CVRG Presenter Disclosure Information Tahsin Kurc, PhD Center for Comprehensive Informatics Emory University CardioVascular Research Grid Core Infrastructure FINANCIAL DISCLOSURE: None UNLABELED/UNAPPROVED USES DISCLOSURE: None

CVRG CardioVascular Research Grid Core Infrastructure Tahsin Kurc Department of Biomedical Engineering Center for Comprehensive Informatics Emory University

CVRG CVRG Infrastructure Provides software support for resource providers to implement and deploy databases and analysis programs as interoperable services and enforce access control policies, application developers to implement client programs that can access such services, and researchers to discover such services, carry out federated queries against these resources, and compose analysis workflows. Designed as a Service Oriented and Model Driven architecture A common set of tools and middleware infrastructure for Managing metadata Building services and client applications Federated query and workflow support Security

CVRG Why CVRG Architecture Challenges in Information Synthesis Location A SNP, ECG, Protein, Image data Location B SNP, ECG, Clinical Image data Location C SNP, Protein ECG, Image data Location C Image Analysis ECG Analysis Location D Image Analysis ECG Analysis SNP, ECG, Protein databases at other institutions Different database systems, different data representations, security Different invocations of programs, remote access, how to transfer data.

CVRG CVRG Infrastructure Stack Portals, Application Components Domain Analytical and Data Services (QTVi, AutoQRS, ECG, SNP, Image, etc.) Domain Analytical and Data Services (QTVi, AutoQRS, ECG, SNP, Image, etc.) Databases, Analysis Programs, Storage and Computation Resources High Level Low Level Middleware and Common Tools (caGrid: Metadata Management, Introduce, GAARDS, XML Data Services) Middleware and Common Tools (caGrid: Metadata Management, Introduce, GAARDS, XML Data Services)

CVRG CVRG Infrastructure Stack Portals, Application Components Domain Analytical and Data Services (QTVi, AutoQRS, ECG, SNP, Image, etc.) Domain Analytical and Data Services (QTVi, AutoQRS, ECG, SNP, Image, etc.) Middleware and Common Tools (caGrid: Metadata Management, Introduce, GAARDS, XML Data Services) Middleware and Common Tools (caGrid: Metadata Management, Introduce, GAARDS, XML Data Services) Databases, Analysis Programs, Storage and Computation Resources High Level Low Level

CVRG Middleware and Common Tools: caGrid A Grid software middleware infrastructure consisting of services, toolkits, and runtime environment Service Oriented Model Driven Object oriented view, published information models, strongly- typed services Rich metadata A production Grid deployment of the core services provided by that infrastructure Security, Workflow, Data Services Infrastructure, Service Development/Deployment, Metadata/Advertisement/Discovery

CVRG Model Driven Architecture An object-oriented representation of resources: client and service interfaces are object-oriented Object definitions are well-defined and registered in the environment Object definitions and object attributes are annotated with terms from controlled vocabularies Objects are exchanged between services as XML documents conforming to a published XML schema CVRG leverages technologies from caBIG® and the National Center for Biomedical Ontology (NCBO) Developed ontology for Electrophysiological data Developing ontology for ECG data

CVRG caGrid Security Infrastructure (GAARDS) Provides services and tools for the administration and enforcement of security policy in an enterprise Grid. Dorian Allows accounts managed in external domains to be federated and managed in the Grid. Grid Grouper/CSM Provides a group-based authorization solution for the Grid Grid Trust Service Supports applications and services in deciding whether or not signers of digital credentials can be trusted.

CVRG CVRG Extensions: XML Data Service Motivated by the need to manage ECG data in HL7 aECG format Provide rapid creation of data service using XML schema and native XML database Supports data subsetting and retrieving large amounts of XML data Current Users: CVRG ECG services, analysis parameter, and analysis output management IVI Annotation and Image Markup (AIM)

CVRG CVRG Infrastructure Stack Portals, Application Components Domain Analytical and Data Services (QTVi, AutoQRS, ECG, SNP, Image, etc.) Domain Analytical and Data Services (QTVi, AutoQRS, ECG, SNP, Image, etc.) Databases, Analysis Programs, Storage and Computation Resources High Level Low Level Middleware and Common Tools (caGrid: Metadata Management, Introduce, GAARDS, XML Data Services) Middleware and Common Tools (caGrid: Metadata Management, Introduce, GAARDS, XML Data Services)

CVRG CVRG Extensions: Out-of-the-box Functionality ECG Data Analysis and Management ECG HL7aECG Data Service ECG WFDB Data Service ECG Physionet Analysis Service ECG Berger QT Analysis Service Analysis results data services Multiscale Data Exploration and Integration ProteinDB Data Service SNP Data Service Clinical Information Data Service (based on OpenClinica) Male Subjects over 50 QTVi 90 th Percentile Subjects with SNP marker rs HL7 aECG XML Repository (Raw and Analyzed Data) Norav ECG Data QT Analysis

CVRG CVRG Extensions: Out-of-the-box Functionality Image management support and integration of imaging with other data types Based on the caGrid-enabled in-vivo imaging middleware VirtualPACS and DICOM Data Services Image annotations Support for security and access control PACS Image Annotations Image Database CVRG Image Management

CVRG Data Analysis and Integration with CVRG Location A ECG, SNP, Clinical, Image data Location B SNP, ECG, Clinical Image Data Location C SNP, ECG, Clinical, Image data Location C Image Analysis ECG Analysis Location D Image Analysis ECG Analysis ECG, Clinical, SNP, Image databases at other institutions caGrid Service Interfaces CVRG Environment Registered Object Definitions Advertisement Log on, Grid credentials Query and Analysis Workflow Discovery

CVRG CVRG Salient Features Information can be maintained in disparate and heterogeneous systems Does not preclude use of centralized systems Collaborative environments can be established dynamically Secure sharing of data and analytical resources Limit access to resources Enforce local access control policies Relevant resources can be discovered based on metadata Information can be synthesized by federated queries and distributed analysis workflows