Activity duration prediction of workflows by using a data science approach: Unveiling the advantage of semantics
Publication date
2017-09-21
Document type
Konferenzbeitrag
Author
Weller, Tobias
Organisational unit
Karlsruhe Institute of Technology
Scopus ID
ISSN
Conference
Semantic Web Technologies for the Internet of Things (SWIT 2017) : Vienna, Austria, October 21, 2017
Series or journal
CEUR Workshop Proceedings
Periodical volume
1930
Book title
Proceedings of the Second Workshop on Semantic Web Technologies for the Internet of Things co-located with 16th International Semantic Web Conference (ISWC 2017)
Part of the university bibliography
Nein
Keyword
Activity duration prediction
Data science
Semantic annotations
Workflow analysis
Abstract
Organizations often have to face a dynamic market environment. Processes must be frequently adapted in order to stay competitive and allow an efficient workflow. Data Science approaches are currently often used in analysis methods to identify influential indicators on processes and learn predictive models to estimate the duration of an activity. However, current methods do not or only partially make use of semantic information in process analysis. The results are unprecise or incomplete, because not all influential indicators have been unveiled and therefore used in the predictive models. We want to make use of the semantics and show the advantage by applying them on existing data science methods for predicting the duration of an activity in a process. Therefore, we 1) enrich process data with meta-information and background knowledge 2) extend existing data science methods so that they include semantic information in their analysis and 3) apply data science methods for predicting values and compare the results with methods, which do not use semantics.
Version
Published version
Access right on openHSU
Metadata only access