Knowledge Discovery in Database Systems

Abstract

At the current stage the technologies for generating and collecting data have been advancing rapidly.The main problem is the extraction of valuable and accurate information from large data sets.One of the main techniques for solving this problem is Data Mining.Data mining (DM) is the process of identification and extraction of useful information in typically large databases.DM aims to automatically discover the knowledge that is not easily perceivable.It uses statistical analysis and artificial intelligence (AI) techniques together to address the issues.There are different types of tasks associated to data mining process.Each task can be thought of as a particular kind of problem to be solved by a data mining algorithm.The main types of tasks performed by DM algorithms are: Classification, association, clustering, regression, anomaly detection, feature extraction, time series analyses.In this paper we will perform a survey of the techniques above.A secondary goal of our paper is to give an overview of how DM is integrated in Business Intelligence (BI) systems.BI refers to a set of tools used for multidimensional data analysis, with the main purpose to facilitate decision making.One of the main components of BI systems is OLAP.The main OLAP component is the data cube which is a multidimensional database model that with various techniques has accomplished an incredible speed-up of analyzing and processing large data sets.We will discuss the advantages of integrating DM tools in BI systems.