Towards On-Line Analytical Mining in Large Databases

Research output: Contribution to journalArticlepeer-review

Abstract

Great efforts have been paid in the Intelligent Database Systems Research Lab for the research and development of efficient data mining methods and construction of on-line analytical data mining systems. Our work has been focused on the integration of data mining and OLAP technologies and the development of scalable, integrated, and multiple data mining functions. A data mining system, DBMiner, has been developed for interactive mining of multiple-level knowledge in large relational databases and data warehouses. The system implements a wide spectrum of data mining functions, including characterization, comparison, association, classification, prediction, and clustering. It also builds up a user-friendly, interactive data mining environment and a set of knowledge visualization tools. In-depth research has been performed on the efficiency and scalability of data mining methods. Moreover, the research has been extended to spatial data mining, multimedia data mining, text mining, and Web mining with several new data mining system prototypes constructed or under construction, including GeoMiner, MultiMediaMiner, and WebLogMiner. This article summarizes our research and development activities in the last several years and shares our experiences and lessons with the readers.

Original languageEnglish (US)
Pages (from-to)97-107
Number of pages11
JournalSIGMOD Record (ACM Special Interest Group on Management of Data)
Volume27
Issue number1
DOIs
StatePublished - Mar 1998
Externally publishedYes

ASJC Scopus subject areas

  • Software
  • Information Systems

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