Scientific and Statistical Database Management, International Conference on (2007)
Banff, Alberta, Canada
July 9, 2007 to July 11, 2007
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/SSDBM.2007.17
Gisele Busichia Baioco , University of Sao Paulo at S. Carlos, Brazil
Agma J. M. Traina , University of Sao Paulo at S. Carlos, Brazil
Caetano Traina Jr. , University of Sao Paulo at S. Carlos, Brazil
This paper presents an effective cost model to estimate the number of disk accesses (I/O cost) and the number of distance calculations (CPU cost) to process similarity queries over data indexed by metric access methods. Two types of similarity queries were taken into consideration: range and k-nearest neighbor queries. The main point of the cost model is considering not only global parameters of the data set but also the local data distribution. The model takes advantage of the intrinsic dimension of the data set, estimated by its correlation fractal dimension. Experiments were performed on real and synthetic data sets, with different sizes and dimensions, in order to validate the proposed model. They confirmed that the estimations are accurate, within the range achieved by real queries.
C. Traina Jr., G. B. Baioco and A. J. Traina, "MAMCost: Global and Local Estimates leading to Robust Cost Estimation of Similarity Queries," 2007 International Conference on Scientific and Statistical Database Management(SSDBM), Banff, Alta., 2007, pp. 6.