Data Mining and Knowledge Engineering

M. Saravanamuthu ¹, G. M. Kadhar Nawaz ²

Affiliations
1 Anna University of Technology, Coimbatore, IN
2 Department of MCA, Sona College of Technology, Salem, IN

Abstract

The significance of statistics plays a major role in society, business and government. In commercial applications, software tools are effectively applying the statistical techniques in order to improve the performance as well as to minimize the cost by drawing timely decisions. This paper focuses the significances of statistics in three main systems namely decision support system, data mining system and data warehousing system. This paper prominently contributes the roles of statistical techniques in DSS, DM and DW systems.

Keywords

Decision Support Systems, Data Mining, Data Warehousing, Business Intelligence, Data Cube, OLAP, MDDB.

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M. Saravanamuthu ¹, G. M. Kadhar Nawaz ²

Affiliations
1 Department of Master of Computer Application, Sree Saraswathi Thyagaraja College, Pollachi, IN
2 Department of Master of Computer Application, Sona College of Technology, Salem, IN

Abstract

The proposed EasyMiner (version 2) will extract knowledge from the data warehouse. The traditional data mining query language (DMQL) requires (a) the set of task-relevant data to be mined, (b) the kind of knowledge to be mined, (c) the background knowledge to be used in the discovery process, (d) the interestingness measures and thresholds for pattern evaluation and (e) the expected representation for visualizing the discovered patterns. These lengthy processes should be minimized so that the query processing time could be reduced. The ultimate goal is to simplify the data mining process by using a simple DMQL through EasyMiner. Normally, DMQL adopt several methodologies at each stage of knowledge mining. But the new DMQL propose some sort of eliminated or updated or simplified model of these methodologies namely concept hierarchy, association rules, classification, data cube, etc., Here the supervised learning ie., classification techniques are used. The user will realize the simplicity of the data mining process through EasyMiner. The outcome of the proposed EasyMiner is that expected knowledge from given data warehouse by saving more amount of query processing time. Further, the level of mining the knowledge might be indicated. The multi-dimensional database could be controlled to 3Ds only in order to maintain the simplification process of the EasyMiner. At last, the EasyMiner will extract the required and/or expected knowledge from the data warehouse by using simplified query processing models.

Keywords

Data Mart, Data Mining, Data Warehouse, Query Processing.

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