From Data Integration to Community Information Management

1. From Data Integration to Community Information Management AnHai Doan University 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

2. Data Integration Challenge Find houses with 4 bedrooms priced under 300K New researcher realestate.com homeseekers.com homes.com

3. Actually 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 ...

4. wrapper wrapper wrapper Architecture of Data Integration Systems Find houses with 4 bedroomspriced under 300K mediated schema source schema 1 source schema 2 source schema 3 homes.com realestate.com houses.com

5. 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 …

6. DI Systems are Still Very Difficultto Build and Maintain • Builder must execute multiple tasks select data sources create wrappers create mediated schemas match schemas eliminate duplicate tuples monitor changes etc. • 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

7. Data Integration @ Illinois • Directions: • automate tasks to minimize human labor • leverage users to spread out the cost • simplify tasksso that they can be done quickly

8. Sample Research on Automating Integration Tasks: Schema Matching Mediated-schema price agent-name address 1-1 match complex match homes.com listed-price contact-name city state 320K Jane Brown Seattle WA 240K Mike Smith Miami FL

9. 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

10. Why 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

11. Current 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

12. Example: LSD [Doan et al. SIGMOD-01] agent name Mediated schema address agent-name 0.5 Name Matcher contact agent Urbana, IL James Smith Seattle, WA Mike Doan Combiner homes.com 0.1 0.3 Naive Bayes Matcher area contact-agent Peoria, IL (206) 634 9435 Kent, WA (617) 335 4243 area => (address, 0.7), (description, 0.3) contact-agent => (agent-phone, 0.7), (agent-name, 0.3) comments => (address, 0.6), (desc, 0.4) Constraint Enforcer Match Selector area = address contact-agent = agent-phone ... comments = desc Only one attribute of source schema matches address

13. Match selector Match selector Match selector Constraint enforcer Constraint enforcer Constraint enforcer Combiner Matcher Combiner Combiner Matcher 1 Matcher n … Multi-Component Matching Solutions • 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] • Such systems are very powerful ... • maximize accuracy; highly customizable • ... but place a serious tuning burden on domain users Match selector Combiner Matcher … Matcher 1 Matcher n … Matcher 1 Matcher n LSD COMA SF LSD-SF

14. Match selector Constraint enforcer Combiner … Matcher 1 Matcher n Tuning Schema Matching Systems • Given a particular matching situation • how to select the right components? • how to adjust the multitude of knobs? Bipartite graph selector Threshold selector • Characteristics of attr. •Split measure A* search enforcer Relax. labeler ILP •Post-prune? •Size of validation set Average combiner Min combiner Max combiner Weighted sum combiner • • • q-gram name matcher Decision tree matcher Naïve Bays matcher TF/IDF name matcher SVM matcher Knobs of decision tree matcher Execution graph Library of matching components • Untuned versions produce inferior accuracy

15. But Tuning is Extremely Difficult • 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  Developing efficient tuning techniques is now crucial

16. The eTuner Solution [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

17. Solving Challenge 1: 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

18. 3 12 Generate Synthetic Input/Output Emps Employees id = id first = NONE last = emp-last salary = wage Perturb data tuples Perturb # of columns Emps Employees Perturb table and column names Schema S 1 3 2 • Make sure tables do not share tuples • Rules are applied probabilistically

19. The eTuner Architecture Tuning Procedures Perturbation Rules Workload Generator Staged Searcher Synthetic Workload Tuned Matching Tool M SΩ1 T1 SΩ2 T2 SΩn Tn Matching Tool M Schema S • More details / experiments in • Sayyadian et. al., VLDB-05

20. eTuner: 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?

21. Automate 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

22. Research Directions • Automate integration tasks • to minimize human labor • Leverage users • to spread the cost • Simplify integration tasks • so that they can be done quickly

23. The MOBS Project • Learn from multitude of users to improve tool accuracy, thus significantly reducing builder workload • MOBS = MassCollaborationto Build Systems Questions Answers

24. Mass 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?

25. Example: Duplicate Data Matching • Hard for machine, but easy for human • Serious problem in many settings (e.g., e.com) Dell laptop X200 with mouse ... Mouse for Dell laptop 200 series ... Dell X200; mouse at reduced price ...

26. 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 ofactive learning

27. Current 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]

28. Research Directions • Automate integration tasks • to minimize human labor • Leverage users • to spread the cost • Simplify integration tasks • so that they can be done quickly

29. Simplify Mediated Schema Keyword 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

30. Simplify Wrappers Structured 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.

31. Research 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

32. Community 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

33. Cimple Project @ Illinois/Wisconsin • Software platform that can be rapidly deployed and customized to manage data-rich online communities Keyword search SQL querying Question answering Browse Mining Alert/Monitor News summary Jim Gray Jim Gray Researcher Homepages Conference Pages Group Pages DBworld mailing list DBLP Web pages * * * * give-talk * * * SIGMOD-04 SIGMOD-04 * * * * * * * * Text documents Import & personalize data Tag entities/relationship / create new contents Context-dependent services Share / aggregation

34. Prototype System: DBlife • 1164 data sources, crawled daily, 11000+ pages / day  160+ MB, 121400+ people mentions  5600+ persons

35. Structure 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? Keyword search SQL querying Question answering Browse Mining Alert/Monitor News summary Jim Gray Jim Gray Researcher Homepages Conference Pages Group Pages DBworld mailing list DBLP Web pages * * * * give-talk * * * SIGMOD-04 SIGMOD-04 * * * * * * * * Text documents

36. User 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 Jim Gray give-talk SIGMOD-04

37. Comparison 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)

38. Applying Cimple to My Web 2.0: An 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 …

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

42. The Big Picture [Speculative Mode] 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. Structured data (relational, XML) Unstructured data (text, Web, email) Database: SQL IR/Web/AI/Mining: keyword, QA Multitude of users Semantic Web Industry/Real World

43. Summary • 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