2016 International Conference on Big Data and Smart Computing (BigComp) (2016)
Hong Kong, China
Jan. 18, 2016 to Jan. 20, 2016
Sung-Hwan Kim , Dept. of Electrical and Computer Engineering, Pusan National University, South Korea
Da-Young Lee , Dept. of Electrical and Computer Engineering, Pusan National University, South Korea
Hwan-Gue Cho , Dept. of Electrical and Computer Engineering, Pusan National University, South Korea
Utilizing pivot spaces is a popular method to perform similarity search queries in metric spaces when it is difficult to search the objects with their plain representation. In the pivot space, an object is transformed into a vector whose coordinates are its distances to pre-defined pivots. Based on this transform, any distance-based and multi-dimensional data structures can be used to perform various types of search queries. Although it has been observed that the search performance in terms of query throughput highly depends on which pivots are chosen, it still has been unclear how to choose good pivots despite of a number of work presented over decades. In this paper, we present a pivot selection strategy based on their correlation. By computing eigenvalues and manipulating them, independent pivots are chosen to improve the efficiency of the searching process in the pivot space. Experimental results show that selecting uncorrelated pivots improves the performance, and outperforms other previous pivot selection approaches.
Search problems, Extraterrestrial measurements, Transforms, Data structures, Indexing
Sung-Hwan Kim, Da-Young Lee and Hwan-Gue Cho, "An eigenvalue-based pivot selection strategy for improving search efficiency in metric spaces," 2016 International Conference on Big Data and Smart Computing (BigComp)(BIGCOMP), Hong Kong, China, 2016, pp. 207-214.