AnHai DoanUniversity of Illinois

Joint work with Pedro DeRose, Robert McCann, Yoonkyong Lee, Mayssam Sayyadian, Warren Shen, Wensheng Wu, Quoc Le, Hoa Nguyen, Long Vu, Robin Dhamankar, Alex Kramnik, Luis Gravano, Weiyi Meng, Raghu Ramakrishnan, Dan Roth, Arnon Rosenthal, Clemen Yu

From Data Integration to From Data Integration to Community Information ManagementCommunity Information Management

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New researcher

Find houses with 4 bedrooms priced under

300K

homes.comrealestate.com homeseekers.com

Data Integration ChallengeData Integration Challenge

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Actually Bought a House in 2004Actually Bought a House in 2004

Buying period– queried 7-8 data sources over 3 weeks– some of the sources are local, not “indexed” by national sources– 3 hours / night 60+ hours– huge amount of time on querying, post processing

Buyer-remorse period– repeated the above for another 3 weeks!

We really need to automate data integration ...

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Architecture of Data Integration SystemsArchitecture of Data Integration Systems

mediated schema

houses.comhomes.com

source schema 2

realestate.com

source schema 3source schema 1

Find houses with 4 bedroomspriced under 300K

wrapper wrapperwrapper

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Current State of Affairs Current State of Affairs

Vibrant research & industrial landscape Research since the 70s, accelerated in past decade

– database, AI, Web, KDD, Semantic Web communities– 14+ workshops in past 3 years: ISWC-03, IJCAI-03, VLDB-04, SIGMOD-04,

DILS-04, IQIS-04, ISWC-04, WebDB-05, ICDE-05, DILS-05, IQIS-05, IIWeb-06, etc.

– main database focuses: – modeling, architecture, query processing, schema/tuple matching– building specialized systems: life sciences, Deep Web, etc.

Industry– 53 startups in 2002 [Wiederhold-02]

– many new ones in 2005

Despite much R&D activities, however …

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DI Systems are Still Very DifficultDI Systems are Still Very Difficultto Build and Maintainto Build and Maintain

Builder must execute multiple tasks

Most tasks are extremely labor intensive Total cost often at 35% of IT budget [Knoblock et. al. 02]

– systems often take months or years to develop

High cost severely limits deployment of DI systems

select data sources

create wrappers

create mediated schemas

match schemas

eliminate duplicate tuples

monitor changes

etc.

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Data Integration @ Illinois Data Integration @ Illinois

Directions:– automate tasks to minimize human labor – leverage users to spread out the cost– simplify tasks so that they can be done quickly

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price agent-name address

Sample Research on Automating Sample Research on Automating Integration Tasks: Schema MatchingIntegration Tasks: Schema Matching

1-1 match complex match

homes.com listed-price contact-name city state

Mediated-schema

320K Jane Brown Seattle WA240K Mike Smith Miami FL

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Schema Matching is Ubiquitous!Schema Matching is Ubiquitous! Fundamental problem in numerous applications Databases

– data integration, – model management– data translation, collaborative data sharing– keyword querying, schema/view integration– data warehousing, peer data management, …

AI– knowledge bases, ontology merging, information gathering agents, ...

Web– e-commerce, Deep Web, Semantic Web, Google Base, next version of

My Web 2.0?

eGovernment, bio-informatics, e-sciences

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Why Schema Matching is DifficultWhy Schema Matching is Difficult

Schema & data never fully capture semantics!– not adequately documented

Must rely on clues in schema & data – using names, structures, types, data values, etc.

Such clues can be unreliable– same names different entities: area location or square-feet– different names same entity: area & address location

Intended semantics can be subjective– house-style = house-description?

Cannot be fully automated, needs user feedback

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Current State of AffairsCurrent State of Affairs Schema matching is now a key bottleneck!

– largely done by hand, labor intensive & error prone– data integration at GTE [Li&Clifton, 2000]

– 40 databases, 27000 elements, estimated time: 12 years

Numerous matching techniques have been developed– Databases: IBM Almaden, Wisconsin, Microsoft Research, Purdue,

BYU, George Mason, Leipzig, NCSU, Illinois, Washington, ... – AI: Stanford, Toronto, Rutgers, Karlsruhe University, NEC, USC, …

"everyone and his brother is doing ontology mapping"

Techniques are often synergistic, leading to multi-component matching architectures– each component employs a particular technique– final predictions combine those of the components

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Example: LSD Example: LSD [Doan et al. SIGMOD-01][Doan et al. SIGMOD-01]

Mediated schema

Urbana, IL James Smith Seattle, WA Mike Doan

address agent-name

area contact-agent

Peoria, IL (206) 634 9435 Kent, WA (617) 335 4243

homes.com

Name Matcher

Naive BayesMatcher

Combiner

0.3

agent

name

contact

agent0.5

0.1

area => (address, 0.7), (description, 0.3)contact-agent => (agent-phone, 0.7), (agent-name, 0.3)

comments => (address, 0.6), (desc, 0.4)

Match Selector

ConstraintEnforcer

Only one attribute of source schema matches address

area = address

contact-agent = agent-phone

...

comments = desc

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Multi-Component Matching SolutionsMulti-Component Matching Solutions

Such systems are very powerful ...– maximize accuracy; highly customizable

... but place a serious tuning burden on domain users

Constraintenforcer

Match selector

Matcher

Matcher Combiner

… Matcher 1 Matcher n

Constraintenforcer

Match selector

Combiner

Matcher 1 Matcher n…

Constraintenforcer

Match selector

Combiner

Matcher 1 Matcher n…

Match selector

Combiner

LSD COMA SF LSD-SF

Introduced in [Doan et. al., WebDB-00, SIGMOD-01, Do&Rahm, VLDB-02, Embley et. al. 02]

Now commonly adopted, with industrial-strength systems– e.g., Protoplasm [MSR], COMA++ [Univ of Lepzig]

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Tuning Schema Matching SystemsTuning Schema Matching Systems

Library of matching components

Constraintenforcer

Match selector

Combiner

Matcher 1 Matcher n…

Execution graph

Knobs of decision tree matcher

Threshold selector

Bipartite graph selector

A* search enforcer Relax. labeler ILP

Average combiner

Min combiner

Max combiner

Weightedsum combiner

q-gram name matcher

Decision treematcher

Naïve Baysmatcher

TF/IDF name matcher

SVMmatcher

• Characteristics of attr.

• Post-prune?• Size of validation set

• Split measure

•••

Given a particular matching situation– how to select the right components? – how to adjust the multitude of knobs?

Untuned versions produce inferior accuracy

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Large number of knobs– e.g., 8-29 in our experiments

Wide variety of techniques – database, machine learning, IR, information theory, etc.

Complex interaction among components Not clear how to compare quality of knob configs

Long-standing problem since the 80s, getting much worse with multiple-component systems

But Tuning is Extremely Difficult But Tuning is Extremely Difficult

Developing efficient tuning techniques is now crucial

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The eTuner Solution The eTuner Solution [VLDB-05a][VLDB-05a] Given schema S & matching system M

– tunes M to maximize average accuracy of matching S with future schemas

– commonly occur in data integration, warehousing, supply chain

Challenge 1: Evaluation– score each knob config K of matching system M– return K*, the one with highest score– but how to score knob config K?

– if we know a representative workload W = {(S,T1), ..., (S,Tn)},and correct matches between S and T1, …, Tn can use W to score K

Challenge 2: Huge or infinite search space

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Solving Challenge 1: Solving Challenge 1: Generate Synthetic Input/Output Generate Synthetic Input/Output

Need workload W = {(S,T1), (S,T2), …, (S,Tn)}

To generate W– start with S– perturb S to generate T1– perturb S to generate T2– etc.

Know the perturbation know matches between S & Ti

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emp-last id wage

Laup 1 45200

Brown 2 59328

Emps

Perturb # of columns

Perturb table and column names

Perturb data tuples

id = idfirst = NONElast = emp-lastsalary = wage

id first last salary ($)

1 Bill Laup 40,000 $

2 Mike Brown 60,000 $

Employees

last id salary ($)

Laup 1 40,000 $

Brown 2 60,000 $

Employees emp-last id wage

Laup 1 40,000$

Brown 2 60,000$

Emps

Generate Synthetic Input/Output Generate Synthetic Input/Output

Schema S

1

23 312

Make sure tables do not share tuples Rules are applied probabilistically

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The eTuner ArchitectureThe eTuner Architecture

StagedSearcher

Tuning Procedures

Workload Generator

Perturbation Rules

Matching Tool M

SyntheticWorkload

Tuned Matching Tool M

S Ω1 T1

S Ω2 T2

S Ωn Tn Schema S

More details / experiments in– Sayyadian et. al., VLDB-05

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eTuner: Current StatuseTuner: Current Status Only the first step

– but now we have a line of attack for a long-standing problem

Current directions– find optimal synthetic workload– develop faster search methods– extend for other matching scenarios

– adapt ideas to scenarios beyond schema matching – wrapper maintenance [VLDB-05b]– domain-specific search engine?

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Automate Integration Tasks: SummaryAutomate Integration Tasks: Summary Schema matching

– architecture: WebDB-00, SIGMOD-01, WWW-02– long-standing problems: SIGMOD-04a, eTuner [VLDB-05a]– learning/other techniques: CIDR-03, VLDBJ-03, MLJ-03, WebDB-03,

SIGMOD-04b, ICDE-05a, ICDE-05b– novel problem: debug schemas for interoperability [ongoing]– industry transfer: involving 2 startups – promote research area: workshop at ISWC-03, special issues in

SIGMOD Record-04 & AI Magazine-05, survey

Query reformulation: ICDE-02

Mediated schema construction: SIGMOD-04b, ICDM-05, ICDE-06

Duplicate tuple removal: AAAI-05, Tech report 06a, 06b

Wrapper maintenance: VLDB-05b

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Research DirectionsResearch Directions

Automate integration tasks– to minimize human labor

Leverage users– to spread the cost

Simplify integration tasks– so that they can be done quickly

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The MOBS ProjectThe MOBS Project Learn from multitude of users to improve tool accuracy,

thus significantly reducing builder workload

MOBS = Mass Collaboration to Build Systems

Questions

Answers

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Mass CollaborationMass Collaboration Build software artifacts

– Linux, Apache server, other open-source software

Knowledge bases, encyclopedia– wikipedia.com

Review & technical support websites– amazon.com, epinions.com, quiq.com,

Detect software bugs– [Liblit et al. PLDI 03 & 05]

Label images/pages on the Web– ESPgame, flickr, del.ici.ous, My Web 2.0

Improve search engines, recommender systems

Why not data integration systems?

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Example: Duplicate Data MatchingExample: Duplicate Data Matching

Hard for machine, but easy for human

Mouse for Dell laptop 200 series ...

Dell X200; mouse at reduced price ...

Dell laptop X200 with mouse ...

Serious problem in many settings (e.g., e.com)

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Key Challenges Key Challenges How to modify tools to learn from users? How to combine noisy user answers

How to obtain user participation?– data experts, often willing to help (e.g., Illinois Fire Service)– may be asked to help (e.g., e.com)– volunteer (e.g., online communities), "payment" schemes

Multiple noisy oracles

–build user models, learn them via interaction with users–novel form of active learning

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Current StatusCurrent Status

Develop first-cut solutions– built prototype, experimented with 3-132 users,

for source discovery and schema matching– improve accuracy by 9-60%, reduced workload by 29-88%

Built two simple DI systems on Web – almost exclusively with users

Building a real-world application– DBlife (more later)

See [McCann et al., WebDB-03, ICDE-05,

AAAI Spring Symposium-05, Tech Report-06]

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Research DirectionsResearch Directions

Automate integration tasks– to minimize human labor

Leverage users– to spread the cost

Simplify integration tasks– so that they can be done quickly

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Simplify Mediated Schema Simplify Mediated Schema Keyword Search over Multiple DatabasesKeyword Search over Multiple Databases

Novel problem Very useful for urgent / one-time DI needs

– also when users are SQL-illiterate (e.g., Electronic Medical Records)– also on the Web (e.g., when data is tagged with some structure)

Solution [Kite, Tech Report 06a]– combines IR, schema matching, data matching, and AI planning

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Simplify Wrappers Simplify Wrappers Structured Queries over Text/Web DataStructured Queries over Text/Web Data

Novel problem– attracts attention from database / AI / Web researchers at Columbia,

IBM TJ Watson/Almaden, UCLA, IIT-Bombay

[SQOUT, Tech Report 06b], [SLIC, Tech Report 06c]

SELECT ... FROM ... WHERE ...

E-mails, text, Web data, news, etc.

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Research DirectionsResearch Directions

Automate integration tasks– to minimize human labor

Leverage users – to spread the cost

Simplify integration tasks– so that they can be done quickly

Integration is difficult

Do best-effort integration

Integrate with text

Should leverage human

Build on this to promote Community Information Management

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Community Information ManagementCommunity Information Management Numerous communities on the Web

– database researchers, movie fans, legal professionals, bioinformatics, etc.

– enterprise intranets, tech support groups

Each community = many disparate data sources + people Members often want to query, monitor, discover info.

– any interesting connection between researchers X and Y?– list all courses that cite this paper– find all citations of this paper in the past one week on the Web– what is new in the past 24 hours in the database community? – which faculty candidates are interviewing this year, where?

Current integration solutions fall short of addressing such needs

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Cimple Project @ Illinois/WisconsinCimple Project @ Illinois/Wisconsin Software platform that can be rapidly deployed and

customized to manage data-rich online communities

Web pages

Text documents

* **

** * ***

SIGMOD-04

**

** give-talk

Jim Gray

Keyword search

SQL querying

Question answering

Browse

Mining

Alert/Monitor

News summary

Jim Gray

SIGMOD-04

**

Share / aggregation

Researcher

Homepages

Conference

Pages

Group Pages

DBworld

mailing list

DBLP

Import & personalize data

Tag entities/relationship / create new contents

Context-dependent services

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Prototype System: DBlifePrototype System: DBlife 1164 data sources, crawled daily, 11000+ pages / day

160+ MB, 121400+ people mentions 5600+ persons

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Structure Related ChallengesStructure Related Challenges

Extraction– better blackboxes, compose blackboxes, exploit domain knowledge

Maintenance– critical, but very little has been done

Exploitation– keyword search over extracted structure? SQL queries?– detect interesting events?

Web pages

Text documents

* **

** * ***

SIGMOD-04

**

** give-talk

Jim Gray

Keyword search

SQL querying

Question answering

Browse

Mining

Alert/Monitor

News summary

Jim Gray

SIGMOD-04

**

Researcher

Homepages

Conference

Pages

Group Pages

DBworld

mailing list

DBLP

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User Related ChallengesUser Related Challenges Users should be able to

– import whatever they want – correct/add to the imported data – extend the ER schema– create new contents for share/exchange– ask for context-dependent services

Examples– user imports a paper, system provides bib item– user imports a movie, add desc, tags it for exchange

Challenges– provide incentives, payment– handle malicious/spam users– share / aggregate user activities/actions/content

give-talk

Jim Gray

SIGMOD-04

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Comparison to Current My Web 2.0Comparison to Current My Web 2.0

Cimple focuses on domain-specific communities– not the entire Web

Besides page level– also considers finer granularities of entities / relations / attributes– leverages automatic “best-effort” data integration techniques

Leverages user feedback to further improve accuracy– thus combines both automatic techniques and human efforts

Considers the entire range of search + structured queries– and how to seamlessly move between them

Allows personalization and sharing– consider context-dependent services beyond keyword search

(e.g., selling, exchange)

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Applying Cimple to My Web 2.0: Applying Cimple to My Web 2.0: An ExampleAn Example

Going beyond just sharing Web pages Leveraging My Web 2.0 for other actions

– e.g., selling, exchanging goods (turning it to a classified ads platform?)

E.g., want to sell my house– create a page describing the house – save it to my account on My Web 2.0– tag it with “sell:house, sell, house, champaign, IL” – took me less than 5 minutes (not including creating the page)

– now if someone searches for any of these keywords …

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40

41

Here a button can be added to facilitate the “sell” action provide context-dependent services

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The Big Picture The Big Picture [Speculative Mode][Speculative Mode]

Structured data

(relational, XML)Unstructured data

(text, Web, email)

Multitude of users

Database: SQLIR/Web/AI/Mining: keyword, QA

Semantic Web

Industry/Real World

Many apps will involve all three

Exact integration will be difficult - best-effort is promising - should leverage human

Apps will want broad range of services - keyword search, SQL queries - buy, sell, exchange, etc.

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SummarySummary Data integration: crucial problem

– at intersection of database, AI, Web, IR

Integration @ Illinois in my group: – automate tasks to minimize human labor – leverage users to spread out the cost– simplify tasks so that they can be done quickly

Best-effort integration, should leverage human The Cimple project @ Illinois/Wisconsin

– builds on current work to study Community Information Management

A step toward managing structured + text + users synergistically!

See “anhai” on Yahoo for more details