论文信息 - The Drill Down Benchmark

The Drill Down Benchmark

Data Mining places specific requirements on DBMS query performance that cannot be evaluated satisfactorily using existing OLAP benchmarks. The DD Benchmark - defined here - provides a practical case and yardstick to explore how well a DBMS is able to support Data Mining applications. It was derived from real-life data mining tasks performed by our Data SurveyorTM tool running on a variety of DBMS backends. We describe initial results obtained using both the Monet system and a relational DBMS product as backend.

[1] Martin L. Kersten,et al. Flattening an object algebra to provide performance , 1998, Proceedings 14th International Conference on Data Engineering.

[2] Rakesh Agarwal,et al. Fast Algorithms for Mining Association Rules , 1994, VLDB 1994.

[3] Jim Gray,et al. Benchmark Handbook: For Database and Transaction Processing Systems , 1992 .

[4] Jeffrey F. Naughton,et al. Cache Conscious Algorithms for Relational Query Processing , 1994, VLDB.

[5] Marcel Holsheimer,et al. Data Surveyor: Searching the Nuggets in Parallel , 1996, Advances in Knowledge Discovery and Data Mining.

[6] Roderic G. G. Cattell. The benchmark handbook for database and transaction processing systems , 1991 .

[7] Hector Garcia-Molina,et al. Main Memory Database Systems: An Overview , 1992, IEEE Trans. Knowl. Data Eng..

[8] Ramakrishnan Srikant,et al. Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.