I2b2 grid integration with Ontology Mapper

Slides:



Advertisements
Similar presentations
ATML Readiness For Use Phase II. Phase II Readiness For Use The ATML: Phase II will build on the Core phases, adding additional ATML components and features.
Advertisements

18 Copyright © 2005, Oracle. All rights reserved. Distributing Modular Applications: Introduction to Web Services.
|epcc| NeSC Workshop Open Issues in Grid Scheduling Ali Anjomshoaa EPCC, University of Edinburgh Tuesday, 21 October 2003 Overview of a Grid Scheduling.
CACORE TOOLS FEATURES. caCORE SDK Features caCORE Workbench Plugin EA/ArgoUML Plug-in development Integrated support of semantic integration in the plugin.
BI Web Intelligence 4.0. Business Challenges Incorrect decisions based on inadequate data Lack of Ad hoc reporting and analysis Delayed decisions.
GridVine: Building Internet-Scale Semantic Overlay Networks By Lan Tian.
I2b2 grid integration with Health Ontology Mapper CTSA Informatics All Hands Meeting October 24, 2009 Rob Wynden (UCSF)
Technical BI Project Lifecycle
Introduction to Databases
Connect. Communicate. Collaborate Click to edit Master title style MODULE 1: perfSONAR TECHNICAL OVERVIEW.
Polaris: A System for Query, Analysis and Visualization of Multi-dimensional Relational Databases Presented by Darren Gates for ICS 280.
Some Thoughts on Data Representation 47th IETF AAAarch Research Group David Spence Merit Network, Inc.
File Systems and Databases
Geographic Information Systems
Automatic Data Ramon Lawrence University of Manitoba
Creating a SharePoint App with Microsoft Access Services
Building Ad-Hoc Reports using the SQL Server 2005 Reporting Services (SSRS) Report Builder (SQL307) Adrian Rupp Business Intelligence Solutions Specialist.
Web-based Portal for Discovery, Retrieval and Visualization of Earth Science Datasets in Grid Environment Zhenping (Jane) Liu.
Metadata: Its Functions in Knowledge Representation for Digital Collections 1 Summary.
Databases & Data Warehouses Chapter 3 Database Processing.
MS Access: Database Concepts Instructor: Vicki Weidler.
Advance Computer Programming Java Database Connectivity (JDBC) – In order to connect a Java application to a database, you need to use a JDBC driver. –
1 Access Lesson 3 Creating Queries Microsoft Office 2010 Introductory.
OpenMDR: Generating Semantically Annotated Grid Services Rakesh Dhaval Shannon Hastings.
OpenMDR: Alternative Methods for Generating Semantically Annotated Grid Services Rakesh Dhaval Shannon Hastings.
Best Practices for Data Warehousing. 2 Agenda – Best Practices for DW-BI Best Practices in Data Modeling Best Practices in ETL Best Practices in Reporting.
C Copyright © 2009, Oracle. All rights reserved. Appendix C: Service-Oriented Architectures.
The Integrated Data Repository (IDR): Ontology Mapping and Data Discovery for the Translational Investigator 1 Rob Wynden, BSCS, 1 Russ J. Cucina, MD,
Uniting i2b2.org and caGrid National scale data sharing networks for Biomedical Informatics research Rob Wynden – UCSF A collaborative effort of UCSF,
Health Ontology Mapper A project initiated within the CTSA (Clinical Translation Science Awards) program Goal: create a semantic interoperability layer.
Division of IT Convergence Engineering Towards Unified Management A Common Approach for Telecommunication and Enterprise Usage Sung-Su Kim, Jae Yoon Chung,
® IBM Software Group © 2007 IBM Corporation J2EE Web Component Introduction
Using the Open Metadata Registry (openMDR) to create Data Sharing Interfaces October 14 th, 2010 David Ervin & Rakesh Dhaval, Center for IT Innovations.
Chapter 6 SAS ® OLAP Cube Studio. Section 6.1 SAS OLAP Cube Studio Architecture.
Using SAS® Information Map Studio
Query Health Concept-to-Codes (C2C) SWG Meeting #12 March 6,
10/17/2012ISC471/HCI571 Isabelle Bichindaritz 1 Technologies Databases.
Nadir Saghar, Tony Pan, Ashish Sharma REST for Data Services.
Roberto Lucchi Esri INSPIRE Discovery, View and Download and OGC standards.
Shannon Hastings Multiscale Computing Laboratory Department of Biomedical Informatics.
LexBIG/LexGrid Services for LexBIG 2.3 Model and API for the Grid.
Health Ontology Mapper NCBO BioPortal Integration 2010 i2b2.org Academic User’s Group Oct Rob Wynden (UCSF)
INT-5: Integrate over the Web with OpenEdge® Web Services
Domain and Persistence Patterns. Fundamental Pattern Types Design Patterns Business Logic Patterns.
Knowledge Representation of Statistic Domain For CBR Application Supervisor : Dr. Aslina Saad Dr. Mashitoh Hashim PM Dr. Nor Hasbiah Ubaidullah.
Fanny Widadie, S.P, M.Agr 1 Database Management Systems.
In this session, you will learn to: Describe data redundancy Describe the first, second, and third normal forms Describe the Boyce-Codd Normal Form Appreciate.
User Profiling using Semantic Web Group members: Ashwin Somaiah Asha Stephen Charlie Sudharshan Reddy.
Microsoft FrontPage 2003 Illustrated Complete Integrating a Database with a Web Site.
Issues in Ontology-based Information integration By Zhan Cui, Dean Jones and Paul O’Brien.
Topic Maps introduction Peter-Paul Kruijsen CTO, Morpheus software ISOC seminar, april 5 th 2005.
1 Service Creation, Advertisement and Discovery Including caCORE SDK and ISO21090 William Stephens Operations Manager caGrid Knowledge Center February.
PART3 Data collection methodology and NM paradigms 1.
Implementation of a Relational Database as an Aid to Automatic Target Recognition Christopher C. Frost Computer Science Mentor: Steven Vanstone.
A Semantic Web Approach for the Third Provenance Challenge Tetherless World Rensselaer Polytechnic Institute James Michaelis, Li Ding,
Patterns in caBIG Baris E. Suzek 12/21/2009. What is a Pattern? Design pattern “A general reusable solution to a commonly occurring problem in software.
Towards Unifying Vector and Raster Data Models for Hybrid Spatial Regions Philip Dougherty.
Java Programming: Advanced Topics 1 Enterprise JavaBeans Chapter 14.
HTML Hyper Text Markup Language. The Basics u HTML documents contain “tags” which instruct the Browser software on how to present the information within.
Copyright 2007, Information Builders. Slide 1 iWay Web Services and WebFOCUS Consumption Michael Florkowski Information Builders.
1 4th of October, 2006 © ATHENA Consortium 2006 B5 EADS CCR piloting Nicolas Figay, EADS Flora Robin, EADS ATHENA Intermediate Review October 2006.
The ECOST Web-based platform for data providers and for data users.
HTML Hyper Text Markup Language. Agenda Basics Tools Important tags Tables & databases Forms Publishing at Stern.
Eurostat May 2016 Eurostat, Unit B3 – IT solutions for statistical production Test Client Jean-Francois LEBLANC Christian SEBASTIAN.
1 Copyright © 2008, Oracle. All rights reserved. Repository Basics.
Semantic Web. P2 Introduction Information management facilities not keeping pace with the capacity of our information storage. –Information Overload –haphazardly.
Geographic Information Systems
Metadata The metadata contains
Towards Unified Management
Presentation transcript:

I2b2 grid integration with Ontology Mapper A description of the 2 methods that are currently under development Just a Summary by Rob Wynden (UCSF)

There are 2 projects under way SHRINE – a Harvard based project to create a new grid platform (Andy McMurray and the Harvard team) caGRID – the existing caBIG grid initiative and related tools. (Shannon Hastings and the OSU team)

SHRINE SHRINE is a new layer of Cells which sit between the existing i2b2 Workbench and the existing i2b2 server. SHRINE queries are generated using i2b2 Workbench and since SHRINE emulates the SOAP(REST) interface to i2b2 the Workbench code is not even aware of the new SHRINE cells that sit between it and the server.

No Major Modifications Necessary Since SHRINE uses the existing i2b2 Workbench in an unmodified form it is possible to provide semantic interoperability for SHRINE using a single database view. The i2b2 workbench displays concepts from the Concept Dimension after filtering by the i2b2 table. The Ontology Mapper extends the Concept Dimension but does not alter any of the existing fields. Therefore i2b2 Workbench (and SHRINE) will work with Ontology Mapper without modification.

SHRINE Layered Integration The i2b2 Workbench (and SHRINE) query and display data from the Obs Fact Table. The Ontology Mapper adds 2 additional fact tables, the Mapped Data and Mapped Aggregate fact tables. Therefore to fully integrate Workbench to Ontology Mapper we have created a simple database View which is a UNION the 3 fact tables and which presents a table definition that is identical to the Obs Fact Table. The only mod to Workbench that is required is that Workbench needs to reference that Obs Fact View instead of directly accessing the Obs Fact Table.

The Old and New Database Access Methods

caGRID Integration SHRINE only supports distributed query. However caGRID is a general purpose distributed networking protocol. caGRID is MUCH more secure than SHRINE. The integration with i2b2 under way at OSU uses the OSU Introduce Tool.

Encoding Tables The Ontology Mapper extends the Concept Dimension in i2b2 by adding keys to a new table called Encoding. The Encoding table(s) store information about which Common Data Element (CDR) within the caDSR(ctsDSR,openMDR) each row in the Concept Dimension references. Therefore the Encoding Table makes it possible (optional) for concept paths to be correlated with specific CDR’s from formal ISO111-79 data models (the flattened form on a formal ontology).

Leveraging Introduce OSU has modified the caGRID Introduce tool OSU to allow Introduce to connect directly to the i2b2 database and view the Concept Dimension and the associated rows within the Encoding Table(s). Introduce therefore only reads data from the Mapped Data and Mapped Aggregate Fact tables as all information that it consumes MUST reference CDR’s. Introduce ONLY exposes semantically interoperable data: it does not allow exposure of data directly from the Obs Fact Table.

Introduce Integration

Conclusion and Note The Ontology Mapper extends i2b2 so that it can contain formally encoded information in a semantically interoperable way. Both of these projects leverage that functionality for grid based semantic interoperability. A 3rd method is possible where the SHRINE network protocol can be implemented as a caGRID web service type (just as cQL was). That may be the most platform independent solution but no one is working on that at present.