purpose designed building Science Research Infrastructure Fund: £ 6m

1. Biological data integration by bi-directional schema transformation rulesAlexandra Poulovassilis, Birkbeck, U. of London

2. The London Knowledge Lab Institute of Education University of London Birkbeck College University of London purpose designed building Science Research Infrastructure Fund: £ 6m Research staff and students: 50 Location: Bloomsbury Open: June 2004 Computer scientists Experts in information systems, information management, web technologies, personalisation, ubiquitous technologies … Social scientists Experts in education, sociology, culture and media, semiotics, philosophy, knowledge management ...

3. LKL Research Themes Research at the London Knowledge Lab consists mainly of externally funded projects by EU, EPSRC, ESRC, AHRB, BBSRC, JISC, Wellcome Trust – currently about 25 projects. Four broad themes guide our work and inform our research strategy: • new forms of knowledge • turning information into knowledge • the changing cultures of new media • creating empowering technologies for formal and informal learning

4. Turning Information Into Knowledge • The need to cope with ubiquitous, complex, incomplete and inconsistent information is pervasive in our societies • How can people benefit from this information in their learning, working and social lives ? • What new techniques are necessary for managing, accessing, integrating and personalising such information ? • How to design and build tools that help people to understand such information and generate new knowledge from it ?

5. Turning Information Into Knowledge – Information Integration AutoMed (EPSRC) – developing tools for semi-automatic integration of heterogeneous information sources – can handle both structured and semi-structured (RDF/S, XML) data – can handle virtual, materialised and hybrid integration scenarios – application in biological data integration, e-learning, p2p data integration ISPIDER (BBSRC e-Science programme) – developing an integrated platform of proteomic data sources, enabled as Grid and Web services – collaboration with groups at EBI, Manchester, UCL

6. The AutoMed Project • Partners: Birkbeck and Imperial Colleges • Data integration based on schema equivalence • Low-level metamodel, the Hypergraph Data Model (HDM), in terms of which higher-level modelling languages are defined – extensible therefore with new modelling languages • Automatically provides a set of primitive equivalence-preserving schema transformations for higher-level modelling languages: • addT(c,q) deleteT(c,q) renameT(c,n,n’) • There are also two more primitive transformations for imprecise integration scenarios: • extendT(c,Range q q’) contractT(c,Range q q’)

7. AutoMed Features • Schema transformations are automatically reversible: • addT/deleteT(c,q) by deleteT/addT(c,q) • extendT(c,Range q1 q2) by contractT(c,Range q1 q2) • renameT(c,n,n’) by renameT(c,n’,n) • Hence bi-directional transformation pathways (more generally transformation networks) are defined between schemas • The queries within transformations allow automatic data and query translation • Schemas may be expressed in a variety of modelling languages • Schemas may or may not have a data source associated with them; thus, virtual, materialised or hybrid integration can be supported

8. Schema Transformation/Integration Networks GS id id id id id US1 US2 USi USn … … … … LS1 LS2 LSi LSn

9. Schema Transformation/Integration Networks (cont’d) • On the previous slide: • GS is a global schema • LS1, …, LSn are local schemas • US1, …, USn are union-compatible schemas • the transformation pathways between each pair LSi and USi may consist of add, delete, rename, expand and contract primitive transformation, operating on any modelling construct defined in the AutoMed Model Definitions Repository • the transformation pathway between USi and GS issimilar • the transformation pathway between each pair of union-compatible schemas consists of id transformation steps

10. AutoMed Architecture Schema and Transformation Repository Wrapper Schema Transformation and Integration Tools Global Query Processor Model Definitions Repository Global Query Optimiser Model Definition Tool Schema Evolution Tool

11. Comparison with GAV & LAV Data Integration • Global-As-View (GAV) approach: specify GS constructs by view definitions over LSi constructs • Local-As-View (LAV) approach: specify LS constructs by view definitions over GS constructs

12. GAV Example • student(id,name,left,degree) = [ x,y,z,w |x,y,z,w,_ug  x,_,_,_,_phd  x,y,z,w,_phd  w = ‘phd’] • monitors(sno,id) = [ x,y |x,_,_,_,yug  x,_,_,_,_phd  x,ysupervises] • staff(sno,sname,dept) = [ x,y,z |x,y,z,w,_tutor  x,_,_supervisor  x,y,zsupervisor]

13. LAV Example • tutor(sno,sname) = [ x,y | x,y,_staff  x,zmonitors  z,_,_,wstudent  w  ‘phd’] • ug(id,name,left,degree,sno) = [ x,y,z,w,v | x,y,z,wstudent  v,xmonitors  w  ‘phd’] • phd, supervises, supervisor are defined similarly

14. Evolution problems of GAV and LAV • GAV does not readily support evolution of local schemas e.g. adding an ‘age’ attribute to ‘phd’ invalidates some of the global view definitions • In LAV, changes to a local schema impact only the derivation rules defined for that schema e.g. adding an ‘age’ attribute to ‘phd’ affects only the rule defining ‘phd’ • But LAV has problems if one wants to evolve the global schema since all the rules defining local schema constructs in terms of the global schema would need to be reviewed • These problems are exacerbated in P2P data integration scenarios where there is no distinction between local and global schemas

15. AutoMed approach, ‘Growing’ Phaseassuming initially a schema U = S1 + S2 • addRel(<<student,id>>, [x | x <<ug,id>>  x  <<phd,id>>]) • addAtt(<<student,name>>, [<x,y> | (<x,y><<ug,name>>  x  <<phd,id>>)  <x,y>  <<phd,name>>]) • addAtt(<<student,left>>, [<x,y> | (<x,y> <<ug,left>>  x  <<phd,id>>)  <x,y>  <<phd,left>>]) …

16. AutoMed approach, `Shrinking’ Phase • contrAtt(<<tutor,sname>>, Range [<x,y> | <x,y> <<staff,sname>>  <z,x>  <<ug,sno>>] Any) • contrRel(<<tutor,sno>>, Range [x | x<<staff,sno>>  <z,x>  <<ug,sno>>] Any) • Similarly contractions for the ug attributes and relation

17. AutoMed approach, Shrinking Phase (cont’d) • contrAtt(<<phd,title>>, Range Void Any) • delAtt(<<phd,left>>, [<x,y> | <x,y><<student,left>>  x  <<phd,id>>]) • delAtt(<<phd,name>>, [<x,y> | <x,y>  <<student,name>>  x  <<phd,id>>]) • delRel(<<phd,id>>, [x | x <<student,id>>  <x,’phd’>  <<student,degree>>]) • Similarly deletions for supervises and supervisor

18. AutoMed vs GAV/LAV/GLAV • AutoMed schema transformation pathways capture at least the information available from GAV and LAV rules: • add/extend transformations correspond to GAV rules • delete/contract transformations correspond to LAV rules • We discussed this our ICDE’03 paper where we termed our integration approach both-as-view (BAV) • In particular, we discussed how GAV and LAV view definitions can be derived from a BAV specification • GLAV rules e :- e’ are captured by BAV transformations of the form add(T,e); …; del(T,e’) • Thus any reasoning or processing that is possible using GAV, LAV or GLAV is also possible using BAV

19. Schema Evolution in BAV New Global Schema S’ • Unlike GAV/LAV/GLAV, BAV framework readily supports the evolution of both localand global schemas • The evolution of the global or local schema is specified by a schema transformation pathway from the old to the new schema • For example, the figure on the right shows transformation pathways T from an old to a new global or local schema T Global Schema S New Local Schema S’ Local Schema S T

20. Global Schema Evolution • Each transformation step t in T:SS’ is considered in turn • if t is an add, delete or rename then schema equivalence is preserved and there is nothing further to do (except perhaps optimise the extended transformation pathway); the extended pathway can be used to regenerate the necessary GAV or LAV views • if t is a contract then there will be information present in S that is no longer available in S’; again there is nothing further to do • if t is an extend then domain knowledge is required to determine if the new construct in S’ can in fact be derived from existing constructs; if not, there is nothing further to do; if yes, the extend step is replaced by an add step

21. Local Schema Evolution • This is a bit more complicated as it may require changes to be propagated also to the global schema(s) • Again each transformation step t in T:SS’ is considered in turn • In the case that t is an add, delete, rename or contract step, the evolution can be carried out automatically • If it is an extend, then domain knowledge is required • See our CAiSE’02, ICDE’03 and ER’04 papers for more details • The last of these discusses a materialised data integration scenario where the old/new global/local schemas have an extent

22. Global Query Processing • We handle query language heterogeneity by translation into/from a functional intermediate query language– IQL • A query Q expressed in a high-level query language on a schema S is first translated into IQL (this functionality is not yet supported in the AutoMed toolkit) • View definitions are derived from the transformation pathways between S and the requested data source schemas • These view definitions are substituted into Q, reformulating it into an IQL query over source schema constructs

23. Global Query Processing (cont’d) • Query optimisation (currently algebraic) and query evaluation then occur • During query evaluation, the evaluator submits to wrappers sub-queries that they are able to translate into the local query language. Currently, AutoMed supports wrappers for SQL, OQL, XPath, XQuery and flat-file data sources • The wrappers translate sub-query results back into the IQL type system • Further query post-processing then occurs in the IQL evaluator

24. Other AutoMed research at BBK • As well as virtual integration of data sources, we have investigated using AutoMed for materialiseddata integratione.g.a data warehousing approach • In particular, Hao Fan has worked on incremental view maintenance, data lineage tracing and schema evolution over AutoMed schema transformation pathways • Lucas Zamboulis has been looking at semi-automatic techniques for transforming and integrating heterogeneous XML data • In recent work we have also investigated using correspondences to RDFS schemas to enhance these techniques

25. Other AutoMed research at BBK (cont’d) • Dean Williams has been working on extracting structure from unstructured text sources • The aim here is to integrate information extracted from unstructured text with structured information available from other sources • Dean is using existing technology (the GATE tool) for the text annotation and IE part of this work • The information extracted from the text is matched with existing structured information to derive new instance data and perhaps also new schema fragments • AutoMed is being used for the schema and data integration aspects of this project

26. Other AutoMed research at Imperial • Automatic generation of equivalences between different data models • A graphical schema & transformations editor • Data mining techniques for extracting schema equivalences • Optimising schema transformation pathways

27. ISPIDER Project • Partners: Birkbeck, EBI, Manchester, UCL • Aims: • Vast, heterogeneous biological data • Need for interoperability • Need for efficient processing • Development of Proteomics Grid Infrastructure, use existing proteomics resources and develop new ones, develop new proteomics clients for querying, visualisation, workflow etc.

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33. myGrid / DQP / AutoMed • myGrid: collection of services/components allowing high-level integration of data/applications for in-silico experiments in biology • DQP • OGSA-DAI (Open Grid Services Architecture Data Access and Integration) • Distributed query processing over OGSA-DAI enabled resources • Current research: AutoMed – DQP interoperation • Future research: AutoMed – myGrid workflows interoperation

34. DQP – AutoMed Interoperability • Data sources wrapped with OGSA-DAI • AutoMed OGSA-DAI wrappers extract data sources’ metadata • Semantic integration of data sources using AutoMed transformation pathways into an integrated AutoMed schema • IQL queries submitted to this integrated schema are: • Reformulated to IQL queries on the data sources, using the AutoMed transformation pathways • Submitted to DQP for evaluation

35. Data source schema extraction • AutoMed wrapper requests the schema of the data source using an OGSA-DAI service • The service replies with the source schema encoded in XML • The AutoMed wrapper creates the corresponding schema in the AutoMed repository

36. Using AutoMed for in the BioMap Project • Relational/XML data sources containing protein sequence, structure, function and pathway data; gene expression data; other experimental data • Wrapping of data sources • Translation of source and global schemas into AutoMed’s XML schema • Domain expert provides matchings between constructs in source and global schemas • Automatic schema restructuring, with automatic generation of schema transformation pathways • See DILS’05 paper for more details Integrated Database Integrated Database Wrapper AutoMed Integrated Schema n n T o o r i i a t t n y a a s a m y f p m o r a w a r r o t w f m h h o s h t a f w t n t a s a i a a o y p n p r n T a r T AutoMed AutoMed AutoMed ….. Relational Relational XMLDSS Schema Schema Schema RDB RDB XML ….. Wrapper Wrapper Wrapper XML RDB ….. File RDB

37. Ongoing and future research • Using the BAV approach for data integration in Grid and P2P environments • The integration may be virtual, materialised or hybrid • P2P query processing over BAV pathways • P2P update processing over BAV pathways • Use of ECA rules and a P2P ECA rule execution engine • Optimisation of ECA rules on semi-structured data