1 / 43

Information & Data Integration

Information & Data Integration. Combining information from multiple autonomous sources. The end-game: 3 options. Have an in-class final exam 5/8 2:30pm is the designated time Have a take-home exam Make the final home-work into a take home

zona
Download Presentation

Information & Data Integration

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Information & Data Integration Combining information from multiple autonomous sources Information Integration on the Web (MA-1)

  2. The end-game: 3 options • Have an in-class final exam • 5/8 2:30pm is the designated time • Have a take-home exam • Make the final home-work into a take home • ..and have a mandatory discussion class on 5/8 2:30pm Also, note the change in demo schedule Information Integration on the Web (MA-1)

  3. Today’s Agenda • Discuss Semtag/Seeker • Lecture start on Information Integration Information Integration on the Web (MA-1)

  4. Information Integration • Combining information from multiple autonomous information sources • And answering queries using the combined information • Many variations depending on • The type of information sources (text? Data? Combination?) • Data vs. Information integration • Horizontal vs. Vertical integration • The level of eagerness of the integration • Ad hoc vs. Pre-mediated integration • Pre-mediation itself can be Warehouse vs online approaches • Generality of the solution • Mashup vs. Mediated Information Integration on the Web (MA-1)

  5. 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 Integrationas making the database repositoryof the web.. The “IR” View

  6. Services Source Trust Webpages Structured data Sensors (streaming Data) Source Fusion/ Query Planning Query Mediator Executor Monitor Answers The “DB” View Integration as Mediation over Autonomous databases Information Integration on the Web (MA-1)

  7. 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)

  8. 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)

  9. 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)

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

  11. 4/26 Information Integration –2 Focus on Data Integration Information Integration on the Web (MA-1)

  12. Information Integration Text Integration Data Integration Soft-Joins Collection Selection Data aggregation (vertical integration) Data Linking (horizontal integration) Information Integration on the Web (MA-1)

  13. “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)

  14. Collection Selection Information Integration on the Web (MA-1)

  15. Collection Selection/Meta Search Introduction • Metasearch Engine • A system that provides unified access to multiple existing search engines. • Metasearch Engine Components • Database Selector • Identifying potentially useful databases for each user query • Document Selector • Identifying potentially useful document returned from selected databases • Query Dispatcher and Result Merger • Ranking the selected documents Information Integration on the Web (MA-1)

  16. Collection Query Results Selection Execution Merging WSJ WP CNN NYT FT Collection Selection Information Integration on the Web (MA-1)

  17. Evaluating collection selection • Let c1..cj be the collections that are chosen to be accessed for the query Q. Let d1…dk be the top documents returned from these collections. • We compare these results to the results that would have been returned from a central union database • Ground Truth: The ranking of documents that the retrieval technique (say vector space or jaccard similarity) would have retrieved from a central union database that is the union of all the collections • Compare precision of the documents returned by accessing Information Integration on the Web (MA-1)

  18. General Scheme & Challenges • Get a representative of each of the database • Representative is a sample of files from the database • Challenge: get an unbiased sample when you can only access the database through queries. • Compare the query to the representatives to judge the relevance of a database • Coarse approach: Convert the representative files into a single file (super-document). Take the (vector) similarity between the query and the super document of a database to judge that database’s relevance • Finer approach: Keep the representative as a mini-database. Union the mini-databases to get a central mini-database. Apply the query to the central mini-database and find the top k answers from it. Decide the relevance of each database based on which of the answers came from which database’s representative • You can use an estimate of the size of the database too • What about overlap between collections? (See ROSCO paper) Information Integration on the Web (MA-1)

  19. Uniform Probing for Content Summary Construction • Automatic extraction of document frequency statistics from uncooperative databases • [Callan and Connell TOIS 2001],[Callan et al. SIGMOD 1999] • Main Ideas • Pick a word and send it as a query to database D • RandomSampling-OtherResource(RS-Ord): from a dictionary • RandomSampling-LearnedResource(RS-Lrd): from retrieved documents • Retrieval the top-K documents returned • If the number of retrieved documents exceeds a threshod T, stop, otherwise retart at the beginning • k=4 , T=300 • Compute the sample document frequency for each word that appeared in a retrieved document. Information Integration on the Web (MA-1)

  20. CORI Net Approach (Representative as a super document) • Representative Statistics • The document frequency for each term and each database • The database frequency for each term • Main Ideas • Visualize the representative of a database as a super document, and the set of all representative as a database of super documents • Document frequency becomes term frequency in the super document, and database frequency becomes document frequency in the super database • Ranking scores can be computed using a similarity function such as the Cosine function Information Integration on the Web (MA-1)

  21. ReDDE Approach(Representative as a mini-collection) • Use the representatives as mini collections • Construct a union-representative that is the union of the mini-collections (such that each document keeps information on which collection it was sampled from) • Send the query first to union-collection, get the top-k ranked results • See which of the results in the top-k came from which mini-collection. The collections are ranked in terms of how much their mini-collections contributed to the top-k answers of the query. • Scale the number of returned results by the expected size of the actual collection Information Integration on the Web (MA-1)

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

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

  24. 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)

  25. 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)

  26. 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)

  27. 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)

  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)

  34. Fully Query-time 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 pre-fixed 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 Laziness in Data 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)

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

  36. 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)

  37. 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)

  38. 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

  39. 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)

  40. 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)

  41. 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)

  42. 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)

More Related