Memory system characterization of commercial workloads
Proceedings of the 25th annual international symposium on Computer architecture
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
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
DBMSs on a Modern Processor: Where Does Time Go?
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Automated Selection of Materialized Views and Indexes in SQL Databases
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
LEO - DB2's LEarning Optimizer
Proceedings of the 27th International Conference on Very Large Data Bases
SPRINT: A Scalable Parallel Classifier for Data Mining
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Automating Statistics Management for Query Optimizers
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
DB2 Advisor: An Optimizer Smart Enough to Recommend its own Indexes
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
The dawning of the autonomic computing era
IBM Systems Journal
Characteristics of production database workloads and the TPC benchmarks
IBM Systems Journal - End-to-end security
Towards workload-aware dbmss: identifying workload type and predicting its change
Towards workload-aware dbmss: identifying workload type and predicting its change
The Psychic-Skeptic Prediction framework for effective monitoring of DBMS workloads
Data & Knowledge Engineering
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The type of the workload on a database management system (DBMS) is a key consideration in tuning the system. Allocations for resources such as main memory can be very different depending on whether the workload type is Online Transaction Processing (OLTP) or Decision Support System (DSS). A DBMS also typically experiences changes in the type of workload it handles during its normal processing cycle. Database administrators must therefore recognize the significant shifts of workload type that demand reconfiguring the system in order to maintain acceptable levels of performance. We envision intelligent, autonomic DBMSs that have the capability to manage their own performance by automatically recognizing the workload type and then reconfiguring their resources accordingly. In this paper, we present an approach to automatically identifying a DBMS workload as either OLTP or DSS. Using data mining techniques, we build a classification model based on the most significant workload characteristics that differentiate OLTP from DSS and then use the model to identify any change in the workload type. We construct and compare classifiers built from two different sets of workloads, namely the TPC-C and TPC-H benchmarks and the Browsing and Ordering profiles from the TPC-W benchmark. We demonstrate the feasibility and success of these classifiers with TPC-generated workloads and with industry-supplied workloads.