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SSLC: A Search Algorithm Based on Linear Collisions and Poisson Probability Distribution

Published: 17 March 2022 Publication History

Abstract

This article proposes an algorithm, sequential search based on linear collisions (SSLC), based on Poisson probability distribution. SSLC works on large static volumes of data, whose keys are ordered and uniformly distributed. We proved that it takes a constant execution time with high probability. Experimental validation focuses on two metrics—processing time and memory resources. We compared SSLC with perfect hashing over various datasets. The experimentation reveals that our algorithm is three times faster with the use of three times fewer memory resources than perfect hashing. Its main contribution is to demonstrate that simple routines can have more powerful computational results.

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Published In

cover image ACM Journal of Experimental Algorithmics
ACM Journal of Experimental Algorithmics  Volume 27, Issue
December 2022
776 pages
ISSN:1084-6654
EISSN:1084-6654
DOI:10.1145/3505192
Issue’s Table of Contents

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 March 2022
Accepted: 01 November 2021
Received: 01 August 2021
Published in JEA Volume 27

Author Tags

  1. Linear collisions
  2. Poisson distribution
  3. sequential search
  4. perfect hashing
  5. search algorithm

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