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Pattern-based Search of Epigenomic Data Using GeNemo
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String search experimentation using massive data.

Alistair Moffat1, Simon Gog

  • 1Department of Computing and Information Systems, The University of Melbourne, , Melbourne, Victoria 3010, Australia.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|April 23, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces new methods for evaluating string search algorithms. By stratifying patterns by length, frequency, and text repetitiveness, researchers can achieve more precise performance measurements.

Keywords:
experimental evaluationpattern searchstring indexing

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Area of Science:

  • Computer Science
  • Data Structures
  • Algorithm Analysis

Background:

  • Experimental evaluations are crucial for assessing new string search and indexing algorithms.
  • Existing evaluation methodologies may not fully capture algorithm performance across diverse datasets.
  • Understanding algorithm behavior requires careful consideration of pattern characteristics and text properties.

Purpose of the Study:

  • To provide guidance on conducting rigorous experimental evaluations of string search algorithms.
  • To introduce novel methodologies for stratifying search patterns and categorizing text repetitiveness.
  • To enhance the understanding of string search algorithm behavior through refined measurement techniques.

Main Methods:

  • Developed a method for stratifying search patterns by length and frequency.
  • Introduced a metric to quantify text repetitiveness.
  • Applied these methods to conduct precise response-time measurements.
  • Integrated pattern and text characteristics into algorithm evaluations.

Main Results:

  • Stratification by pattern length and frequency enables precise response-time measurements.
  • A text repetitiveness metric allows for factoring dataset type into evaluations.
  • Separating these concepts leads to a deeper understanding of algorithm performance.
  • The proposed methodologies reveal nuanced algorithm behaviors.

Conclusions:

  • The presented methodologies offer a more comprehensive approach to evaluating string search algorithms.
  • Precise measurements and dataset characterization are key to understanding algorithm efficiency.
  • This work provides a framework for more informative and reproducible algorithm benchmarking.
  • Future evaluations should incorporate pattern and text properties for robust analysis.