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Data Integration: The Teenage Years Alon Halevy (Google) Anand Rajaraman (Kosmix) Joann Ordille (Avaya) VLDB 2006
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)
Acknowledgements Other members of the Information Manifold Project: – Jaewoo Kang (NCSU, Korea Univ. ) – Divesh Srivastava (AT&T Labs) – Shuky Sagiv (Hebrew U. ) – Tom Kirk
Acknowledgements To the SIGMOD 1996 Program committee For rejecting the earlier version of the paper.
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Data Integration Enterprise Databases Phenotype Gene Sequenceable Entity Protein Structured Vocabulary Nucleotide Sequence Experiment Microarray Experiment Legacy Databases Services and Applications
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
Design time Run time Mediated Schema query reformulation Semantic mappings optimization & execution wrapper wrapper
Semantic Mappings [a. k. a. Source Descriptions] Mediated Schema CD: ASIN, Title, Genre, … Artist: ASIN, name, … logic CDs Album ASIN Price Discount. Price Studio Books Title ISBN Price Discount. Price Edition Authors ISBN First. Name Last. Name Artists CDCategories ASIN Category Book. Categories ISBN Category ASIN Artist. Name Group. Name
Global-as-View (GAV) Mapping: CD(A, T, G) : - R 1(A, T, G) CD(A, T, G) : - R 2(A, T), R 3(T, G) Mediated Schema CD: ASIN, Title, Genre, … Artist: ASIN, name, … Source R 1 Source R 2 Source R 3 Source R 4 Source R 5
Local-as-View (LAV) Mapping: R 1(A, T, G) : - CD(A, T, G, Y), Artist(A, N), Y< 1970 R 2(A, T) : - CD(A, T, ”French”, Y) Mediated Schema CD: ASIN, Title, Genre, Year Artist: ASIN, Name, … Source R 1 Source R 2 Source R 3 Source R 4 Source R 5
Query Answering in LAV = Answering queries using views Given a set of views V 1, …, Vn, And a query Q, Can we answer Q using only the answers to V 1, …, Vn?
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.
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
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
Certain Answers Mediated schema: Route (Origin, Destination) Source 1: Origins SF NY Source 2: Destinations Seattle Seoul Query: Route (SF, Seattle)? Possible databases: Origin Destination SF NY Seattle Seoul SF NY Seoul Seattle
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
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.
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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.
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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.
Semantic Mappings Books. And. Music Title Author Publisher Item. ID Item. Type Suggested. Price Categories Keywords Inventory Database A Title ISBN Price Discount. Price Edition Authors ISBN First. Name Last. Name Book. Categories ISBN Category CDCategories CDs Album ASIN Price Discount. Price Studio ASIN Category Artists ASIN Artist. Name Group. Name Inventory Database B “Standards are great, but there are too many of them. ”
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]
A Machine Learning Approach [Doan et al. , 2001, ACM Distinguished Dissertation 2003] hes Mediated schema Pred atc ict new nm ones ive G • 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
Matching Real-Estate Sources Mediated schema address location price agent-phone listed-price phone description comments Schema of realestate. com location listed-price phone comments realestate. com Miami, FL $250, 000 (305) 729 0831 Fantastic house Boston, MA $110, 000 (617) 253 1429 Great location. . . homes. com price contact-phone extra-info $550, 000 (278) 345 7215 Beautiful yard $320, 000 (617) 335 2315 Great beach. . Learned hypotheses If “phone” occurs in the name => agent-phone If “fantastic” & “great” occur frequently in data values => description
Reference Reconciliation To 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, …
Query Processing To 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 De. Witt (1998). Query scrambling [Urhan & Franklin] Eddies [Avnur & Hellerstein] Convergent query processing [Ives et al. ]
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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
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…
After $30 M… Front-End User Applications Lens™ File Software Developers Kit Info. Browser™ Lens Builder™ NIMBLE™ APIs XML Query Nimble Integration Engine™ Cache Compiler Executor Metadata Server Common XML View Management Tools Integration Builder Concordance Developer Relational Data Warehouse/ Legacy Mart Flat File Web Pages Data Administrator Security Tools Integration Layer XML
NASDAQ 95 96 97 98 99 00 01 02 03 04 05 06
So… Back in the Lab • Model management • Peer data management systems • Data exchange
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.
Peer Data Management Systems Q 3 UW (Wisconsin) Stanford Q 1 Q 4 Berkeley Q 5 LAV, GLAV Q UW (Washington) DBLP Q 2 UW (Waterloo) Q 6 Cite. Seer
PDMS-Related Projects • • • Piazza (Washington) Hyperion (Toronto) Peer. DB (Singapore) Local relational models (Trento, Toronto) Active XML (INRIA) Edutella (Hannover, Germany) Semantic Gossiping (EPFL Lausanne) Raccoon (UC Irvine) Orchestra (U. Penn)
PDMS Challenges • Semantics: • careful about cycles • Optimization: • Compose mappings • Prune paths UW (Wisconsin) Stanford Berkeley • Manage networks: • Consistency • Quality • Caching UW (Washington) DBLP UW (Waterloo) Cite. Seer
Data Exchange S M T • Key question: given an instance of S and a mapping, create an instance for T. • [Fagin, Kolaitis, Popa & Tan]
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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
2006 Status Report [Enterprise Angle] • Enterprise Information Integration is established: – IBM, BEA, Oracle, Meta. Matrix, Composite, Actuate, … • Impact on design tools: – IBM Rational Data Architect – ADO. NET v. 3
Forrester Says… "Enterprises are facing the growing challenges of using disparate sources of data managed 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. "
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!
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.
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
Pay-as-you-go Data Management Dataspaces: Franklin, Halevy, Maier [see PODS 2006] Benefit Dataspaces Data integration solutions Artist: Mike Franklin Investment (time, cost)
Reusing Human Attention • Principle: § User action = statement of semantic relationship Ø Leverage actions to infer other semantic relationships • Examples – Providing a semantic mapping • Infer other mappings – Writing a query • Infer content of sources, relationships between sources – Creating a “digital workspace” • Infer “relatedness” of documents/sources • Infer co-reference between objects in the dataspace – Annotating, cutting & pasting, browsing among docs
Conclusion • We’ve done extremely well as a community! • Next challenge: data management and integration tools for the masses