Computer Aided Investigation: Visualization and Analysis of data from Mobile communication devices using Formal Concept Analysis

TL;DR

This paper introduces a novel computer-aided investigation method that visualizes and analyzes mobile communication data using Formal Concept Analysis, aiding criminal investigations by revealing patterns and relationships.

Contribution

It proposes a new approach leveraging Formal Concept Analysis and Galois Lattices for visualizing and analyzing crime data from mobile devices, enhancing data interpretation.

Findings

Effective visualization of mobile communication data.

Improved analysis of crime patterns.

Enhanced conceptual understanding of suspect networks.

Abstract

In this world of terrorism, it is very important to know the network of individual suspects. It is also important to analyze the attributes of members of a network and the relationships that exist between them either directly or indirectly. This will make it easy for concepts to be built in aiding criminal investigations. However traditional approaches cannot be used to visualize and analyze data collected on individuals. With this current day where information systems play critical role in everyday life of every individual, it is easier to depend on digital information in fighting crime.Effective computer tools and intelligent systems that are automated to analyze and interpret criminal data in real time effectively and efficiently are needed in fighting crime. These current computer systems should have the capability of providing intelligence from raw data and creating a visual graph which will make it easy for new concepts to be built and generated from crime data in order to solve understand and analyze crime patterns easily. This paper proposes a new method of computer aided investigation by visualizing and analyzing data of mobile communication devices using Formal Concept Analysis, or Galois Lattices, a data analysis technique grounded on Lattice Theory and Propositional Calculus. This method considered the set of common and distinct attributes of data in such a way that categorizations are done based on related data with respect to time and events. This will help in building a more defined and conceptual systems for analysis of crime data that can easily be visualized and intelligently analyzed by computer systems.