Paper titled “Divide And Conquer: A Tool Framework for Supporting Decomposed Discovery in Process Mining” has been accepted for publication

dac

The paper titled “Divide And Conquer: A Tool Framework for Supporting Decomposed Discovery in Process Mining” accepted for publication in The Computer Journal. Abstract Process mining has been around for more than a decade now, and in that period several discovery algorithms have been introduced that work fairly well on average-sized event logs, that is, … [Read more…]

Publications 2017

G. Acampora, A. Vitiello, B. Di Stefano, W. M. P. van der Aalst, C. W. Günther, and H. M. W. Verbeek, “IEEE 1849TM: The XES Standard: The Second IEEE Standard Sponsored by IEEE Computational Intelligence Society,” IEEE Computational Intelligence Magazine, pp. 4-8, 2017. [Bibtex] @Article{Acampora17, Title = {{IEEE 1849TM: The XES Standard: The Second IEEE … [Read more…]

Paper titled “Decomposed Replay Using Hiding and Reduction as Abstraction” accepted for publication

verbeek17

The paper titled “Decomposed Replay Using Hiding and Reduction as Abstraction” has been accepted for publication in the ToPNoC issue that includes  selected ATPN 2017 workshop papers. Abstract In the area of process mining, decomposed replay has been proposed to be able to deal with nets and logs containing many different activities. The main assumption … [Read more…]

New Plug-in: Replay using Recomposition

A new plug-in has been added to the Divide-and-Conquer framework: The “Replay using Recomposition” plug-in. Initially, this plug-in is similar to the “Replay with Decomposition” plug-in, but it does not stop there. Where the “Replay with Decomposition” plug-in stops after having done the decomposed replay, the “Replay with Recomposition” possibly continues to improve on the … [Read more…]

Updated Divide and Conquer framework

The Divide and Conquer framework (for decomposed discovery and decomposed replay) now also supports a move-on-model activity. A transition that is mapped to this activity will always result in a visible move-on-model in the replayer, like a transition that is mapped to the invisible activity will always result in an invisible move-on-model. The difference between … [Read more…]

Presentations 2016

Decomposed Replay Using Hiding and Reduction Presented at the PNSE 2016 workshop on Tuesday June 21. Presentation of the following publication: H. M. W. Verbeek, “Decomposed replay using hiding and reduction,” in PNSE 2016 workshop proceedings, Torun, Poland, 2016. [Bibtex] @InProceedings{Verbeek16a, Title = {Decomposed Replay using Hiding and Reduction}, Author = {Verbeek, H. M. W.}, … [Read more…]

New Plug-in: DrFurby Classifier

Input Training log (XLog) Test log (XLog) Output Test log (XLog) with classified traces (using DrFurby Extension, see below). Package DivideAndConquer DrFurby Classifier Plug-in The DrFurby Classifier plug-in takes a training log and a test log, and returns a copy of the test log where every trace in the test log is classified as fitting … [Read more…]

New alignment merge algorithm

The alignment merge algorithm has been updated. Instead of merging one subalignment at a time, it now merges all subalignments at once. This new algorithm comes with two options: A strategy that determines which legal move from a collection of legal moves is considered to be best. A boolean that states whether the merged alignment … [Read more…]