Assessing Big Data SQL Frameworks for Analyzing Event Logs

Markku Hinkka, Teemu Lehto, Keijo Heljanko

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

5 Citations (Scopus)

Abstract

Performing Process Mining by analyzing event logs generated by various systems is a very computation and I/O intensive task. Distributed computing and Big Data processing frameworks make it possible to distribute all kinds of computation tasks to multiple computers instead of performing the whole task in a single computer. This paper assesses whether contemporary structured query language (SQL) supporting Big Data processing frameworks are mature enough to be efficiently used to distribute computation of two central Process Mining tasks to two dissimilar clusters of computers providing BPM as a service in the cloud. Tests are performed by using a novel automatic testing framework detailed in this paper and its supporting materials. As a result, an assessment is made on how well selected Big Data processing frameworks manage to process and to parallelize the analysis work required by Process Mining tasks.

Original languageEnglish
Title of host publicationProceedings - 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP 2016
PublisherIEEE
Pages101-108
Number of pages8
ISBN (Print)9781467387750
DOIs
Publication statusPublished - 31 Mar 2016
MoE publication typeA4 Article in a conference publication
EventEuromicro International Conference on Parallel, Distributed, and Network-Based Processing - Heraklion, Greece
Duration: 17 Feb 201619 Feb 2016
Conference number: 24

Conference

ConferenceEuromicro International Conference on Parallel, Distributed, and Network-Based Processing
Abbreviated titlePDP
Country/TerritoryGreece
CityHeraklion
Period17/02/201619/02/2016

Keywords

  • automatic business process discovery
  • distributed computing framework
  • distributed SQL
  • event log analysis
  • Hadoop
  • Hive
  • Presto
  • process mining
  • Spark

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