Management of Data Streams for Large-Scale Data Mining

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Author(s): Jon R. Wright (AT&T Labs - Research, USA), Gregg T. Vesonder (AT&T Labs - Research, USA)and Tamraparni Dasu (AT&T Labs - Research, USA)
Copyright: 2008
Pages: 15
Source title: Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-59904-951-9.ch163

Keywords: Data Mining and Databases / Data Warehousing / Information Science Reference / Library & Information Science

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Abstract

In an enterprise setting, a major challenge for any data mining operation is managing data streams or feeds, both data and metadata, to ensure a stable and certifiably accurate flow of data. Data feeds in this environment can be complex, numerous and opaque. The management of frequently changing data and metadata presents a considerable challenge. In this paper, we articulate the technical issues involved in the task of managing enterprise data and propose a multi-disciplinary solution, derived from fields such as knowledge engineering and statistics, to understand, standardize, and automate information acquisition and quality management in preparation for enterprise mining.

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