Adapting association rule mining to discover patterns of collaboration in process logs
Stefan Schönig,Michael Zeising,Stefan Jablonski +2 more
- pp 531-534
Reads0
Chats0
TLDR
A method to discover cross-perspective collaborative patterns in process logs and therefore strive for a genotypic analysis of recorded process data is proposed using the association rule mining algorithm to analyse execution logs.Abstract:
The execution order of work steps within business processes is influenced by several factors, like the organizational position of performing agents, document flows or temporal de-pendencies. Process mining techniques are successfully used to discover execution orders from process execution logs automati-cally. However, the methods are mostly discovering the execution order of process steps without facing possible coherencies with other perspectives of business processes, i.e., other types of pro-cess execution data. In this paper, we propose a method to dis-cover cross-perspective collaborative patterns in process logs and therefore strive for a genotypic analysis of recorded process data. For this purpose, we adapted the association rule mining algo-rithm to analyse execution logs. The resulting rules can be used for guiding users through collaborative process execution.read more
Citations
More filters
Proceedings ArticleDOI
Supporting collaborative work by learning process models and patterns from cases
TL;DR: It is proposed that managing human-centric processes should start with model skeletons that serve as a lattice where initial process execution can lean against, and may evolve over time, become more and more complete and better reflect operational reality.
DissertationDOI
A proposal for the evolution of model-driven software
TL;DR: A new development method described as a possible concrete evolution of MDSE that defines a paradigm for software development is introduced along with domain specific languages, a tool-chain and sample software systems.
Comprehensive Business Process Management through Observation and Navigation
TL;DR: This contribution introduces the integration of both systems which leads to an approach for supporting the full range from unmodelled processes to both automation and knowledge-intensive processes as well as the transition from un modelled to modelled processes.
Journal ArticleDOI
A survey on recommendation in process mining
TL;DR: A systematic review is conducted to provide a taxonomy of the published studies on process‐aware recommender systems based on specified criteria, including the type and perspective of recommendation, a list of datasets and evaluation metrics used in the setting of PARS, implementation environments, and different algorithms used in PARS.
Posted Content
A Systematic Literature Review on Process-Aware Recommender Systems.
TL;DR: In this article, a systematic review was conducted on 33 academic articles published between 2008 and 2020 according to several aspects, and a state-of-the-art review with critical details and researchers with a better perception of which path to pursue in this field was provided.
References
More filters
Journal ArticleDOI
The WEKA data mining software: an update
TL;DR: This paper provides an introduction to the WEKA workbench, reviews the history of the project, and, in light of the recent 3.6 stable release, briefly discusses what has been added since the last stable version (Weka 3.4) released in 2003.
Proceedings ArticleDOI
Mining association rules between sets of items in large databases
TL;DR: An efficient algorithm is presented that generates all significant association rules between items in the database of customer transactions and incorporates buffer management and novel estimation and pruning techniques.
Book
Process Mining: Discovery, Conformance and Enhancement of Business Processes
TL;DR: This book provides real-world techniques for monitoring and analyzing processes in real time and is a powerful new tool destined to play a key role in business process management.
Book ChapterDOI
The prom framework: a new era in process mining tool support
B. F. van Dongen,A. K. A. de Medeiros,H. M. W. Verbeek,A.J.M.M. Weijters,W.M.P. van der Aalst +4 more
TL;DR: The ProM framework is introduced and an overview of the plug-ins that have been developed and is flexible with respect to the input and output format, and is also open enough to allow for the easy reuse of code during the implementation of new process mining ideas.