Mining timed sequential patterns: The Minits-AllOcc technique

Somayah Karsoum, Clark Barrus, Le Gruenwald, Eleazar Leal

Research output: Contribution to journalArticlepeer-review

Abstract

Sequential pattern mining is one of the data mining tasks used to find the subsequences in a sequence dataset that appear together in order based on time. Sequence data can be collected from devices, such as sensors, GPS, or satel-lites, and ordered based on timestamps, which are the times when they are generated/collected. Mining patterns in such data can be used to support many applications, including transportation recommendation systems, transportation safety, weather forecasting, and disease symptom analysis. Numerous techniques have been proposed to address the problem of how to mine subsequences in a sequence dataset; however, current traditional algorithms ignore the temporal information between the itemset in a sequential pattern. This information is essential in many situations. Though knowing that measurement Y occurs after measurement X is valuable, it is more valuable to know the estimated time before the appearance of measurement Y, for example, to schedule maintenance at the right time to prevent railway damage. Con-sidering temporal relationship information for sequential patterns raises new issues to be solved, such as designing a new data structure to save this information and traversing this structure efficiently to discover patterns without re-scan-ning the database. In this paper, we propose an algorithm called Minits-AllOcc (MINIng Timed Sequential Pattern for All-time Occurrences) to find sequential patterns and the transition time between itemsets based on all occurrences of a pattern in the database. We also propose a parallel multi-core CPU version of this algorithm, called MMinits-AllOcc (Multi-core for MINIng Timed Sequential Pattern for All-time Occurrences), to deal with Big Data. Extensive experi-ments on real and synthetic datasets show the advantages of this approach over the brute-force method. Also, the mul-ti-core CPU version of the algorithm is shown to outperform the single-core version on Big Data by 2.5X.

Original languageEnglish (US)
JournalJournal of Autonomous Intelligence
Volume6
Issue number1
DOIs
StatePublished - 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023 by author(s).

Keywords

  • Data Mining
  • Sequential Pattern Mining
  • Singe-core and Multi-core Processor
  • Timed Sequential Patterns

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