Data Mining and Business Intelligence

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Business Objects
DB2

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Open Source BI: The Great Leap Forward
Ken North (SQLSummit.com)

This article discusses a survey of open source business intelligence (BI) software deployments and planning. It explores trends, LAMP, Eclipse and available open source BI software.

The Link is The Thing
Richard Hackathorn, PhD (Bolder Technology)

This is a PDF version of Dick Hackathorn's 3-part series for DM Review about Associate Link Analysis. Part I introduces network analysis of complex systems and part II applies the concepts to business intelligence and data warehousing. Part III discusses metrics, strategies and implementation issues.

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Business Intelligence from Web Usage Mining
Ajith Abraham (Oklahoma State)

This paper explains several approaches to Web usage data mining. It also introduces an approach called intelligent miner (i-Miner) for optimizing the architecture of a fuzzy clustering algorithm (for discovering web data clusters) and fuzzy inference systems (for analyzing visitor trends). The author explains several approaches to analyzing web logs and identifying data clusters. He introduces an approach based on a  Takagi-Sugeno fuzzy inference system using a neural net learning and an evolutionary algorithm to analyze clustered data.

Farming Web Resources for the Data Warehouse
Richard Hackathorn, PhD (Bolder Technology)

The author explains Web farming, a discipline that combines aspects of data warehousing, web data mining, and knowledge base creation. This article explores how to refine data collected from the web. The author also explains how to create dimension tables, fact tables and star schemas for merging the data into a data warehouse.

Parallel SQL Based Association Rule Mining on Large Scale PC Cluster: Performance Comparison with Directly Coded C Implementation
Iko Pramudiono, Takahiko Shintani, Takayuki Tamura, Masaru Kitsuregawa

Abstract: Data mining is becoming increasingly important since the size of databases grows even larger and the need to explore hidden rules from the databases becomes widely recognized. Currently database systems are dominated by relational database and the ability to perform data mining using standard SQL queries will definitely ease implementation of data mining. However the performance of SQL based data mining is known to fall behind specialized implementation. In this paper we present an evaluation of parallel SQL based data mining on large scale PC cluster (). 

SQL Based Association Rule Mining using Commercial RDBMS (IBM DB2 UDB EEE)
Takeshi Yoshizawa, Iko Pramudiono, Masaru Kitsuregawa

Abstract: In we present an evaluation of SQL based data mining on commercial RDBMS (IBM DB2 UDB EEE). We examine some techniques to reduce I/O cost by using View and Subquery. Those queries can be more than 6 times faster than SETM SQL query reported previously. In addition, we have made performance evaluation on parallel database environment and compared the performance result with commercial data mining tool (IBM Intelligent Miner).

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