Data Integration Zachary G. Ives University of Pennsylvania CIS 550 – Database & Information Systems November 14, 2007

2 Challenges of Mapping Schemas In a perfect world, it would be easy to match up items from one schema with another  Every table would have a similar table in the other schema  Every attribute would have an identical attribute in the other schema  Every value would clearly map to a value in the other schema Real world: as with human languages, things don’t map clearly!  May have different numbers of tables – different decompositions  Metadata in one relation may be data in another  Values may not exactly correspond  It may be unclear whether a value is the same

3 Different Aspects to Mapping Schema matching / ontology alignment How do we find correspondences between attributes? Entity matching / deduplication / record linking / etc. How do we know when two records refer to the same thing? Mapping definition  How do we specify the constraints or transformations that let us reason about when to create an entry in one schema, given an entry in another schema? Let’s see one influential approach to schema matching…

4 The LSD (Learning Source Descriptions) System Suppose user wants to integrate 100 data sources 1.User:  manually creates mappings for a few sources, say 3  shows LSD these mappings 2.LSD learns from the mappings  “Multi-strategy” learning incorporates many types of info in a general way  Knowledge of constraints further helps 3.LSD proposes mappings for remaining 97 sources

5 listed-price $250,000 $110, address price agent-phone description Example location Miami, FL Boston, MA... phone (305) (617) comments Fantastic house Great location... realestate.com location listed-price phone comments Schema of realestate.com If “fantastic” & “great” occur frequently in data values => description Learned hypotheses price $550,000 $320, contact-phone (278) (617) extra-info Beautiful yard Great beach... homes.com If “phone” occurs in the name => agent-phone Mediated schema

6 LSD’s Multi-Strategy Learning Use a set of base learners  Each exploits well certain types of information:  Name learner looks at words in the attribute names  Naïve Bayes learner looks at patterns in the data values  Etc. Match schema elements of a new source  Apply the base learners  Each returns a score  For different attributes one learner is more useful than another  Combine their predictions using a meta-learner Meta-learner  Uses training sources to measure base learner accuracy  Weighs each learner based on its accuracy

7 Boston, MA $110,000 (617) Great location Miami, FL $250,000 (305) Fantastic house Training the Learners Naive Bayes Learner (location, address) (listed-price, price) (phone, agent-phone) (comments, description)... (“Miami, FL”, address) (“$ 250,000”, price) (“(305) ”, agent-phone) (“Fantastic house”, description)... realestate.com Name Learner address price agent-phone description Schema of realestate.com Mediated schema location listed-price phone comments

8 Beautiful yard Great beach Close to Seattle (278) (617) (512) Seattle, WA Kent, WA Austin, TX Applying the Learners Name Learner Naive Bayes Meta-Learner (address,0.8), (description,0.2) (address,0.6), (description,0.4) (address,0.7), (description,0.3) (address,0.6), (description,0.4) Meta-Learner Name Learner Naive Bayes (address,0.7), (description,0.3) (agent-phone,0.9), (description,0.1) address price agent-phone description Schema of homes.com Mediated schema area day-phone extra-info

9 Putting It All Together: LSD System L1L1 L2L2 LkLk Mediated schema Source schemas Data listings Training data for base learners Constraint Handler Mapping Combination User Feedback Domain Constraints Matching PhaseTraining Phase

10 Mappings between Schemas LSD provides attribute correspondences, but not complete mappings Mappings generally are posed as views: define relations in one schema (typically either the mediated schema or the source schema), given data in the other schema  This allows us to “restructure” or “recompose + decompose” our data in a new way We can also define mappings between values in a view  We use an intermediate table defining correspondences – a “concordance table”  It can be filled in using some type of code, and corrected by hand

11 A Few Mapping Examples  Movie(Title, Year, Director, Editor, Star1, Star2)  PieceOfArt(ID, Artist, Subject, Title, TypeOfArt)  MotionPicture(ID, Title, Year) Participant(ID, Name, Role) CustIDCustName 1234Smith, J. PennIDEmpName 46732John Smith PieceOfArt(I, A, S, T, “Movie”) :- Movie(T, Y, A, _, S1, S2), ID = T || Y, S = S1 || S2 Movie(T, Y, D, E, S1, S2) :- MotionPicture(I, T, Y), Participant(I, D, “Dir”), Participant(I, E, “Editor”), Participant(I, S1, “Star1”), Participant(I, S2, “Star2”) T1 T2 Need a concordance table from CustIDs to PennIDs

12 Two Important Approaches  TSIMMIS [Garcia-Molina+97] – Stanford  Focus: semistructured data (OEM), OQL-based language (Lorel)  Creates a mediated schema as a view over the sources  Spawned a UCSD project called MIX, which led to a company now owned by BEA Systems  Other important systems of this vein: Penn  Information Manifold [Levy+96] – AT&T Research  Focus: local-as-view mappings, relational model  Sources defined as views over mediated schema  Requires a special  Led to peer-to-peer integration approaches (Piazza, etc.)  Focus: Web-based queriable sources

13 TSIMMIS  One of the first systems to support semi-structured data, which predated XML by several years: “OEM”  An instance of a “global-as-view” mediation system  We define our global schema as views over the sources

14 XML vs. Object Exchange Model Bernstein Newcomer Principles of TP Chamberlin DB2 UDB O1: book { O2: author { Bernstein } O3: author { Newcomer } O4: title { Principles of TP } } O5: book { O6: author { Chamberlin } O7: title { DB2 UDB } }

15 Queries in TSIMMIS Specified in OQL-style language called Lorel  OQL was an object-oriented query language that looks like SQL  Lorel is, in many ways, a predecessor to XQuery Based on path expressions over OEM structures: select book where book.title = “DB2 UDB” and book.author = “Chamberlin” This is basically like XQuery, which we’ll use in place of Lorel and the MSL template language. Previous query restated = for $b in AllData()/book where $b/title/text() = “DB2 UDB” and $b/author/text() = “Chamberlin” return $b

16 Query Answering in TSIMMIS Basically, it’s view unfolding, i.e., composing a query with a view  The query is the one being asked  The views are the MSL templates for the wrappers  Some of the views may actually require parameters, e.g., an author name, before they’ll return answers  Common for web forms (see Amazon, Google, …)  XQuery functions (XQuery’s version of views) support parameters as well, so we’ll see these in action

17 A Wrapper Definition in MSL Wrappers have templates and binding patterns ($X) in MSL: B :- B: }> // $$ = “select * from book where author=“ $X //  This reformats a SQL query over Book(author, year, title) In XQuery, this might look like: declare function GetBook($x AS xsd:string) as book { for $b in sql(“Amazon.DB”, “select * from book where author=‘” + $x +”’”) return {$b/title} $x } book title author … … … The union of GetBook’s results is unioned with others to form the view Mediator()

18 How to Answer the Query Given our query: for $b in Mediator()/book where $b/title/text() = “DB2 UDB” and $b/author/text() = “Chamberlin” return $b Find all wrapper definitions that:  Contain output enough “structure” to match the conditions of the query  Or have already tested the conditions for us!

19 Query Composition with Views We find all views that define book with author and title, and we compose the query with each: declare function GetBook($x AS xsd:string) as book { for $b in sql(“Amazon.DB”, “select * from book where author=‘” + $x + “’”) return {$b/title} {$x} } for $b in Mediator()/book where $b/title/text() = “DB2 UDB” and $b/author/text() = “Chamberlin” return $b book title author … …

20 Matching View Output to Our Query’s Conditions  Determine that $b/book/author/text()  $x by matching the pattern on the function’s output: declare function GetBook($x AS xsd:string) as book { for $b in sql(“Amazon.DB”, “select * from book where author=‘” + $x + “’”) return { $b/title } {$x} } let $x := “Chamberlin” for $b in GetBook($x)/book where $b/title/text() = “DB2 UDB” return $b book title author … …

21 The Final Step: Unfolding let $x := “Chamberlin” for $b in ( for $b’ in sql(“Amazon.com”, “select * from book where author=‘” + $x + “’”) return { $b/title } {$x} )/book where $b/title/text() = “DB2 UDB” return $b  How do we simplify further to get to here? for $b in sql(“Amazon.com”, “select * from book where author=‘Chamberlin’”) where $b/title/text() = “DB2 UDB” return $b

22 Virtues of TSIMMIS  Early adopter of semistructured data, greatly predating XML  Can support data from many different kinds of sources  Obviously, doesn’t fully solve heterogeneity problem  Presents a mediated schema that is the union of multiple views  Query answering based on view unfolding  Easily composed in a hierarchy of mediators

23 Limitations of TSIMMIS’ Approach Some data sources may contain data with certain ranges or properties  “Books by Aho”, “Students at UPenn”, …  If we ask a query for students at Columbia, don’t want to bother querying students at Penn…  How do we express these? Mediated schema is basically the union of the various MSL templates – as they change, so may the mediated schema

24 An Alternate Approach: The Information Manifold (Levy et al.) When you integrate something, you have some conceptual model of the integrated domain  Define that as a basic frame of reference, everything else as a view over it  “Local as View” May have overlapping/incomplete sources  Define each source as the subset of a query over the mediated schema  We can use selection or join predicates to specify that a source contains a range of values: ComputerBooks(…)  Books(Title, …, Subj), Subj = “Computers”

25 The Local-as-View Model The basic model is the following:  “Local” sources are views over the mediated schema  Sources have the data – mediated schema is virtual  Sources may not have all the data from the domain – “open-world assumption” The system must use the sources (views) to answer queries over the mediated schema

26 Query Answering Assumption: conjunctive queries, set semantics Suppose we have a mediated schema: author(aID, isbn, year), book(isbn, title, publisher) Suppose we have the query: q(a, t) :- author(a, i, _), book(i, t, p), t = “DB2 UDB” and sources: s1(a,t)  author(a, i, _), book(i, t, p), t = “123” … s5(a, t, p)  author(a, i, _), book(i,t), p = “SAMS” We want to compose the query with the source mappings – but they’re in the wrong direction!  Yet: everything in s1, s5 is an answer to the query!

27 Answering Queries Using Views Numerous recently-developed algorithms for these  Inverse rules [Duschka et al.]  Bucket algorithm [Levy et al.]  MiniCon [Pottinger & Halevy]  Also related: “chase and backchase” [Popa, Tannen, Deutsch] Requires conjunctive queries

28 Summary of Data Integration Local-as-view integration has replaced global-as-view as the standard  More robust way of defining mediated schemas and sources  Mediated schema is clearly defined, less likely to change  Sources can be more accurately described Methods exist for query reformulation, including inverse rules Integration requires standardization on a single schema  Can be hard to get consensus  Today we have peer-to-peer data integration, e.g., Piazza [Halevy et al.], Orchestra [Ives et al.], Hyperion [Miller et al.] Some other aspects of integration were addressed in related papers  Overlap between sources; coverage of data at sources  Semi-automated creation of mappings and wrappers Data integration capabilities in commercial products: BEA’s Liquid Data, IBM’s DB2 Information Integrator, numerous packages from middleware companies