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Table of contents:

Data Fusion

• Introduction
• The Technical Challenges
• Greater Volumes of Data
• Information Sharing
• CASE STUDY 1
• CASE STUDY 2
• CASE STUDY 3
• CASE STUDY 4
• CASE STUDY 5
• CASE STUDY 6
• Objectivity Advantages
• Trees and Networks of Objects
• Navigation & Inheritance
• Clustering and In-Memory Caching
• Concurrently Accessed Objects
• Distributed Architecture
• Data Scalability
• Continuous Availability
• Objectivity/DB
• Progran Control Challenges
• Lower Cost of Ownership
• Summary

Data Fusion - integrating complex data from multiple sources

Introduction

This White Paper discusses practical ways of deploying Objectivity/DB as a hub for integrating data from multiple sources. There are many approaches to consolidating multisource data. Data warehouses are popular in traditional information technology departments. Data fusion is a difficult problem confronting users of geospatial and sensor data. Users of criminal investigation or intelligence data need to store and correlate vast amounts of disparate information, ranging from text, images, video and voice to various kinds of specialized intelligence data (such as SIGINT, ELINT, RADINT, TELINT and COMINT).

This White Paper examines the technical challenges faced by designers of advanced data integration systems. It contains technical case studies describing how to use Objectivity/DB to overcome these challenges. It then briefly outlines the pertinent features of Objectivity/DB and outlines material that you can use to evaluate the suitability of Objectivity/DB for your own project.