Cancer Biomedical Informatics Grid (caBIG) – An Approach towards Data Access and Integration

1. 0 Cancer Biomedical Informatics Grid (caBIG) – An Approach towards Data Access and Integration Avinash Shanbhag Director, Core Infrastructure Engineering National Cancer Institute Center for Bioinformatics

2. Relieve suffering and death due to cancer by the year 2015 National Cancer Institute 2015 Goal

3. Origins of caBIG • Need: Enable investigators and research teams nationwide to combine and leverage their findings and expertise in order to meet NCI 2015 Goal. • Strategy: Create scalable, actively managed organization that will connect members of the NCI-supported cancer enterprise by building a biomedical informatics network and data can be seamlessly shared

4. caBIG Challenges • Handle diversity of data types • Precise “Meaning” of data • Provide local hosting of data • Local access control • Provide tools to “publish” and “access” data easily • High Performance computing will be needed in future

5. Interoperability ability of a system to access and use the parts or equipment of another system Syntacticinteroperability Semanticinteroperability

6. How to Achieve Interoperability for Data Systems? • Well Documented public API access to data • Based on object oriented abstraction of underlying data • No particular technology or tool specified • Abstraction layer must be derived using widely accepted “standards” • Model Driven Architecture • Information Model is the “Metadata” of the data and needs to be persisted and accessible via API • Need to be able to “unambiguously” and programmatically determine the meaning of data

7. OMG Model Driven Architecture (MDA) Approach • Analyze the problem space and develop the artifacts for each scenario • Use Cases • Use Unified Modeling Language (UML) to standardize model representations and artifacts. Design the system by developing artifacts based on the use cases • Class Diagram – Information Model • Sequence Diagram – Temporal Behavior • Use meta-model tools to generate the code

8. Limitations of MDA • Limited expressivity for semantics • No facility for runtime semantic metadata management

9. Syntactic and Semantic Integration MDA Plus a whole lot more! caCORE

10. S E C U R I T Y Bioinformatics Objects Common Data Elements Enterprise Vocabulary caCORE

11. Use Cases • Description • Actors • Basic Course • Alternative Course

12. Bioinformatics Objects

13. Common Data Elements • What do all those data classes and attributes actually mean, anyway? • Data descriptors or “semantic metadata” required • Computable, commonly structured, reusable units of metadata are “Common Data Elements” or CDEs. • NCI uses the ISO/IEC 11179 standard for metadata structure and registration • Semantics all drawn from Enterprise Vocabulary Service resources

14. Description Logic Enterprise Vocabulary Concept Code Relationships Preferred Name Definition Synonyms

15. Semantic metadata example: Agent <Agent> <name>Taxol</name> <nSCNumber>007</nSCNumber> </Agent>

16. Why do you need metadata?

17. C1708 C1708 C1708:C41243 C1708:C41243 Computable Interoperability Agent Drug name id nSCNumber NDCCode CTEPName approvalDate FDAIndID approver IUPACName fdaCode My model Your model

18. Cancer Data Standards Repository • ISO/IEC 11179 Registry for Common Data Elements – units of semantic metadata • Client for Enterprise Vocabulary: metadata constructed from controlled terminology and annotated with concept codes • Precise specification of Classes, Attributes, Data Types, Permissible Values: Strong typing of data objects.

19. caCORE Tools • UML Loader: automatically register UML models as metadata components • CDE Curation: Fine tune metadata and constrain permissible values with data standards • Form Builder: Create standards-based data collection forms • CDE Browser: search and export metadata components • Common Security Module: Provides role based security

20. caCORE Software Development Kit • UML Modeling Tool (any with XMI export) • Semantic Connector (concept binding utility) • UML Loader (model registration in caDSR) • Codegen (middleware code generator) • Security Adaptor (Common Security Module) caCORE SDK generates syntactically and semantically interoperable data service system

21. caGrid caCORE meets grid technology!

22. Use cases not satisfied by caCORE alone • Advertisement • Service Provider composes service metadata describing the service and publishes it to grid. • Discovery • Researcher (or application developer) specifies search criteria describing a service of interest • The research submits the discovery request to a discovery service, which identifies a list of services matching the criteria, and returns the list. • Invocation • Researcher (or application developer) instantiates the grid service and access its resources

23. Silver Silver Silver Silver Gold Silver Silver Silver OTHER TOOLKITS NCI OTHER caBIG SERVICE PROVIDERS Cancer Center Cancer Center Cancer Center Cancer Center Cancer Center

24. caGrid Components • Leverage existing technologies: • caDSR, EVS, Mobius GME: Common data elements, controlled vocabularies, schema management • Globus Toolkit (currently version 4.0.1) • Core grid services infrastructure • Service deployment, service registry, invocation, base security infrastructure • Additional Core Infrastructure • Higher-level security services (Dorian) • Grid service access to metadata components (caDSR, GME, etc) • Workflow, Identifier services • Service Provider Tooling (Introduce) • Graphical service development and configuration environment • Abstractions from service infrastructure for Data and Analytical services • Deployment wizards • Client Tooling • High-level APIs for interacting with core components and services • Graphical Tools

25. caGrid 0.5 Architecture(May be updated for 1.0) Functions Quality of Service Semanticservice ID Resolution Analytical Business Process UI GUMS caDSR Security Resource Management Service Registry Service OGSA-DAI GT3 GSI GME Index caDSR Service Description Grid Communication Protocol GLOBUS Toolkit CAMS GT3 EVS GT3 Transport

26. Data Object Semantics, Metadata, and Schemas • Object oriented, APIs, well-defined data types • Classes defined in UML and converted into ISO/IEC 11179, registered in the caDSR • Definitions drawn from Enterprise Vocabulary Services (EVS), relationships semantically described • XML serialization of objects adhere to XML schemas registered in the Global Model Exchange (GME)

27. Introduce Toolkit • A framework which enables fast and easy creation of caGrid compatible services whether they are data, analytical, custom, or core services. • Provide easy to use graphical service authoring tools. • Hide all “grid-ness” from the developer so that they can concentrate on the domain expert implementation. • Utilize best practice layered grid service architectures. • Handle all service architecture requirements of the caGrid. • Strong service interface data typing • Metadata and service registration • Grid security integration

28. Data Service Access on caGrid • Specialization of caGrid grid services to expose data through a common query interface • Present an object view of data sources • Exposed objects are registered in caDSR and their XML representation in GME • Queries made with caBIG Query Language (CQL) Query objects • Results returned as objects (or identifiers) nested in a CQL Query Result Set

29. Data Service Query Language • Specialization of caGrid grid services to expose data through a common query interface • Present an object view of data sources • Exposed objects are registered in caDSR and their XML representation in GME • Queries made with CQL Query objects • Results returned as objects (or identifiers) nested in a CQL Query Result Set

30. Data Service Interface public CQLQueryResultsType processQuery(CQLQueryType query) • Data Provider’s only responsibility is to implement CQL over their local data resource • A default implementation will be provided for caCORE SDK created systems • caGrid provides grid service implementation to invoke provider’s CQL implementation • Service provides all features necessary for compliance, such as advertisement of data service metadata, and security integration

31. Data Service Query Scenario • Client builds a CQL Query • CQL Query is serialized and submitted to the Grid Data Service • Grid Data Service deserializes the CQL Query Object and processes it • Data Source is queried by the Grid Data Service • Grid Data Service Builds a CQL Result Set • Result Set is serialized and returned to the client • Client deserializes result set • Result set is iterated with client tools to retrieve objects

32. Federated and Aggregated Queries • Componentized library being developed to facilitate limited federating and aggregating queries • An extension language used to describe distributed queries • Library creates and executes a Query Plan for the distributed query, using multiple CQL queries to targeted data services

33. Data Service Client Tooling • APIs provided to discover available data services on the grid based on client-defined criteria (such exposed data models and concepts) • Object-Oriented API for building queries, querying a given data service, and processing the results • Client tools available to iterate query result sets • Object iterator deserializes XML into registered objects • XML iterator simply returns XML documents

34. Ohio State University - Department of BioMedical Informatics Dave Ervin Shannon Hastings Tahsin Kurc Stephen Langella Scott Oster Joel Saltz Argonne National Lab / University of Chicago William Allcock Jarek Gawor Ravi Madduri Frank Siebenlist Michael Wilde Acknowledgements (caGrid Team) • Duke University • A. Jamie Cuticchia • Patrick McConnell • Georgetown University • Colin Freas • Paul A. Kennedy • Chad La Joie • SAIC(http://www.saic.com) • Manav Kher • ScenPro/Semantic Bits • Vinay Kumar • David Wellborn • Valerie Bragg • Booz | Allen | Hamilton (http://www.bah.com) • Arumani Manisundaram • Michael Keller • Reechik Chatterjee

35. Acknowledgements • NCI • Andrew von Eschenbach • Anna Barker • Wendy Patterson • OC • DCTD • DCB • DCP • DCEG • DCCPS • CCR • Industry Partners • SAIC • BAH • Oracle • ScenPro • Ekagra • Apelon • Terrapin Systems • Panther Informatics • NCICB • Ken Buetow • Peter Covitz • George Komatsoulis Denise Warzel Frank Hartel • Sherri De Coronado • Dianne Reeves • Gilberto Fragoso • Jill Hadfield • Leslie Derr