Information Analysis and Knowledge Gain within Graph Data Model

Abstract

The information gathered within most databases is dedicated to have some interconnections. Part of these connections is described explicitly, like references or foreign keys in relational database model. Others remain implicit and should be discovered by a deeper analysis of relations in data. These hidden relations give a more complex and complete understanding of stored data or imply to be the required knowledge gained from a stored data. Considering data set with explicit references allows us to build a graph data model and apply approaches of graph theory to search for implicit ties. The particular example of an airspace network was investigated and an artificial locust swarm routing algorithm was implemented as an approach to search for a set of paths between airports. The attention of the paper is focused on meta-heuristic approach to reveal the significant subset of implicit relations as a part of knowledge gain procedure.