Analysis of SAP Log Data Based on Network Community Decomposition

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F19%3A10244304" target="_blank" >RIV/61989100:27240/19:10244304 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.mdpi.com/2078-2489/10/3/92/htm" target="_blank" >https://www.mdpi.com/2078-2489/10/3/92/htm</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/info10030092" target="_blank" >10.3390/info10030092</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Analysis of SAP Log Data Based on Network Community Decomposition

Popis výsledku v původním jazyce

Information systems support and ensure the practical running of the most critical business processes. There exists (or can be reconstructed) a record (log) of the process running in the information system. Computer methods of data mining can be used for analysis of process data utilizing support techniques of machine learning and a complex network analysis. The analysis is usually provided based on quantitative parameters of the running process of the information system. It is not so usual to analyze behavior of the participants of the running process from the process log. Here, we show how data and process mining methods can be used for analyzing the running process and how participants behavior can be analyzed from the process log using network (community or cluster) analyses in the constructed complex network from the SAP business process log. This approach constructs a complex network from the process log in a given context and then finds communities or patterns in this network. Found communities or patterns are analyzed using knowledge of the business process and the environment in which the process operates. The results demonstrate the possibility to cover up not only the quantitative but also the qualitative relations (e.g., hidden behavior of participants) using the process log and specific knowledge of the business case.

Název v anglickém jazyce

Analysis of SAP Log Data Based on Network Community Decomposition

Popis výsledku anglicky

Information systems support and ensure the practical running of the most critical business processes. There exists (or can be reconstructed) a record (log) of the process running in the information system. Computer methods of data mining can be used for analysis of process data utilizing support techniques of machine learning and a complex network analysis. The analysis is usually provided based on quantitative parameters of the running process of the information system. It is not so usual to analyze behavior of the participants of the running process from the process log. Here, we show how data and process mining methods can be used for analyzing the running process and how participants behavior can be analyzed from the process log using network (community or cluster) analyses in the constructed complex network from the SAP business process log. This approach constructs a complex network from the process log in a given context and then finds communities or patterns in this network. Found communities or patterns are analyzed using knowledge of the business process and the environment in which the process operates. The results demonstrate the possibility to cover up not only the quantitative but also the qualitative relations (e.g., hidden behavior of participants) using the process log and specific knowledge of the business case.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Information

ISSN

2078-2489

e-ISSN

—

Svazek periodika

10

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

25

Strana od-do

—

Kód UT WoS článku

000464294500001

EID výsledku v databázi Scopus

2-s2.0-85063871014