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Mining and Modeling Database User Access Patterns Qingsong Yao, Aijun An, and Xiangji Huang

Summary: Mining and Modeling Database User Access Patterns
Qingsong Yao, Aijun An, and Xiangji Huang
Department of Computer Science and Engineering, York University, Toronto, M3J 1P3 ,Canada
{qingsong, aan}@cs.yorku.ca, jhuang@yorku.ca
Abstract. We present our approach to mining and modeling the behavior of data-
base users. In particular, we propose graphic models to capture the database user's
dynamic behavior and focus on applying data mining techniques to the problem
of mining and modeling database user behaviors from database trace logs. The
experimental results show that our approach can discover and model user behav-
iors successfully.
1 Introduction
Workload analysis has played an important role in optimizing the performance of data-
base systems. While most work on database workload analysis focuses on providing sta-
tistical summaries and run-time behavior on the physical resource level of the database
system, it has been brought into attention that analysis of task-oriented user sessions
provides useful insight into the query behavior of the database users [7,8]. A session
is a sequence of queries issued by a user (or an application) to achieve a certain task.
It consists of one or more database transactions, which are in turn a sequence of oper-
ations performed as a logical unit of work. Analysis of sessions allows us to discover
high-level patterns that stem from the structure of the task the user is solving.


Source: An, Aijun - Department of Computer Science, York University (Toronto)


Collections: Computer Technologies and Information Sciences