From Relational to Semantics A Methodology Arka Mukherjee, Ph.D. Founder / CTO Global IDs David Schaengold Director, Business Solutions Revelytix

© 2012 Global IDs 2 Proprietary

© 2012 Global IDs 3 Proprietary Objectives 1.The Bridge from Relational to Semantics 2.An Implementation Methodology 3.Review a Use Case

Introduction

© 2012 Global IDs 5 Proprietary Evolution of Data Representation 1970’s Relational Models Relational Databases Optimized For: Speed Volume 2010’s Semantic Models Graph Databases Optimized For: Complexity Integration Improved Representations

© 2012 Global IDs 6 Proprietary Central Questions Can software help us migrate from a relational-centric world to a semantic-based world? Is there a clear path to providing better data integration and management of distributed data sets?

© 2012 Global IDs 7 Proprietary Partner Organizations Focus: - Data Profiling - Data Mapping - Enterprise Scale Focus: - Distributed Information Management - Data Virtualization - Data Federation

© 2012 Global IDs 8 Proprietary Focus Relational To RDF Mapping, Federation and Analytics

Methodology

© 2012 Global IDs 10 Proprietary Prototypical Customer Pain Points: Little Transparency or Traceability Limited Analytical Capability What is needed: Quicker Time-to-Market Reduced Cost of Integration More Comprehensive Analytics Access to More Data Too Many Data Silos High Costs of Maintenance

© 2012 Global IDs 11 Proprietary 2 Improve Quality Methodology 3 Generate Portals 1 Create Transparency 5 Integrate / Federate 6 Emergent Analytics 4 Map to Ontology 6 Stages

© 2012 Global IDs 12 Proprietary Stage 1 : Create Transparency 1.Scan 2.Analyze 3.Organize 4.Create Maps 1.Convert Maps to Ontologies

© 2012 Global IDs 13 Proprietary Stage 2 : Improve Quality Data VerificationData Validation Data StewardshipData Monitoring

© 2012 Global IDs 14 Proprietary Stage 3 : Generate Portals For Any Data Type: Customers Products Employees Suppliers Vendors Partners SKUs Sensors

© 2012 Global IDs 15 Proprietary Stage 4 : Map to Ontology Map to: Company Ontology Industry Ontology Domain Ontology An ontology is synonymous with a Business Vocabulary

© 2012 Global IDs 16 Proprietary Stage 5 : Integrate / Federate Populate Semantic Database Deploy SPARQL Query Processor Integrate Data, Enabling Federated Queries Over Distributed Data Stores

© 2012 Global IDs 17 Proprietary Stage 6 : Emergent Analytics Rapid Analytics Query any SPARQL End Point, Internally or Externally

© 2012 Global IDs 18 Proprietary Result: Lower Costs + Higher Automation

Financial Services Demonstration

© 2012 Global IDs 20 Proprietary Architecture 1. Scanning & Profiling 2. Export Mappings 3. Create Queries over Federated Sources

© 2012 Global IDs 21 Proprietary Starting Point How is Data Defined? Where is Data Located?

© 2012 Global IDs 22 Proprietary Global IDs provides automated data discovery at enterprise scale

© 2012 Global IDs 23 Proprietary Revelytix provides enterprise data management solutions using W3C semantic standards A Distributed Information Management System is a layer above your current DBMS, similar to how a RDBMS is a layer above a file system Both provide an additional level of abstraction Both bundle new computational capabilities into the system Both simplify the access to and use of data by applications and developers

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