Data Integration: The Teenage Years

1. Data Integration: The Teenage Years Alon Halevy (Google) Anand Rajaraman (Kosmix) Joann Ordille (Avaya) VLDB 2006

2. Agenda • A few perspectives on the last 10 years • Technical, commercial • Perspectives from our personal paths • Wild speculations about the future • This is not a survey on data integration (See the paper in the proceedings for another non-survey)

3. Acknowledgements Other members of the Information Manifold Project: • Jaewoo Kang (NCSU, Korea Univ.) • Divesh Srivastava (AT&T Labs) • Shuky Sagiv (Hebrew U.) • Tom Kirk

4. Acknowledgements To the SIGMOD 1996 Program committee For rejecting the earlier version of the paper.

5. Timeline 95 96 97 98 99 00 01 02 03 04 05 06

6. Enterprise Databases Sequenceable Entity Structured Vocabulary Experiment Phenotype Gene Nucleotide Sequence Microarray Experiment Protein Legacy Databases Services and Applications Data Integration

7. The Information Manifold • Goal: integrate data from multiple sources on the web: Find the Woody Allen movies playing in my area, and their reviews • Need to describe the data sources: • Contents, constraints, access patterns

8. wrapper wrapper wrapper wrapper wrapper Design time Run time Mediated Schema query reformulation Semantic mappings optimization & execution

9. Semantic Mappings[a.k.a. Source Descriptions] Mediated Schema CD: ASIN, Title, Genre,… Artist: ASIN, name, … logic CDs Album ASIN Price DiscountPrice Studio Books Title ISBN Price DiscountPrice Edition Authors ISBN FirstName LastName Artists ASIN ArtistName GroupName CDCategories ASIN Category BookCategories ISBN Category

10. Source Source Source Source Source Global-as-View (GAV) Mapping: CD(A,T,G) :- R1(A,T,G) CD(A,T,G) :- R2(A,T), R3(T,G) Mediated Schema CD: ASIN, Title, Genre,… Artist: ASIN, name, … R1 R2 R3 R4 R5

11. Source Source Source Source Source Local-as-View (LAV) Mapping: R1(A,T,G) :- CD(A,T,G,Y), Artist(A,N), Y< 1970 R2(A,T) :- CD(A,T,”French”,Y) Mediated Schema CD: ASIN, Title, Genre, Year Artist: ASIN, Name, … R1 R2 R3 R4 R5

12. Query Answering in LAV =Answering queries using views Given a set of views V1,…,Vn, And a query Q, Can we answer Q using only the answers to V1,…,Vn?

13. AQUV (I) • [Larson et al., 85 & 87], [Tsatalos et al., 94], [Chaudhuri et al., 95], • Focus on AQUV for: • Query optimization • Supporting physical data independence • Every commercial DBMS supports AQUV.

14. AQUV (II) • AQUV for data integration: • Find maximally contained rewriting • Not necessarily equivalent rewriting • Algorithms: • Bucket algorithm [LRO, 96] • Inverse rules [Duschka, 97] • Minicon [Pottinger and Halevy, 2000] • Views and security: [Miklau and Suciu, 04] Survey: Halevy, VLDB Journal, 2001

15. Some Subsequent Results • Semantics of data integration: • Abiteboul & Duschka, 1998: certain answers • Open vs. closed world assumption • CWA is bad complexity news! Survey: Lenzerini, PODS 2002

16. Certain Answers Mediated schema: Route (Origin, Destination) Source 1: Origins SF NY Source 2: Destinations Seattle Seoul Query: Route (SF, Seattle)? Possible databases:

17. Some Subsequent Results • Limitations due to binding patterns • Input title, get book info [Rajaraman et al., 95] • Additional query processing capabilities • Form applies multiple predicates • Disjunction, negation in sources. • Ordering sources, probabilistic mappings • [Florescu et al., 97, Doan et al., Dong et al.] • GLAV [Millstein et al., 99] Survey: Lenzerini, PODS 2002

18. A word on Description Logics • Selecting relevant sources = reasoning. • Description logics to the rescue: • [Catarci and Lenzerini, 93] • Information Manifold • Combined the Classic DL with Datalog (CARIN) • See AAAI-96 (not sigmod) • Brought DL and DB closer together. • A very active area of research today.

19. XML 95 96 97 98 99 00 01 02 03 04 05 06

20. XML and Semi-structured Data • Tsimmis: semi-structured data for integration. • XML: whetted the integration appetites • We have the syntax • Now just solve the silly semantics problems • Don’t bother: we’ll all standardize on DTDs. • XML will have a significant role on the data integration industry and research.

21. XML 95 96 97 98 99 00 01 02 03 04 05 06

22. Back in the Lab… • Two observations: • Who’s going to write all these LAV/GAV formulas? • This was the bottleneck. • Once we have mappings, how can we execute queries? • Traditional plan-then-execute doesn’t work.

23. Semantic Mappings Books Title ISBN Price DiscountPrice Edition Authors ISBN FirstName LastName BooksAndMusic Title Author Publisher ItemID ItemType SuggestedPrice Categories Keywords BookCategories ISBN Category CDCategories ASIN Category CDs Album ASIN Price DiscountPrice Studio Artists ASIN ArtistName GroupName Inventory Database A Inventory Database B “Standards are great, but there are too many of them.”

24. Techniques for Schema Mapping[Survey by Rahm and Bernstein, VLDBJ 2001] • Compare schema elements based on: • Names (or n-grams) • Data types and instances • Text descriptions, integrity constraints • Combine multiple techniques: • [Momis, Cupid, LSD, Coma] • Create mappings from matches • [Clio @ IBM + Miller]

25. A Machine Learning Approach[Doan et al., 2001, ACM Distinguished Dissertation 2003] Mediated schema • Many mapping tasks are repetitive • Learn from previous experience: • Build a classifier for every element of the mediated schema. • Many kinds of cues  meta-strategy learning Given matches Predict new ones

26. Matching Real-Estate Sources Mediated schema address price agent-phone description locationlisted-pricephonecomments Learned hypotheses If “phone” occurs in the name => agent-phone Schema of realestate.com location Miami, FL Boston, MA ... listed-price $250,000 $110,000 ... phone (305) 729 0831 (617) 253 1429 ... comments Fantastic house Great location ... realestate.com If “fantastic” & “great” occur frequently in data values => description homes.com price $550,000 $320,000 ... contact-phone (278) 345 7215 (617) 335 2315 ... extra-info Beautiful yard Great beach ...

27. Reference ReconciliationTo Join or not to Join? • Many ways to refer to the same object in the world: • “IBM”, “International Business Machines” • Alon Levy, Alon Halevy • Automated methods are necessity • Can’t go through all the data manually • Very active area in ML, KDD, DB, UAI, …

28. Query ProcessingTo Plan or to Execute? • In addition to distributed query processing issues: • Few statistics, if any. • Network behavior issues: latency, burstiness,… • Garlic @IBM • “Adaptive query processing”: • Stonebraker saw it coming in Ingres. • Revivals by Graefe (1993) and DeWitt (1998). • Query scrambling [Urhan & Franklin] • Eddies [Avnur & Hellerstein] • Convergent query processing [Ives et al.]

29. XML 95 96 97 98 99 00 01 02 03 04 05 06

30. Commercialization • Late 90’s – anything goes. • Want money from VC’s? • Say “XML” 3 times loud and clear. • Academia at the forefront: • Nimble (UW), Cohera (Berkeley), Enosys (UCSD),… • Big companies took notice • Some faster than others

31. Commercialization Retrospective[See Panel-of-Experts, SIGMOD 05] • Uphill battle vs. the warehousing folks • Virtual integration was more “pay-as-you-go” • Another battle with the EAI folks • Should really be a symbiosis there. • Go vertical or horizontal? • Obvious: go vertical if you can find the right one. • The technology worked • But it’s all in the timing…

32. After $30M… XML Query XML Relational Data Warehouse/ Mart Legacy Flat File Web Pages Front-End Lens Builder™ User Applications Lens™ File InfoBrowser™ Software Developers Kit NIMBLE™ APIs Management Tools Integration Layer Nimble Integration Engine™ Metadata Server Cache Compiler Executor Security Tools Common XML View Integration Builder Concordance Developer Data Administrator

33. NASDAQ XML 95 96 97 98 99 00 01 02 03 04 05 06

34. So… Back in the Lab • Model management • Peer data management systems • Data exchange

35. Model Management[Bernstein et al.] • Generic infrastructure for managing schemas and mappings: • Manipulate models and mappings as bulk objects • Operators to create & compose mappings, merge & diff models • Short operator scripts can solve schema integration, schema evolution, reverse engineering, etc. • First challenge: semantics of operators.

36. Q3 Q1 Q4 Q5 Q6 Q Q2 Peer Data Management Systems UW (Wisconsin) Stanford Berkeley LAV, GLAV DBLP CiteSeer UW (Washington) UW (Waterloo)

37. PDMS-Related Projects • Piazza (Washington) • Hyperion (Toronto) • PeerDB (Singapore) • Local relational models (Trento, Toronto) • Active XML (INRIA) • Edutella (Hannover, Germany) • Semantic Gossiping (EPFL Lausanne) • Raccoon (UC Irvine) • Orchestra (U. Penn)

38. PDMS Challenges • Semantics: • careful about cycles • Optimization: • Compose mappings • Prune paths UW (Wisconsin) Stanford Berkeley • Manage networks: • Consistency • Quality • Caching DBLP UW (Washington) UW (Waterloo) CiteSeer

39. Data Exchange • Key question: given an instance of S and a mapping, create an instance for T. • [Fagin, Kolaitis, Popa & Tan] S T M

40. XML 95 96 97 98 99 00 01 02 03 04 05 06

41. XML ? 95 96 97 98 99 00 01 02 03 04 05 06

42. 2006 Status Report[The People Angle] • Joann @ Avaya • Integrating communications into business processes • Anand @ Kosmix • Creating a new kind of search company • Alon @ Google • Working for Joann’s old boss • Deep web evangelist • Pondering data management for the masses

43. 2006 Status Report[Enterprise Angle] • Enterprise Information Integration is established: • IBM, BEA, Oracle, MetaMatrix, Composite, Actuate, … • Impact on design tools: • IBM Rational Data Architect • ADO .NET v. 3

44. Forrester Says… "Enterprises are facing the growing challenges of using disparate sources of datamanaged by different applications, including problems with data integration, security, performance, availability and quality.... New technology is emerging that Forrester has coined "information fabric,"a term defined as a virtualized data layer that integrates heterogeneous data and content repositories in real time.... The potential benefits of this technology are so great that enterprises should develop a strategy to leverage information fabric technology as it becomes more widely available."

45. 2006 Status Report[Web Angle] • Vertical search engines: one domain • At scale: need even better source descriptions • deep web can be surfaced • Terminology: Data integration = mashups!

46. Wikipedia: A mashup is a website or Web 2.0 application that uses content from more than one source to create a completely new service. This is akin to transclusion.

50. Looking Ahead • Data management: from the enterprise to the masses • Challenges: • Databases of everything • Need support for collaboration • Help people structure their data • Pay-as-you go data management