community. data acquisition and usage value chain

COMMUNITY

Data Acquisition and Usage Value Chain

Data Processing Value Chain

Monitor StoreData 1

Monitor StoreData 2

Monitor StoreData n

Monitor StoreData m

IntData1

IntDatan

IntData2 Virtual Int. Data

Information Processing Value Chain (Taylor, 1975)

Informing Knowledge

ActionProductive Knowledge

InformationData

OrganizingGrouping

Classifying Formatting Displaying

Analyzing

SeparatingEvaluating Interpreting

Synthesizing

Judging Options Quality

Advantages Disadvantages

Deciding Matching goals, Compromising

Bargaining Deciding

• Forces to Move Data• one-shot to reusable form

• External force – contracts

• Internal – humanitarian, benefits

Resistances to Move Data • Mechanical

• Personal

• Institutional

Assigning maintenance responsibility (Wiederhold)

a. Source data quality – supplier database, files, or web pages

b. Interface to the source – wrapper, supplier or vendor for supplier

c. Source selection – expert specialist in mediator

d. Source quality assess. – customer input to mediator

e. Semantic interoperation – specialist group input to the mediator

f. Consistency & metadata – mediator service operation or warehouse

g. Informal, integration – client services with customer input

h. User presentation – client services with customer input

Services

Sources

Customers

Standard Data Support System

• Data management systems, DBMS

• Data processing end exploration tools

• Presentation tools

PM/Haze Data Flow in Support of AQ Management

• There are numerous organizations in need of data relevant to PM/Haze

• Most interested parties (stakeholders) are both producers and consumers of PM and haze data

• There is a general willingness to share data but there are many physical and organizational resistances to data flow and processing

RPO

RPO

RPO

Regional Planning Orgs

FLM

FLM

FLM

Federal Land Managers

EPA

EPA

EPA

EPA Regul. & Research

Industry

AcademicNARSTO

Other: Private, Academic

SuperSite

Shared PM/Haze

Data

• PM and haze data are used for may parts of AQ management, mostly in form of Reports

• The variety of pertinent (ambient, emission) data come from many different sources

• To produce relevant reports, the data need to be ‘processed’ (integrated, filtered aggregated)

PM/Haze Data Flow in Support of AQ Management

• PM and haze data are used for may parts of AQ management, mostly in form of Reports

• The variety of pertinent (ambient, emission) data come from many different sources

• To produce relevant reports, the data need to be ‘processed’ (integrated, filtered aggregated)

Data from multiple measurements are shared by their providers or custodians

Data are integrated, filtered, aggregated and fused in the process of analysis

Reports use processed data for Status and Trends; Exposure Assessment; Compliance

•

Data Re-Use and Synergy

• Data producers maintain their own workspace and resources (data, reports, comments).

• Part of the resources are shared by creating a common virtual resources.

• Web-based integration of the resources can be across several dimensions:Spatial scale: Local – global data sharing

Data content: Combination of data generated internally and externally

• The main benefits of sharing are data re-use, data complementing and synergy.

• The goal of the system is to have the benefits of sharing outweigh the costs.

Content

Content

User

User

User

LocalLocal

GlobalGlobal

Virtual Shared Resources

Data, KnowledgeTools, Methods

User

User

Shared part of resources

Integration for Global-Local Activities

Global Activity Local Benefit

Global data, tools Improved local productivity

Global data analysis Spatial context; initial analysis

Analysis guidance Standardized analysis, reporting

Local Activity Global Benefit

Local data, tools Improved global productivity

Local data analysis Elucidate, expand initial analysis

Identify relevant issues Responsive, relevant global analysis

Global and local activities are both needed – e.g. ‘think global, act local’

‘Global’ and ‘Local’ here refers to relative, not absolute spatial scale

Content Integration for Multiple Uses (Reports)

Data from multiple measurements are shared by their providers or custodiansData are integrated, filtered, aggregated and fused in the process of analysisReports use processed data for Status and Trends; Exposure Assessment; Compliance

The creation of the needed reports requires data sharing and integration from multiple sources.

DATAFED Rationale

• As much as possible, data should reside in their respective home environment. ‘Uprooted’ data in centralized databases need updated and maintained.

• Data Providers would need to ‘open up’ their SQL data servers for limited data subsets and queries, in accordance with a ‘contract’. However, the data structures of the Providers will not need to be changed.

• Retrieval of uniform data from the data warehouse facilitates integration and comparison along the key dimensions (space, time, parameter, method)

• The open architecture of DATAFED and the use of web-standards promotes the building tools by and for the community: Data Viewers, Data Transformers and Integrators, Report Generators, Renderers etc..