nets or frames. van den Dries (1998:52)

asserts that rule-driven decision making matches the conduct of lithic use-wear

research best, and that it lent to simplicity and modularity of system design. Correctness

was insured by confining WAVES to connecting a wear pattern to a specific material or

40

motion is the observed features matched the motion exactly. Non-matching features,

not seen previously, are excluded.

WAVES user interface was designed to include graphics and be highly interactive

with attractive, intuitive screens. Built in, are facilities to guide information input and

provide additional user support. Information on internal procedures was also made

transparent. On request, a user is informed how and why certain facilities had to be

incorporated, why a conclusion was drawn and why not. Rules active at a particular

stage of the reasoning process are shown, and those activated next are indicated.

Large collections of use-wear photos are included to support description of traces and

to support interpretations resulting from the analytical procedures.

WAVES seems to make significant strides over Grace’s (1989) pioneering expert

system in lithic use-wear analysis, FAST (Functional Analysis of Stone Tools). FAST

like WAVES was designed to facilitate training students. The two are very different in

basic design. FAST was developed for functional analysis of entire stone tools. WAVES

was built to focus on interpretation of use-wear traces. van den Dries (1998:77) asserts

that because WAVES does not focus on gross morphology, it is more applicable to

analysis of a wider range of tool uses in different materials and across cultural periods.

Further, FAST produces an interpretation as one final answer, where WAVES

introduces all appropriate complexity to the student user, and includes an analytical and

a hypothesis validating procedure.

Van den Dries (1998) included development of a neural network prototype called

WARP. The neural network can handle more complex bits of data, involving unknowns.

Neural networks are increasingly used in industrial, medical and other fields involving

many practical and time-consuming redundant tasks like classification, pattern

recognition and prediction. Neural nets effectively learn, by means of mathematical

models that predict outcomes of complex situations by generalizing from similar,

previous situations. The neural agent must be trained by entering lots and lots of

information and running autonomous searchers for non-linear relationships between

variables. It learns to select important variables and disregard others. Neural networks

41

have a very different architecture from expert systems, use specific knowledge storing

and processing methods and are applicable to different study domains (cf. Gibson 1992:

265; 1996). Van den Dries’ (1998) WARP was developed to investigate the possibilities

and difficulties of formalizing and modeling expert knowledge involved in use-wear

analyses by means of a neural network. The findings were compared with those

generated from development of WAVES. It was found that WARP was easier to handle

and to understand. Users found interfaces highly intuitive and the necessary user

learning curves were much less than for the expert system WAVES. Van den Dries

(1998:90-91) also concluded that WARP produced comparable analytical results with

the added advantage of dealing with previously unclassified or unknown features.

These could be used to guide future research. A profound difficulty in using the neural

network centered on the neural agent having difficulty being trained in introduction of

complex datasets. Too many contradictory facts led to many stumbling points in

successful training. A lack of validation for answers and the autonomous character of

the neural agent were also phrased as possible drawbacks.

SIGGI-AACS

SIGGI-AACS represents a significant step beyond WARP-WAVES in use of a neural

network to develop both an automated classification system and an authoritative online

database (Lohse et al. 2003). Past experiments with AI systems have largely been

confined to rule-based forced classifications as in that developed by van den Dries

(1998) for teaching use-wear analysts. Use of a sophisticated neural network that is

trainable and capable of making novel intelligent decisions is an important approach to

improving information sharing and exploring theoretical tenets of archaeological

classification and data design.

SIGGI-AACS is a working prototype for online classification of stone projectile

points in a neural network. The initial application uses specimens drawn from the North

American Pacific Northwest cultural area but the system is extensible. The current

database design is not software specific and was initially done in Microsoft Access. The

autoclassification system consists of three interrelated products. Product 1 is the

42

classification system, with software that allows users to submit images of artifacts or

actual specimens to be digitized. This stage generates projectile point classifications

with specimens assigned to recognized types and is a .NET standalone application.

Product 2 consists of release of a typological descriptive report to system users,

including a full image inventory of submitted and classified specimens with attached

statistical probabilities of type assignment. Product 3 is a web-based educational venue

for public access and study. SIGGI functions as a virtual analyst, which given some

basic rules and concepts, is trained by introduction of new data sets. To improve its

accuracy, SIGGI must be continually exposed to new and amplifying data fields. SIGGI

is capable of accurately applying extant projectile point typologies; however, SIGGI can

also identify outliers or unique data sets and suggest that these represent new types or

that previous analysis identifying types needs modification within new explicit data

ranges. As with any student, we must be certain that the data we ask SIGGI to analyze

has been authenticated, and that we gather samples that are clearly representative of

defined research populations. Because SIGGI learns by mimicking expert’s decisions,

behaviors, and explicit rules, and then creates new decision frameworks integral to the

compilation of new data, SIGGI eventually may generate insights into decisions made

by human analysts and by prehistoric makers.

The principal barrier to training SIGGI is to retrieve collections that have fine

excavation and analytical context. A primary assumption in archaeological typologies is

that the knappers of the stone points were operating within a very well defined cultural

model that laid out clear expectations regarding what a particular projectile point form

should look like. This working assumption is borne out in the clear temporal and spatial

separation of populations of projectile points (e.g., Lohse 1985). Essential for training

this virtual analyst are retrieval of sample populations that as nearly as possible

represent these real time actors in the past. For example, for training SIGGI needs

projectile point samples that were found in large numbers on a single site, within a

specific layer, in association with cultural features representing clear prehistoric human

activity, and bracketed by good reliable radiocarbon dates. These samples supply the

43

virtual analyst with numerous points made to a prehistoric standard, and reveal

expected ranges of statistical variation in basic variables of form. This allows SIGGI to

make intelligent decisions on where to draw lines demarcating the distinctive types of

projectile points. SIGGI’s ability to explicitly handle multiple variables in a

multidimensional statistical environment promises insights into clarification and

refinement of chronologies of prehistoric projectile point types, a result of considerable

interest to the practicing archaeologist (cf. Lohse et al. 2003).

The current SIGGI-AACS Project has focused on obtaining authenticated data in an

effort to produce a “clean” set of data that reproduces exactly the classification

published by Lohse (1985). Data collected are stored in an image database with

attached descriptive fields. SIGGI is in a sense, Lohse’s virtual brain. Since SIGGI

represents the thinking of only one archaeologist, future work intends to extract

knowledge from other archaeological typologists. SIGGI can “think” like Lohse, who

summarized previous typological work, but the project is expanding and will have SIGGI

interact with other researchers’ ideas, i.e. bring SIGGI an education from the larger

community. This reflective activity is one of the more important aspects of the project.

Obviously, certain kinds of things can be classified in proscribed ways, but project

research focuses on identifying WHY things should be classified in certain ways. By

watching SIGGI make classifications, these researchers hope to gain a better

understanding of why archaeologists make classifications and how these might be

continually improved as research methodology improves.

The accuracy of classification provided by SIGGI depends on a large authenticated

knowledge base. The SIGGI database performs several functions that influence both

the performance of SIGGI and the sharing of data among researchers. At a minimum,

SIGGI-AACS is required to (1) store images; (2) store locations, (3) store

characteristics, and (4) keep information secure.

SIGGI also allows for improved research collaboration. Using the underlying SIGGI-

AACS research database, many researchers can maintain information about many

collections on-line. Each researcher is able to maintain complete ownership of access to

44

his own material. This allows research bridges to be built while maintaining the unique

identity of each research collection. When fully implemented, the system can maintain

collections of many types and provide neural network analytic services where

appropriate.

A crucial aspect of the SIGGI-AACS project is how users will access and input

information on the Internet. Four major user groups have been identified: government

agencies, researchers, Native Americans, and the general public. Tasks have also been

identified that will be requested by each of these user groups.

A primary user of SIGGI-AACS should be federal and state land-management

agencies (e.g., USDA Forest Service, USDI Bureau of Land Management, National

Park Service). Agency users will be interested in the automated typology made possible

by SIGGI’s neural network. Three major tasks include analysis of point images

submitted, data storage, and summative transfers of information.

For Task 1, the web site contains appropriate interfaces to allow the agency (or a

SIGGI-AACS staff member) to enter an image of the projectile point for automatic type

assignment. This site allows the user to progress through the different stages of image

acquisition, manipulation, and analysis. At each point in this image manipulation

process, the user must be able to check the image and authorize its movement to the

next stage. Once the image has been successfully entered, SIGGI will analyze it and

return the appropriate typological assignment. At the same time (and out of the user’s

view), SIGGI will also encode the relevant datapoints (size and shape indicators), and

file the datapoints, image, and typological, locational, and temporal information into the

SIGGI-AACS database. In Task 2, the agency user is able to access information which

is already in the database and for which that agency has statutory responsibility. In this

case, the web site provides an interface by which the government agency can request

images, specimen or inventory numbers, counts, chronologies, charts, graphs, or maps

displaying information about specific projectile points. In Task 3, the web site provides

interfaces for the agency to request similar, but summative, information for projectile

points not within their statutory responsibility.

45

Researchers are interested in the automatic typology made possible by SIGGI’s

neural network, but also need to access information from the database for specific

projectile points, including images but perhaps excluding locational data. For Tasks 1-3,

the web interface is the same as that set for government agencies. For Task 4, the

interface must also allow the user to request information about specific projectile

point(s). For example, a researcher may request images of points from the type site or

of a recent find. Providing this type of specific information to archaeologists advances

the resolution of study, adding chronology, spatial distribution, functional studies, or

symbolic studies. However, locational information would be excepted: academic or

contract archaeologists cannot be given exact locations for artifacts without permission

from the federal, state, or tribal landowner.

The third profiled user group are Native American tribes and First Nations in the

United States and Canada. SIGGI-AACS, with its chronological and spatial data, may

have profound implications regarding cultural affinity issues, and tribes may be

interested in this information for both heritage and legal reasons. Tribes may be able to

use the data stored in SIGGI-AACS to argue for traditional use of land or rivers not

currently within the tribes’ legally recognized authority. In addition, tribes and first

nations may wish to use the projectile point database as a mechanism for storing

cultural heritage information and to provide that information in educational contexts

(tribal museums, schools, etc). Security will be a major issue, and United States federal

law requires that locations of archaeological sites not be made available to the general

public. A fifth tasks is added for these users: the imposition of secure access controls.

The fourth major user group for SIGG-AACS are members of the public. These

users will ask such questions as, “I found a point. What type is it?” “Where are these

points found?” and “How old is it?” They may also be interested in images of projectiles

in the database, perhaps to compare with a point in their own possession or to copy into

a school report. The materials provided to these users will be the result of scripted

actions and not the result of active database searchers.

Implications for Future Developments

46

WAVES-WARP and SIGGI-AACS projects, while still in the prototype phase, provide

innumerable examples illustrating fundamentals of database design, user interface

design, and relational database design. Both operate on multiple levels, from

development of an explicit statistically based online classification system with attached

database, to use of an electronic or virtual agent to augment archaeological training in

classification, to observation of an artificial agent to study the character and

effectiveness of archaeological thinking. Anthropologists and archaeologists are

beginning to join cognitive psychologists and learning theorists in the use of artificial

intelligence systems to explore human thought and behavior (e.g., Baylor 2002; Conte

and Castelfranchi 1995; Cumming 1998; Doran various; Epstein and Axtell 1996;

Gonzalez and DesJardins 2002; Russell and Norvig 1995; Woolridge and Jennings

1998; Woolridge, Muller and Tambe 1996).

Although others have used neural networks in archaeology, WAVES-WARP and

SIGGI-AACS are partially successful prototypes in archaeological information

technology. Key now is to expand on these prototypes and authenticate their potential

(cf. Stead 2003). Obvious productive spinoffs from this research include: (1) training of

an online neural classification system capable of accurately identifying archaeological

artifacts (SIGGI in this sense constitutes a highly interactive user interface sitting atop a

secure database); (2) creation of new theoretical and methodological frameworks to

accelerate effective information design; (WAVES and SIGGI offer advantages in

teaching and insights into how we conceive of our study domains); (3) further

development of artificial intelligence systems linked to giant heritage databases that are

constantly maintained and revised to ensure secure storage, organization and transfer

of our archaeological heritage.

Construction of large databases supervised by intelligent agents is a completely

attainable, realistic projection not just for archaeology but for all information heavy, data

rich disciplines (e.g., Egenhofer 2002; Farenc et al. 2000; Thalmann, Farenc and Boulic

1999; The Semantic Web 2003; Tsukii, Kihara and Ugawa 2001). This is a major break

from past practice in archaeology: where laborious searches in libraries and archives

47

for hard-to-find publications and “gray literature” was the norm; where tedious and time-

consuming requests are made to overworked archive and collections managers to

hand-relate various hard copy finder’s guides in order to find and pull specimens from

cabinet drawers and storage boxes (cf. Huggett 1995; Lock 1995; Lock and Brown

2000; Madsen 2001; Pinto et al. 2001; Stewart 1996). The vision that information can be

accessed through a central portal and seamlessly indexed and sorted dependent upon

researcher interest and creative motivation constitutes a paradigm shift in

archaeological information management very like that declared for e-publishing by

Morton (1997), fueled by a shared ideology steeped in IT (cf. Ginsparg 1996; Odlyzko

1996). An affirmation of technology has taken place and is driving significant changes in

the infrastructure of scientific research (cf. Dreyfus 2001; Dreyfus 2002; Dreyfus and

Dreyfus 2002; Hodder 1999; Neustupny 1992). Use of the Internet for delivery of

scientific information not only speeds access but forces changes in the social

organization of scholarship and the authentication of information (cf. Fulda 2000; Gray

and Walford 1999; Holmen et al. 2003; Lamprell et al. 1995; Miller, Dawson and Perkins

2002; van Leusen et al. 1996). New kinds of interfaces will be developed that sit atop

these huge heritage databases, ensuring that users have virtually seamless interaction

with data, whether through virtual documents or virtual agents (Gruber, Vemuri and Rice

2003; Miller 2000; Ryan 1995). GIS is still the backbone for many archaeological data

applications, and innovative uses of maps to transmit information is yet another form of

enhanced interface produced in Ian Johnson’s impressive TimeMap Project (e.g.,

Johnson 1999, 2002a, 2002b, 2003 ( see also Lancaster and Bodenhammer 2002 on

the Electronic Cultural Atlas Initiative). Virtual Reality applications also have tremendous

potential in transferring information as 3-D data (e.g., Pringle and Moulding 1997;

Razdan et al. 2001, 2002).

We are not at our vision yet. Technology, in the form of hardware and software is

available, and we can do things today in manipulating huge data sets, that were

unthinkable a decade ago. Computers are increasing in power and our students have

an underpinning in IT that readies them to join our vision for the future. The principle

48

problem lies in social informatics, in understanding how our discipline has assembled

data and conveyed information. Standards are in place that gauge scholarly contribution

and data integrity, and some of these will have to fall away to accept the shared vision

of IT applications across the board.

49

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