Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Continuous Event Log Extraction for Process Mining
KTH, School of Information and Communication Technology (ICT).
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Process mining is the application of data science technologies on transactional business data to identify or monitor processes within an organization. The analyzed data often originates from process-unaware enterprise software, e.g. Enterprise Resource Planning (ERP) systems. The differences in data management between ERP and process mining systems result in a large fraction of ambiguous cases, affected by convergence and divergence. The consequence is a chasm between the process as interpreted by process mining, and the process as executed in the ERP system. In this thesis, a purchasing process of an SAP ERP system is used to demonstrate, how ERP data can be extracted and transformed into a process mining event log that expresses ambiguous cases as accurately as possible. As the content and structure of the event log already define the scope (i.e. which process) and granularity (i.e. activity types), the process mining results depend on the event log quality. The results of this thesis show how the consideration of case attributes, the notion of a case and the granularity of events can be used to manage the event log quality. The proposed solution supports continuous event extraction from the ERP system.

Abstract [sv]

Process mining är användningen av datavetenskaplig teknik för transaktionsdata, för att identifiera eller övervaka processer inom en organisation. Analyserade data härstammar ofta från processomedvetna företagsprogramvaror, såsom SAP-system, vilka är centrerade kring affärsdokumentation. Skillnaderna i data management mellan Enterprise Resource Planning (ERP)och process mining-system resulterar i en stor andel tvetydiga fall, vilka påverkas av konvergens och divergens. Detta resulterar i ett gap mellan processen som tolkas av process mining och processen som exekveras i ERP-systemet. I denna uppsats används en inköpsprocess för ett SAP ERP-system för att visa hur ERP-data kan extraheras och omvandlas till en process mining-orienterad händelselogg som uttrycker tvetydiga fall så precist som möjligt. Eftersom innehållet och strukturen hos händelseloggen redan definierar omfattningen (vilken process) och granularitet (aktivitetstyperna), så beror resultatet av process mining på kvalitén av händelseloggen. Resultaten av denna uppsats visar hur definitioner av typfall och händelsens granularitet kan användas för att förbättra kvalitén. Den beskrivna lösningen stöder kontinuerlig händelseloggsextraktion från ERPsystemet.

Place, publisher, year, edition, pages
2017. , 54 p.
Series
TRITA-ICT-EX, 2017:84
Keyword [en]
Process Mining, Event Log, Data Convergence and Divergence, Continuous Log Extraction, Case Identification
Keyword [sv]
Process Mining, händelselogg, Data Convergence, Data Divergence, Kontinuerlig loggextraktion, Typfallsidentifiering
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-210710OAI: oai:DiVA.org:kth-210710DiVA: diva2:1119380
Subject / course
Computer Science
Educational program
Master of Science - School of Electrical Engineering (EES) - Master of Science - Research on Information and Communication Technologies
Supervisors
Examiners
Available from: 2017-07-04 Created: 2017-07-04 Last updated: 2017-07-04Bibliographically approved

Open Access in DiVA

fulltext(2694 kB)80 downloads
File information
File name FULLTEXT01.pdfFile size 2694 kBChecksum SHA-512
9b516383491f4d84259b984bfa2a22493bb15c4dda5c437bd3c5ce222560af1cf8219e98a3475c122ca52de414ca82a98f4b58ada1d3f35cef65e1ca8c3b1255
Type fulltextMimetype application/pdf

By organisation
School of Information and Communication Technology (ICT)
Computer and Information Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 80 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 236 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf