Dex: Introduction

Graph Database info@sparsity-technologies.comDEX Graph Database http://www.sparsity-technologies.com

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

Introduction Data tendency:  Higher connectivity degree  More complex data models  Data generation decentralizationDEX Graph Database http://www.sparsity-technologies.com

Introduction Classic relational model Apparently inefficient for complex data model or flexible schemas Inefficient forDEX Graph Database structural queries Intensive use of joins http://www.sparsity-technologies.com

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 performanceDEX Graph Database http://www.sparsity-technologies.com

Introduction  Applications  Network analysis  Pattern recognition  Data sources integration  Scenarios  Social Networks  MySpace, Facebook, …  Information Networks  Bibliographical databases, Wikipedia, …  Physical NetworksDEX Graph Database  transport, electrical, …  Biological Networks  protein integration, … Scenarios where relationships are relevant http://www.sparsity-technologies.com

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 itsDEX Graph Database relationships (mortgages, ..) extracted from the registered transactions http://www.sparsity-technologies.com

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 relationshipsDEX Graph Database • Integration of two public databases: • WordNet: definitions, dictionaries • ConceptNet: relationships between concepts http://www.sparsity-technologies.com

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 toolDEX Graph Database  Patients, pathologies, diagnosis, procedures and hospital admissions network http://www.sparsity-technologies.com

Technology: Requirements  APIs:  Java  .Net  C++ Java public library(1.5 or superior)  High-performance native libraryDEX Graph Database  OS:  Windows – 32 bits & 64 bits  Linux – 32 & 64 bits  MacOS – 32 & 64 bits http://www.sparsity-technologies.com

Technology: Data model  Attributed directed labeled multigraph  Nodes and edges belong to types  Nodes and edges may have attributes  Edges may be directedDEX Graph Database  Several edges between nodes (even from the same type) http://www.sparsity-technologies.com

Thanks for your attention Any questions? Pere Baleta Ferrer Josep Lluís Larriba PeyDEX 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 http://www.sparsity-technologies.com

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Dex: Introduction

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