Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Automatically classifying database workloads
Proceedings of the eleventh international conference on Information and knowledge management
Streaming-Data Algorithms for High-Quality Clustering
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Density-based clustering for real-time stream data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
System models for goal-driven self-management in autonomic databases
Data & Knowledge Engineering
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An important goal of self-managing databases is the autonomic adaptation of the database configuration to evolving workloads. However, the diversity of SQL statements in real-world workloads typically causes the required analysis overhead to be prohibitive for a continuous workload analysis. The workload classification presented in this paper reduces the workload analysis overhead by grouping similar workload events into classes. Our approach employs clustering techniques based upon a general distance function for DBS workload events. To be applicable for a continuous workload analysis, our workload classification specifically addresses a stream-based, lightweight operation, a controllable loss of quality, and self-management.