Abstract
In this paper, we present an experimental study of the space-time tradeoffs for the dictionary problem, where we design a data structure to represent set data, which consist of a subset S of n items out of a universe U = {0, 1,...,u – 1} supporting various queries on S. Our primary goal is to reduce the space required for such a dictionary data structure. Many compression schemes have been developed for dictionaries, which fall generally in the categories of combinatorial encodings and data-aware methods and still support queries efficiently. We show that for many (real-world) datasets, data-aware methods lead to a worthwhile compression over combinatorial methods. Additionally, we design a new data-aware building block structure called BSGAP that presents improvements over other data-aware methods.
Support was provided in part by the National Science Foundation through research grant IIS–0415097.
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Gupta, A., Hon, WK., Shah, R., Vitter, J.S. (2006). Compressed Dictionaries: Space Measures, Data Sets, and Experiments. In: Àlvarez, C., Serna, M. (eds) Experimental Algorithms. WEA 2006. Lecture Notes in Computer Science, vol 4007. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11764298_14
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DOI: https://doi.org/10.1007/11764298_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34597-8
Online ISBN: 978-3-540-34598-5
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