Extracting Hidden Information and Conclusions in Software Testing Via Distributed Relational Visual Mining

Abstract

Visual Mining is typically concerned with the visualization of data and its representation to facilitate the mining aiming at extracting interesting and hidden information. It can also mean the visualization of the results of the mining process with the purpose of deepening the understanding of such results and maximizing its exploitation. However, in global systems and global economies, the targeted knowledge of interest may not be embedded in one database or data system. Instead, it may be hidden, not in the data sets, but in the relations between seemingly unrelated data systems. We introduce this problem and the concept of Distributed Relational Visual Mining and its potential for information and knowledge discovery from distributed seemingly disconnected systems. With potential applications in many areas, we introduce a case study applying the proposed technique in the area of Software Development in general and Software testing in Particular.