Mining and modeling database user access patterns

Authors:
Qingsong Yao;Aijun An;Xiangji Huang
Affiliations:
Department of Computer Science and Engineering, York University, Toronto, Canada;Department of Computer Science and Engineering, York University, Toronto, Canada;Department of Computer Science and Engineering, York University, Toronto, Canada
Venue:
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Year:
2006

Citing 10
Cited 2

On Workload Characterization of Relational Database Environments

IEEE Transactions on Software Engineering
Statistical methods for speech recognition

Statistical methods for speech recognition
Characterization of database access pattern for analytic prediction of buffer hit probability

The VLDB Journal — The International Journal on Very Large Data Bases
Performance Analysis of Affinity Clustering on Transaction Processing Coupling Architecture

IEEE Transactions on Knowledge and Data Engineering
PROMISE: Predicting Query Behavior to Enable Predictive Caching Strategies for OLAP Systems

DaWaK 2000 Proceedings of the Second International Conference on Data Warehousing and Knowledge Discovery
Characteristics of production database workloads and the TPC benchmarks

IBM Systems Journal - End-to-end security
Applying language modeling to session identification from database trace logs

Knowledge and Information Systems
Primitives for workload summarization and implications for SQL

VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
A machine learning approach to identifying database sessions using unlabeled data

DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
A distance-based algorithm for clustering database user sessions

ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems

Towards workflow-driven database system workload modeling

Proceedings of the Second International Workshop on Testing Database Systems
On predictive modeling for optimizing transaction execution in parallel OLTP systems

Proceedings of the VLDB Endowment

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present our approach to mining and modeling the behavior of database 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 behaviors successfully.