Algorithms for telemetry data mining using discrete attributes

Roy B. Ofer, Adi Eldar, Adi Shalev, Yehezkel S. Resheff

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

As the cost of collecting and storing large amounts of data continues to drop, we see a constant rise in the amount of telemetry data collected by software applications and services. With the data mounding up, there is an increasing need for algorithms to automatically and efficiently mine insights from the collected data. One interesting case is the description of large tables using frequently occurring patterns, with implications for failure analysis and customer engagement. Finding frequently occurring patterns has applications both in an interactive usage where an analyst repeatedly query the data and in a completely automated process queries the data periodically and generate alerts and or reports based on the mining. Here we propose two novel mining algorithms for the purpose of computing such predominant patterns in relational data. The first method is a fast heuristic search, and the second is based on an adaptation of the apriori algorithm. Our methods are demonstrated on real-world datasets, and extensions to some additional fundamental mining tasks are discussed.

Original languageAmerican English
Title of host publicationICPRAM 2017 - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods
EditorsMaria De De Marsico, Gabriella Sanniti di Baja, Ana Fred
PublisherSciTePress
Pages309-317
Number of pages9
ISBN (Electronic)9789897582226
DOIs
StatePublished - 2017
Event6th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2017 - Porto, Portugal
Duration: 24 Feb 201726 Feb 2017

Publication series

NameICPRAM 2017 - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods
Volume2017-January

Conference

Conference6th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2017
Country/TerritoryPortugal
CityPorto
Period24/02/1726/02/17

Bibliographical note

Publisher Copyright:
Copyright © 2017 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.

Keywords

  • Data mining
  • Failure analysis
  • Pattern mining
  • Software telemetry
  • Subspace clustering

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