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

3. 2 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. 3 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. 4 Semantic interoperability Syntactic interoperability Interoperability ability of a system to access and use the parts or equipment of another system

6. 5 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. 6 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. 7 Limitations of MDA  Limited expressivity for semantics  No facility for runtime semantic metadata management

9. 8 caCORE Syntactic and Semantic Integration MDA Plus a whole lot more!

10. 9 caCORE Bioinformatics ObjectsEnterprise VocabularyCommon Data Elements SECURITYSECURITY

11. 10 Use Cases  Description  Actors  Basic Course  Alternative Course

12. 11 Bioinformatics Objects

13. 12  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 Common Data Elements

14. 13 Preferred Name Synonyms Definition Relationships Concept Code Enterprise Vocabulary Description Logic

15. 14 Semantic metadata example: Agent Taxol 007

16. 15 Why do you need metadata? Class/ Attribute Example Object Data CIA MetadataNCI Metadata Agent A sworn intelligence agent; a spy Chemical compound administered to a human being to treat a disease or condition, or prevent the onset of a disease or condition Agent nSCNumber 007 Identifier given to an intelligence agent by the National Security Council Identifier given to chemical compound by the US Food and Drug Administration Nomenclature Standards Committee Agent name Taxol CIA code name given to intelligence agents Common name of chemical compound used as an agent

17. 16 Computable Interoperability Agent name nSCNumber FDAIndID CTEPName IUPACName Drug id NDCCode approver approvalDate fdaCode C1708:C41243 C1708 My modelYour model

18. 17 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. 18 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. 19 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. 20 caGrid caCORE meets grid technology!

22. 21 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. 22 Gold Cancer Center Cancer Center NCI OTHER caBIG SERVICE PROVIDERS OTHER TOOLKITS Silver

24. 23 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. 24 caGrid 0.5 Architecture (May be updated for 1.0) Grid Communication Protocol Service Description Service Business Process Service Registry Security Semantic service Resource Management Functions Quality of Service ID Resolution Transport GSI GUMS GT3 Analytical OGSA-DAIGT3 GLOBUS Toolkit caDSR EVSGT3 UI caDSR Index GME CAMS

26. 25 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. 26 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. 27 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. 28 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. 29 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. 30 Data Service Query Scenario 4.Data Source is queried by the Grid Data Service 5.Grid Data Service Builds a CQL Result Set 6.Result Set is serialized and returned to the client 7.Client deserializes result set 8.Result set is iterated with client tools to retrieve objects 1.Client builds a CQL Query 2.CQL Query is serialized and submitted to the Grid Data Service 3.Grid Data Service deserializes the CQL Query Object and processes it

32. 31 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. 32 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. 33 Acknowledgements (caGrid Team)  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  Duke University – A. Jamie Cuticchia –Patrick McConnell  Georgetown University –Colin Freas –Paul A. Kennedy –Chad La Joie  SAIC (http://www.saic.com)http://www.saic.com –Manav Kher  ScenPro/Semantic Bits –Vinay Kumar –David Wellborn –Valerie Bragg  Booz | Allen | Hamilton (http://www.bah.com)http://www.bah.com –Arumani Manisundaram –Michael Keller –Reechik Chatterjee

35. 34 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