From Relational to Semantics

A Methodology

www.globalids.com

Arka Mukherjee, Ph.D.Founder / CTOGlobal IDs

www.revelytix.com

David SchaengoldDirector, Business SolutionsRevelytix

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Objectives

1. The Bridge from Relational to Semantics

2. An Implementation Methodology

3. Review a Use Case

Introduction

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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

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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?

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Partner Organizations

Focus:

- Data Profiling

- Data Mapping

- Enterprise Scale

Focus:

- Distributed Information Management

- Data Virtualization

- Data Federation

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FocusRelational To RDF Mapping, Federation and Analytics

Methodology

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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

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2

Improve Quality

Methodology

3

Generate Portals

1

Create Transparency

5

Integrate / Federate

6

Emergent Analytics

4

Map to Ontology

6 Stages

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Stage 1 : Create Transparency

1. Scan

2. Analyze

3. Organize

4. Create Maps

1. Convert Maps to Ontologies

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Stage 2 : Improve Quality

Data Verification Data Validation

Data Stewardship Data Monitoring

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Stage 3 : Generate Portals

For Any Data Type:

• Customers• Products• Employees• Suppliers• Vendors• Partners• SKUs• Sensors

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Stage 4 : Map to Ontology

Map to:

• Company Ontology• Industry Ontology• Domain Ontology

An ontology is synonymous with a Business Vocabulary

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Stage 5 : Integrate / Federate

• Populate Semantic Database

• Deploy SPARQL Query Processor

• Integrate Data, Enabling Federated Queries Over Distributed Data Stores

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Stage 6 : Emergent Analytics

• Rapid Analytics

• Query any SPARQL End Point, Internally or Externally

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Result: Lower Costs + Higher Automation

Financial Services Demonstration

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Architecture

1. Scanning & Profiling

2. Export Mappings

3. Create Queries over Federated Sources

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Starting Point

How is Data Defined?Where is Data Located?

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Global IDs provides automated data discovery at enterprise scale

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Revelytix provides enterprise datamanagement 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

Thank You

For more Information, please contact

Arka.Mukherjee@globalids.comDavid.Schaengold@revelytix.com

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