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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

Greater Volumes of Data

The increased power, capabilities and adoption rates of computers and communications systems are generating exponentially increasing amounts of data. Any attempt to centralize all of this data in a single database repository is doomed to failure. Published statistics on relational database usage show that the largest current databases based on that technology are around fifty to sixty Terabytes in size. Recent hardware storage architectures are designed to accommodate Petabytes and even Exabytes of data files, so the physical storage isn’t a problem (other than its cost, perhaps).

The problem is that maintaining central server databases becomes very difficult as the number of data types and data instances grows. Discovering and defining new relationships between the data can significantly increase this complexity, to the point where relational indices and JOIN tables add to the problem rather than offering a simple solution¹.