• Journal of KIISE:Computer Systems and Theory
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• v.36 no.4
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• pp.283-290
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• 2009

The rank/select data structure is a basic tool of succinct representations for several data structures such as trees, graphs and text indexes. For a given string sequence, it is used to answer the occurrence of characters up to a certain position. In previous studies, theoretical rank/select data structures were proposed, but they didn't support practical operational time and space. In this paper, we propose a simple solution for implementing rank/select data structures efficiently. According to experiments, our methods without complex encodings achieve nH$_0$ + O(n) bits of theoretical size and perform rank/select operations faster than the original HSS data structure.

• KIISE Transactions on Computing Practices
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• v.23 no.10
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• pp.594-598
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• 2017

Tremendous quantities of numerical data are generated every day from various sources, including the stock market. Universal codes such as Elias gamma coding, Elias delta coding and Fibonacci coding are generally used to store arrays of integers. Studies have been conducted to support fast access to specific elements in an integer array, while occupying less space. We suggest an improved code system that utilizes the concepts of succinct data structures. This system is based on a data structure that allows compressing a delimiter bit array while supporting queries in constant time. The results of an experiment show that the encoded array uses lower space, while not sacrificing time efficiency.

• Journal of Industrial Technology
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• v.21 no.A
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• pp.147-153
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• 2001

This paper presents techniques for discovering rules with elastic patterns. Elastic patterns are useful for discovering rules from data sequences with different sampling rates. For fast discovery of rules whose heads and bodies are elastic patterns, we construct a suffix tree from succinct forms of data sequences. The suffix tree is a compact representation of rules, and is also used as an index structure for finding rules matched to a target head sequence. When matched rules cannot be found, the concept of rule relaxation is introduced. Using a cluster hierarchy and a relaxation error, we find the least relaxed rules that provide the most specific information on a target head sequence. Performance evaluation through extensive experiments reseals the effectiveness of the proposed approach.