Data mining techniques have been applied in decision support systems in order to detect patterns and to mine knowledge from large datasets. These mining techniques can be used together with OLAP to analyze large datasets which can make Online Analytical Processing (OLAP) more useful and easier to apply in decision support systems. Several works in the past proved the likelihood and interest of integrating OLAP with data mining and as a result a new promising direction of Online Analytical Mining (OLAM) has emerged. In this ...
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Data mining techniques have been applied in decision support systems in order to detect patterns and to mine knowledge from large datasets. These mining techniques can be used together with OLAP to analyze large datasets which can make Online Analytical Processing (OLAP) more useful and easier to apply in decision support systems. Several works in the past proved the likelihood and interest of integrating OLAP with data mining and as a result a new promising direction of Online Analytical Mining (OLAM) has emerged. In this book, a variety of OLAM architectures in the literature were reviewed and the limitations in the previously reported work have been identified. Literature review reveals the fact that none of the previously reported OLAM architectures have integrated enhanced OLAP with data mining. We enhanced the performance of OLAP in terms of cube construction time and visualization by providing interactive visual exploration of data cube. The aim of this book is to propose an integrated OLAM architecture that not only overcomes the existing limitations but also extends the architecture by adding an automation layer for OLAP schema generation.
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