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Applying Language Modeling to Session Identification from Database Trace Logs
| Citations: | 4 - 3 self |
BibTeX
@MISC{Yao_applyinglanguage,
author = {Qingsong Yao and Xiangji Huang and Aijun An},
title = {Applying Language Modeling to Session Identification from Database Trace Logs},
year = {}
}
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Abstract
A database session is a sequence of requests presented to the database system by a user or an application to achieve a certain task. Session identification is an important step in discovering useful patterns from database trace logs. The discovered patterns can be used to improve the performance of database systems by prefetching predicted queries, rewriting the current query or conducting effective cache replacement. In this paper, we present an application of a new session identification method based on statistical language modeling to database trace logs. Several problems of the language modeling based method are revealed in the application, which include how to select values for the parameters of the language model, how to evaluate the accuracy of the session identification result and how to learn a language model without well-separated training data. All of these issues are important in the successful application of the language modeling based method for session identification. We propose solutions to these open issues. In particular, new methods for determining an entropy threshold and the order of the language model are proposed. New performance measures are presented to better evaluate the accuracy of the identified sessions. Furthermore, three types of learning methods, namely, supervised, semi-supervised and unsupervised learning, are introduced to learn
Keyphrases
session identification database trace log language model language modeling database system important step unsupervised learning new method database session new session identification method trace log well-separated training data new performance measure several problem useful pattern effective cache replacement session identification result current query discovered pattern successful application predicted query entropy threshold open issue certain task statistical language identified session
