Query Driven Data Subspace Mapping

Abstract

The increased use of multiple types of computer systems and smart devices in several areas, has created massive amounts of data. Concurrently, the need for a subset of these data by numerous applications and users for task execution and knowledge extraction, has resulted in the injection of a massive number of queries per second into distributed database servers. As a result of this phenomena, a major process is the efficient response of these queries both in terms of time and the detection of acceptable data, while rejecting undesired data points. In this paper, we present a hierarchical query-driven clustering approach, for performing efficient data mapping in remote databases for future incoming queries. We distinguish ourselves from current methods, by combining the technique of Query-Based Learning (QBL) with a hierarchical clustering of multiple forms of clustering in the same model. The suggested model's performance is assessed, using a number of experimental scenarios as well as numerical data.