A Machine Learning Approach to Identifying Database Sessions Using Unlabeled Data

In this paper, we describe a novel co-training based algorithm for identifying database user sessions from database traces. The algorithm learns to identify positive data (session boundaries) and negative data (non-session boundaries) incrementally by using two methods interactively in several itera...

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Bibliographic Details
Published inData Warehousing and Knowledge Discovery pp. 254 - 264
Main Authors Yao, Qingsong, Huang, Xiangji, An, Aijun
Format Book Chapter Conference Proceeding
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2005
Springer
SeriesLecture Notes in Computer Science
Subjects
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Exact sciences and technology
Information systems. Data bases
Label Data
Machine Learn Approach
Memory organisation. Data processing
Negative Data
Software
Training Step
Unlabeled Data
Data analysis
Database
Knowledge discovery
Iterative method
Data mining
Artificial intelligence
Modeling
Data warehouse
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Summary:In this paper, we describe a novel co-training based algorithm for identifying database user sessions from database traces. The algorithm learns to identify positive data (session boundaries) and negative data (non-session boundaries) incrementally by using two methods interactively in several iterations. In each iteration, previous identified positive and negative data are used to build better models, which in turn can label some new data and improve performance of further iterations. We also present experimental results.
ISBN:354028558X
9783540285588
ISSN:0302-9743
1611-3349
DOI:10.1007/11546849_25