Redondo Beach, California
Nov. 12, 2000 to Nov. 14, 2000
P. Ferragina , Dipt. di Inf., Pisa Univ., Italy
G. Manzini , Dipt. di Inf., Pisa Univ., Italy
We address the issue of compressing and indexing data. We devise a data structure whose space occupancy is a function of the entropy of the underlying data set. We call the data structure opportunistic since its space occupancy is decreased when the input is compressible and this space reduction is achieved at no significant slowdown in the query performance. More precisely, its space occupancy is optimal in an information-content sense because text T[1,u] is stored using O(H/sub k/(T))+o(1) bits per input symbol in the worst case, where H/sub k/(T) is the kth order empirical entropy of T (the bound holds for any fixed k). Given an arbitrary string P[1,p], the opportunistic data structure allows to search for the occurrences of P in T in O(p+occlog/sup /spl epsiv//u) time (for any fixed /spl epsiv/<0). If data are uncompressible we achieve the best space bound currently known (Grossi and Vitter, 2000); on compressible data our solution improves the succinct suffix array of (Grossi and Vitter, 2000) and the classical suffix tree and suffix array data structures either in space or in query time or both. We also study our opportunistic data structure in a dynamic setting and devise a variant achieving effective search and update time bounds. Finally, we show how to plug our opportunistic data structure into the Glimpse tool (Manber and Wu, 1994). The result is an indexing tool which achieves sublinear space and sublinear query time complexity.
data structures; data compression; database indexing; computational complexity; database theory; opportunistic data structures; data compression; data indexing; entropy; data set; query performance; search; succinct suffix array; suffix tree data structures; suffix array data structures; Glimpse tool; sublinear query time complexity; sublinear space complexity
P. Ferragina, G. Manzini, "Opportunistic data structures with applications", FOCS, 2000, 2013 IEEE 54th Annual Symposium on Foundations of Computer Science, 2013 IEEE 54th Annual Symposium on Foundations of Computer Science 2000, pp. 390, doi:10.1109/SFCS.2000.892127