The document introduces DEX, a graph database from Sparsity Technologies that is optimized for applications involving highly connected data with complex relationships. It can handle large volumes of data efficiently and features an attributed, directed multigraph data model. Examples of successful uses of DEX include fraud detection in financial transactions by analyzing networks of people and entities, concept mapping to aid brainstorming, and clinical analysis of patient histories to evaluate cancer treatment procedures. DEX provides APIs for Java, .NET and C++ and can integrate data from sources like WordNet and ConceptNet.

1. Graph Database
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DEX Graph Database
http://www.sparsity-technologies.com

2. Index
 Introduction
 DEX Graph Database
 Successful stories
 Technology
DEX Graph Database
http://www.sparsity-technologies.com

3. Index
 Introduction
 DEX Graph Database
 Successful stories
 Technology
DEX Graph Database
http://www.sparsity-technologies.com

4. Introduction
Data tendency:
 Higher connectivity degree
 More complex data models
 Data generation decentralization
DEX Graph Database
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5. Introduction
Classic relational model
Apparently inefficient
for complex data
model or flexible
schemas
Inefficient for
DEX Graph Database
structural queries
Intensive use of joins
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6. Index
 Introduction
 DEX Graph Database
 Successful stories
 Technology
DEX Graph Database
http://www.sparsity-technologies.com

7. DEX Graph Database
 Graph databases focus on the structure of the model.
 Implicit relation in the model
 DEX is a programming library that allows data stored in
a network or graph.
 Big volumes
 High performance
DEX Graph Database
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8. Introduction
 Applications
 Network analysis
 Pattern recognition
 Data sources integration
 Scenarios
 Social Networks
 MySpace, Facebook, …
 Information Networks
 Bibliographical databases, Wikipedia, …
 Physical Networks
DEX Graph Database
 transport, electrical, …
 Biological Networks
 protein integration, …
Scenarios where relationships are relevant
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9. Index
 Introduction
 DEX Graph Database
 Successful stories
 Technology
DEX Graph Database
http://www.sparsity-technologies.com

10. Successful stories:
Fraud detection
Who? Fraud Prevention Organ
What? Fraud detection in patrimonial transactions
How? Detect fraud patterns. A transaction might be a
potential fraud by contrasting it to before-hand known
patterns.
• Network of people, entities, properties and its
DEX Graph Database
relationships (mortgages, ..) extracted from the
registered transactions
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11. Successful stories:
Advertising Agency
Who? An advertising agency
What? Tool to identify new concepts during a
brainstorming for an advertising agency
How? Find related concepts (clusters) from a group of
given words.
• Semantic network of concepts and words, and its
relationships
DEX Graph Database
• Integration of two public databases:
• WordNet: definitions, dictionaries
• ConceptNet: relationships between concepts
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12. Successful stories:
Oncology analysis
Who? An Oncology Institute
What? Objective evaluation tool to analyze the
procedures applied to cancer patients
How? Helping in the diagnosis of the different typologies
of tumors by integrating the history of every patient
 Visual exploration tool
DEX Graph Database
 Patients, pathologies, diagnosis, procedures and
hospital admissions network
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13. Index
 Introduction
 DEX Graph Database
 Successful stories
 Technology
DEX Graph Database
http://www.sparsity-technologies.com

14. Technology:
Requirements
 APIs:
 Java
 .Net
 C++
Java public library(1.5 or superior)
 High-performance native library
DEX Graph Database
 OS:
 Windows – 32 bits & 64 bits
 Linux – 32 & 64 bits
 MacOS – 32 & 64 bits
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15. Technology:
Data model
 Attributed directed labeled multigraph
 Nodes and edges belong to types
 Nodes and edges may have attributes
 Edges may be directed
DEX Graph Database
 Several edges between nodes (even from the same
type)
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16. Thanks for your
attention
Any questions?
Pere Baleta Ferrer Josep Lluís Larriba Pey
DEX Graph Database
CEO Founder
pbaleta@sparsity-technologies.com larri@sparsity-technologies.com
SPARSITY-TECHNOLOGIES
Jordi Girona, 1-3, Edifici K2M
08034 Barcelona
info@sparsity-technologies.com
http://www.sparsity-technologies.com
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