Compact Data Structures
Speaker: Gonzalo Navarro – Santiago, ChileTopic(s): Computational Theory, Algorithms and Mathematics
Abstract
The sharp growth in the amount of data applications are handling, plus the increasing gap in the memory hierarchy, gives rise to several approaches to efficiently handling large volumes of information. One is the use of compact data structures, which ideally use space asymptotically equal to the entropy of the data they handle, while at the same time support a number of query and navigational operations over the data.
This area lies between Information Theory and Data Structures. Like the former, it aims to represent the data within as little space as possible, but it is not just compression. Like the latter, the representation must support operations on the data, but precomputing data to speed up those operations is generally avoided.
In this tutorial I will overview the main compact data structures for bitvectors, sequences, permutations, trees, graphs, grids, and texts. I will focus on the theoretical aspects of the problem: the relation with the worst-case and Shannon entropy of the data, time/space complexities and lower bounds. I will also include current research lines and open problems.
About this Lecture
Number of Slides: 120Duration: 120 minutes
Languages Available: English
Last Updated:
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