Understanding knowlegde-intensive processes: From traces to instance graphs

Claudia Diamantini; Laura Genga; Domenico Potena

doi:10.1109/HPCSim.2016.7568338

Understanding knowlegde-intensive processes: From traces to instance graphs

Claudia Diamantini, Laura Genga, Domenico Potena

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

2 Downloads (Pure)

Samenvatting

Enterprise information systems, while support daily activities, typically collect data on executed processes in event logs. These data describe the temporal sequence in which activities were carried out, hiding possible parallelism and other control flows. Representing the structure of each process execution in the form of an Instance Graph, enables managers to discover valuable knowledge on enterprise behaviors. In this work, we describe BIG4ProM, a tool which implements the Building Instance Graph (BIG) algorithm. BIG4ProM exploits filtering Process Discovery algorithms implemented in ProM in order to return the set of instance graphs related to the given event log. The plug-in is conceived to support both expert and standard users.

Originele taal-2	Engels
Titel	2016 International Conference on High Performance Computing and Simulation, HPCS 2016
Redacteuren	Vesna Zeljkovic, Waleed W. Smari
Uitgeverij	Institute of Electrical and Electronics Engineers
Pagina's	218-221
Aantal pagina's	4
ISBN van elektronische versie	9781509020881
DOI's	https://doi.org/10.1109/HPCSim.2016.7568338
Status	Gepubliceerd - 13 sep. 2016
Extern gepubliceerd	Ja
Evenement	14th International Conference on High Performance Computing and Simulation, HPCS 2016 - Innsbruck, Oostenrijk Duur: 18 jul. 2016 → 22 jul. 2016

Congres

Congres	14th International Conference on High Performance Computing and Simulation, HPCS 2016
Land/Regio	Oostenrijk
Stad	Innsbruck
Periode	18/07/16 → 22/07/16

Toegang tot document

10.1109/HPCSim.2016.7568338

Andere bestanden en links

Link to publication in Scopus

Citeer dit

Diamantini, C., Genga, L., & Potena, D. (2016). Understanding knowlegde-intensive processes: From traces to instance graphs. In V. Zeljkovic, & W. W. Smari (editors), 2016 International Conference on High Performance Computing and Simulation, HPCS 2016 (blz. 218-221). Artikel 7568338 Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/HPCSim.2016.7568338

@inproceedings{634c3b17aeb94b60898610b89c4a2fd8,

title = "Understanding knowlegde-intensive processes: From traces to instance graphs",

abstract = "Enterprise information systems, while support daily activities, typically collect data on executed processes in event logs. These data describe the temporal sequence in which activities were carried out, hiding possible parallelism and other control flows. Representing the structure of each process execution in the form of an Instance Graph, enables managers to discover valuable knowledge on enterprise behaviors. In this work, we describe BIG4ProM, a tool which implements the Building Instance Graph (BIG) algorithm. BIG4ProM exploits filtering Process Discovery algorithms implemented in ProM in order to return the set of instance graphs related to the given event log. The plug-in is conceived to support both expert and standard users.",

keywords = "building instance graph, knowledge-intensive process, process mining, ProM",

author = "Claudia Diamantini and Laura Genga and Domenico Potena",

year = "2016",

month = sep,

day = "13",

doi = "10.1109/HPCSim.2016.7568338",

language = "English",

pages = "218--221",

editor = "Vesna Zeljkovic and Smari, {Waleed W.}",

booktitle = "2016 International Conference on High Performance Computing and Simulation, HPCS 2016",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "14th International Conference on High Performance Computing and Simulation, HPCS 2016 ; Conference date: 18-07-2016 Through 22-07-2016",

}

Diamantini, C, Genga, L & Potena, D 2016, Understanding knowlegde-intensive processes: From traces to instance graphs. in V Zeljkovic & WW Smari (uitgave), 2016 International Conference on High Performance Computing and Simulation, HPCS 2016., 7568338, Institute of Electrical and Electronics Engineers, blz. 218-221, 14th International Conference on High Performance Computing and Simulation, HPCS 2016, Innsbruck, Oostenrijk, 18/07/16. https://doi.org/10.1109/HPCSim.2016.7568338

Understanding knowlegde-intensive processes: From traces to instance graphs. / Diamantini, Claudia; Genga, Laura; Potena, Domenico.
2016 International Conference on High Performance Computing and Simulation, HPCS 2016. uitgave / Vesna Zeljkovic; Waleed W. Smari. Institute of Electrical and Electronics Engineers, 2016. blz. 218-221 7568338.

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

TY - GEN

T1 - Understanding knowlegde-intensive processes

T2 - 14th International Conference on High Performance Computing and Simulation, HPCS 2016

AU - Diamantini, Claudia

AU - Genga, Laura

AU - Potena, Domenico

PY - 2016/9/13

Y1 - 2016/9/13

N2 - Enterprise information systems, while support daily activities, typically collect data on executed processes in event logs. These data describe the temporal sequence in which activities were carried out, hiding possible parallelism and other control flows. Representing the structure of each process execution in the form of an Instance Graph, enables managers to discover valuable knowledge on enterprise behaviors. In this work, we describe BIG4ProM, a tool which implements the Building Instance Graph (BIG) algorithm. BIG4ProM exploits filtering Process Discovery algorithms implemented in ProM in order to return the set of instance graphs related to the given event log. The plug-in is conceived to support both expert and standard users.

AB - Enterprise information systems, while support daily activities, typically collect data on executed processes in event logs. These data describe the temporal sequence in which activities were carried out, hiding possible parallelism and other control flows. Representing the structure of each process execution in the form of an Instance Graph, enables managers to discover valuable knowledge on enterprise behaviors. In this work, we describe BIG4ProM, a tool which implements the Building Instance Graph (BIG) algorithm. BIG4ProM exploits filtering Process Discovery algorithms implemented in ProM in order to return the set of instance graphs related to the given event log. The plug-in is conceived to support both expert and standard users.

KW - building instance graph

KW - knowledge-intensive process

KW - process mining

KW - ProM

UR - http://www.scopus.com/inward/record.url?scp=84991584199&partnerID=8YFLogxK

U2 - 10.1109/HPCSim.2016.7568338

DO - 10.1109/HPCSim.2016.7568338

M3 - Conference contribution

AN - SCOPUS:84991584199

SP - 218

EP - 221

BT - 2016 International Conference on High Performance Computing and Simulation, HPCS 2016

A2 - Zeljkovic, Vesna

A2 - Smari, Waleed W.

PB - Institute of Electrical and Electronics Engineers

Y2 - 18 July 2016 through 22 July 2016

ER -

Understanding knowlegde-intensive processes: From traces to instance graphs

Samenvatting

Congres

Toegang tot document

Andere bestanden en links

Vingerafdruk

Citeer dit