Information Integration

1. Information Integration Information Integration on the Web (MA-1)

2. Services Source Trust Webpages Structured data Sensors (streaming Data) Source Fusion/ Query Planning Query Mediator Executor Monitor Answers The Problem Information Integration on the Web (MA-1)

3. Services Source Trust Ontologies; Source/Service Descriptions Probing Queries Webpages Structured data Sensors (streaming Data) Source Fusion/ Query Planning Needs to handle: Multiple objectives, Service composition, Source quality & overlap Source Calls Query Preference/Utility Model Updating Statistics Replanning Requests Executor Needs to handle Source/network Interruptions, Runtime uncertainity, replanning Monitor Answers • User queries refer to the mediated schema. • Data is stored in the sources in a local schema. • Content descriptions provide the semantic mappings between the different schemas. • Mediator uses the descriptions to translate user queries into queries on the sources. DWIM Information Integration on the Web (MA-1)

4. Skeptic’s corner Who is dying to have it? (Applications) • WWW: • Comparison shopping • Portals integrating data from multiple sources • B2B, electronic marketplaces • Science and culture: • Medical genetics: integrating genomic data • Astrophysics: monitoring events in the sky. • Culture: uniform access to all cultural databases produced by countries in Europe provinces in Canada • Enterprise data integration • An average company has 49 different databases and spends 35% of its IT dollars on integration efforts Information Integration on the Web (MA-1)

5. Skeptic’s corner Why isn’t this just • Search engines do text-based retrieval of URLS • Works reasonably well for single document texts, or for finding sites based on single document text • Cannot integrate information from multiple documents • Cannot do effective “query relaxation” or generalization • Cannot link documents and databases • The aim of Information integration is to support query processing over structured and semi-structured sources as well as services. Information Integration on the Web (MA-1)

6. Skeptic’s corner Is it like Expedia/Travelocity/Orbitz… • Surpringly, NO! • The online travel sites don’t quite need to do data integration; they just use SABRE • SABRE was started in the 60’s as a joint project between American Airlines and IBM • It is the de facto database for most of the airline industry (who voluntarily enter their data into it) • There are very few airlines that buck the SABRE trend—SouthWest airlines is one (which is why many online sites don’t bother with South West) • So, online travel sites really are talking to a single database (not multiple data sources)… • To be sure, online travel sources do have to solve a hard problem. Finding an optimal fare (even at a given time) is basically computationally intractable (not to mention the issues brought in by fluctuating fares). So don’t be so hard on yourself • Check out http://www.maa.org/devlin/devlin_09_02.html Information Integration on the Web (MA-1)

7. Skeptic’s corner Why isn’t this just Databases Distributed Databases • No common schema • Sources with heterogeneous schemas (and ontologies) • Semi-structured sources • Legacy Sources • Not relational-complete • Variety of access/process limitations • Autonomous sources • No central administration • Uncontrolled source content overlap • Unpredictable run-time behavior • Makes query execution hard • Predominantly “Read-only” • Could be a blessing—less worry about transaction management • (although the push now is to also support transactions on web) Information Integration on the Web (MA-1)

8. Are we talking “comparison shopping” agents? • Certainly closer to the aims of these • But: • Wider focus • Consider larger range of databases • Consider services • Implies more challenges • “warehousing” may not work • Manual source characterization/ integration won’t scale-up Information Integration on the Web (MA-1)

9. 11/7 --Project part C due 11/30 --Homework 4 due 11/14 --Google guy talk on 11/18 (TBA) Information Integration on the Web (MA-1)

10. Services Source Trust Ontologies; Source/Service Descriptions Probing Queries Webpages Structured data Sensors (streaming Data) Source Fusion/ Query Planning Needs to handle: Multiple objectives, Service composition, Source quality & overlap Source Calls Query Preference/Utility Model Updating Statistics Replanning Requests Executor Needs to handle Source/network Interruptions, Runtime uncertainity, replanning Monitor Answers • User queries refer to the mediated schema. • Data is stored in the sources in a local schema. • Content descriptions provide the semantic mappings between the different schemas. • Mediator uses the descriptions to translate user queries into queries on the sources. DWIM Information Integration on the Web (MA-1)

11. Linkage Queries • Querying integrated information sources (e.g. queries to views, execution of web-based queries, …) • Data mining & analyzingintegrated information (e.g., collaborative filtering/classification learning using extracted data, …) • Discovering information sources (e.g. deep web modeling, schema learning, …) • Gathering data (e.g., wrapper learning & information extraction, federated search, …) • Cleaning data (e.g., de-duping and linking records) to form a single [virtual] database Information Integration Another view

12. “Data Aggregation” (Vertical) All sources export (parts of a) single relation No need for joins etc Could be warehouse or virtual E.g. BibFinder, Junglee, Employeds etc Challenges: Schema mapping; data overlap Different “Integration” scenarios • “Collection Selection” • All sources export text documents • E.g. meta-crawler etc. • Challenges: Similarity definition; relevance handling • “Data Linking” (Horizontal) • Joins over multiple relations stored in multiple DB • E.g. Softjoins in WHIRL • Ted Kennedy episode • Challenges: record linkage over text fields (object mapping); query reformulation • All together (vertical & horizontal) • Many interesting research issues • ..but few actual fielded systems Information Integration on the Web (MA-1)

13. Dimensions to Consider • How many sources are we accessing? • How autonomous are they? • Can we get meta-data about sources? • Is the data structured? • Discussion about soft-joins. See slide next • Supporting just queries or also updates? • Requirements: accuracy, completeness, performance, handling inconsistencies. • Closed world assumption vs. open world? • See slide next Information Integration on the Web (MA-1)

14. Soft Joins..WHIRL [Cohen] • We can extend the notion of Joins to “Similarity Joins” where similarity is measured in terms of vector similarity over the text attributes. So, the join tuples are output n a ranked form—with the rank proportional to the similarity • Neat idea… but does have some implementation difficulties • Most tuples in the cross-product will have non-zero similarities. So, need query processing that will somehow just produce highly ranked tuples • Also other similarity/distance metrics may be used • E.g. Edit distance

16. Local Completeness Information • If sources are incomplete, we need to look at each one of them. • Often, sources are locally complete. • Movie(title, director, year) complete for years after 1960, or for American directors. • Question: given a set of local completeness statements, is a query Q’ a complete answer to Q? Problems: 1. Sources may not be interested in giving these! Need to learn hard to learn! 2. Even if sources are willing to give, there may not be any “big enough” LCWs Saying “I definitely have the car with vehicle ID XXX is useless Advertised description True source contents Guarantees (LCW; Inter-source comparisons) Information Integration on the Web (MA-1)

17. Source Descriptions • Contains all meta-information about the sources: • Logical source contents (books, new cars). • Source capabilities (can answer SQL queries) • Source completeness (has all books). • Physical properties of source and network. • Statistics about the data (like in an RDBMS) • Source reliability • Mirror sources • Update frequency. Information Integration on the Web (MA-1)

18. We just did this! Source Access • How do we get the “tuples”? • Many sources give “unstructured” output • Some inherently unstructured; while others “englishify” their database-style output • Need to (un)Wrap the output from the sources to get tuples • “Wrapper building”/Information Extraction • Can be done manually/semi-manually Information Integration on the Web (MA-1)

19. Source Fusion/Query Planning • Accepts user query and generates a plan for accessing sources to answer the query • Needs to handle tradeoffs between cost and coverage • Needs to handle source access limitations • Needs to reason about the source quality/reputation Information Integration on the Web (MA-1)

20. Monitoring/Execution • Takes the query plan and executes it on the sources • Needs to handle source latency • Needs to handle transient/short-term network outages • Needs to handle source access limitations • May need to re-schedule or re-plan Information Integration on the Web (MA-1)

21. Models for Integration Modified from Alon Halevy’s slides Information Integration on the Web (MA-1)

22. Solutions for small-scale integration • Mostly ad-hoc programming: create a special solution for every case; pay consultants a lot of money. • Data warehousing: load all the data periodically into a warehouse. • 6-18 months lead time • Separates operational DBMS from decision support DBMS. (not only a solution to data integration). • Performance is good; data may not be fresh. • Need to clean, scrub you data. Junglee did this, for employment classifieds Information Integration on the Web (MA-1)

23. The Virtual Integration Architecture • Leave the data in the sources. • When a query comes in: • Determine the relevant sources to the query • Break down the query into sub-queries for the sources. • Get the answers from the sources, and combine them appropriately. • Data is fresh. Approach scalable • Issues: • Relating Sources & Mediator • Reformulating the query • Efficient planning & execution Garlic [IBM], Hermes[UMD];Tsimmis, InfoMaster[Stanford]; DISCO[INRIA]; Information Manifold [AT&T]; SIMS/Ariadne[USC];Emerac/Havasu[ASU] Information Integration on the Web (MA-1)

24. Desiderata for Relating Source-Mediator Schemas • Expressive power: distinguish between sources with closely related data. Hence, be able to prune access to irrelevant sources. • Easy addition: make it easy to add new data sources. • Reformulation: be able to reformulate a user query into a query on the sources efficiently and effectively. • Nonlossy: be able to handle all queries that can be answered by directly accessing the sources Reformulation Information Integration on the Web (MA-1)

25. 11/9 --Google Mashups—what type of integration are they? --Mturk.comcollaborative computing the capitalist way Information Integration on the Web (MA-1)

26. Information Integration Text/Data Integration Service integration Data Integration Collection Selection Data aggregation (vertical integration) Data Linking (horizontal integration) Information Integration on the Web (MA-1)

27. Fully automated II (blue sky for now) Get a query from the user on the mediator schema Go “discover” relevant data sources Figure out their “schemas” Map the schemas on to the mediator schema Reformulate the user query into data source queries Optimize and execute the queries Return the answers Fully hand-coded II Decide on the only query you want to support Write a (java)script that supports the query by accessing specific (pre-determined) sources, piping results (through known APIs) to specific other sources Examples include Google Map Mashups Extremes of automation in Information Integration (most interesting action is “in between”) E.g. We may start with known sources and their known schemas, do hand-mapping and support automated reformulation and optimization Information Integration on the Web (MA-1)

28. “View” Refresher Virtual vs Materialized Differences minor for data aggregation… Approaches for relating source & Mediator Schemas • Global-as-view (GAV): express the mediated schema relations as a set of views over the data source relations • Local-as-view (LAV): express the source relations as views over the mediated schema. • Can be combined…? Let’s compare them in a movie Database integration scenario.. Information Integration on the Web (MA-1)

29. Global-as-View Express mediator schema relations as views over source relations Mediated schema: Movie(title, dir, year, genre), Schedule(cinema, title, time). Create View Movie AS select * from S1[S1(title,dir,year,genre)] union select * from S2[S2(title, dir,year,genre)] union[S3(title,dir), S4(title,year,genre)] select S3.title, S3.dir, S4.year, S4.genre from S3, S4 where S3.title=S4.title Information Integration on the Web (MA-1)

30. Global-as-View Express mediator schema relations as views over source relations Mediated schema: Movie(title, dir, year, genre), Schedule(cinema, title, time). Create View Movie AS select * from S1 [S1(title,dir,year,genre)] union select * from S2 [S2(title, dir,year,genre)] union[S3(title,dir), S4(title,year,genre)] select S3.title, S3.dir, S4.year, S4.genre from S3, S4 where S3.title=S4.title Mediator schema relations are Virtual views on source relations Information Integration on the Web (MA-1)

31. Create Source S1 AS select * from Movie Create Source S3 AS select title, dir from Movie Create Source S5 AS select title, dir, year from Movie where year > 1960 AND genre=“Comedy” S1(title,dir,year,genre) S3(title,dir) S5(title,dir,year), year >1960 Sources are “materialized views” of mediator schema Local-as-View: example 1 Express source schema relations as views over mediator relations Mediated schema: Movie(title, dir, year, genre), Schedule(cinema, title, time). Information Integration on the Web (MA-1)

32. GAV vs. LAV Source S4: S4(cinema, genre) Mediated schema: Movie(title, dir, year, genre), Schedule(cinema, title, time). Lossy mediation Information Integration on the Web (MA-1)

33. Not modular Addition of new sources changes the mediated schema Can be awkward to write mediated schema without loss of information Query reformulation easy reduces to view unfolding (polynomial) Can build hierarchies of mediated schemas Best when Few, stable, data sources well-known to the mediator (e.g. corporate integration) Garlic, TSIMMIS, HERMES Modular--adding new sources is easy Very flexible--power of the entire query language available to describe sources Reformulation is hard Involves answering queries only using views (can be intractable—see below) Best when Many, relatively unknown data sources possibility of addition/deletion of sources Information Manifold, InfoMaster, Emerac, Havasu GAV vs. LAV Information Integration on the Web (MA-1)