Knowledge-based trace abstraction for semantic process mining

Stefania Montani; Manuel Striani; Silvana Quaglini; Anna Cavallini; Giorgio Leonardi

doi:10.1007/978-3-319-59758-4_30

Knowledge-based trace abstraction for semantic process mining

Stefania Montani, Manuel Striani, Silvana Quaglini, Anna Cavallini, Giorgio Leonardi

Department of Science and Technological Innovation

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Many hospital information systems nowadays record data about the executed medical process instances in the form of traces in an event log. In this paper we present a framework able to convert actions found in the traces into higher level concepts, on the basis of domain knowledge. Abstracted traces are then provided as an input to semantic process mining. The approach has been tested in stroke care, where we show how the abstraction mechanism allows the user to mine process models that are easier to interpret, since unnecessary details are hidden, but key behaviors are clearly visible.

Original language	English
Title of host publication	Artificial Intelligence in Medicine - 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Proceedings
Editors	Annette [surname]ten Teije, Christian Popow, Lucia Sacchi, John H. Holmes
Publisher	Springer Verlag
Pages	267-271
Number of pages	5
ISBN (Print)	9783319597577
DOIs	https://doi.org/10.1007/978-3-319-59758-4_30
Publication status	Published - 2017
Event	16th Conference on Artificial Intelligence in Medicine, AIME 2017 - Vienna, Austria Duration: 21 Jun 2017 → 24 Jun 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10259 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th Conference on Artificial Intelligence in Medicine, AIME 2017
Country/Territory	Austria
City	Vienna
Period	21/06/17 → 24/06/17

Access to Document

10.1007/978-3-319-59758-4_30

Cite this

Montani, S., Striani, M., Quaglini, S., Cavallini, A., & Leonardi, G. (2017). Knowledge-based trace abstraction for semantic process mining. In A. [surname]ten Teije, C. Popow, L. Sacchi, & J. H. Holmes (Eds.), Artificial Intelligence in Medicine - 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Proceedings (pp. 267-271). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10259 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-59758-4_30

Montani, Stefania ; Striani, Manuel ; Quaglini, Silvana et al. / Knowledge-based trace abstraction for semantic process mining. Artificial Intelligence in Medicine - 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Proceedings. editor / Annette [surname]ten Teije ; Christian Popow ; Lucia Sacchi ; John H. Holmes. Springer Verlag, 2017. pp. 267-271 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Knowledge-based trace abstraction for semantic process mining",

abstract = "Many hospital information systems nowadays record data about the executed medical process instances in the form of traces in an event log. In this paper we present a framework able to convert actions found in the traces into higher level concepts, on the basis of domain knowledge. Abstracted traces are then provided as an input to semantic process mining. The approach has been tested in stroke care, where we show how the abstraction mechanism allows the user to mine process models that are easier to interpret, since unnecessary details are hidden, but key behaviors are clearly visible.",

author = "Stefania Montani and Manuel Striani and Silvana Quaglini and Anna Cavallini and Giorgio Leonardi",

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Montani, S , Striani, M, Quaglini, S, Cavallini, A & Leonardi, G 2017, Knowledge-based trace abstraction for semantic process mining. in A [surname]ten Teije, C Popow, L Sacchi & JH Holmes (eds), Artificial Intelligence in Medicine - 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10259 LNAI, Springer Verlag, pp. 267-271, 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Vienna, Austria, 21/06/17. https://doi.org/10.1007/978-3-319-59758-4_30

Knowledge-based trace abstraction for semantic process mining. / Montani, Stefania ; Striani, Manuel; Quaglini, Silvana et al.
Artificial Intelligence in Medicine - 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Proceedings. ed. / Annette [surname]ten Teije; Christian Popow; Lucia Sacchi; John H. Holmes. Springer Verlag, 2017. p. 267-271 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10259 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Knowledge-based trace abstraction for semantic process mining

AU - Montani, Stefania

AU - Striani, Manuel

AU - Quaglini, Silvana

AU - Cavallini, Anna

AU - Leonardi, Giorgio

N1 - Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017

Y1 - 2017

N2 - Many hospital information systems nowadays record data about the executed medical process instances in the form of traces in an event log. In this paper we present a framework able to convert actions found in the traces into higher level concepts, on the basis of domain knowledge. Abstracted traces are then provided as an input to semantic process mining. The approach has been tested in stroke care, where we show how the abstraction mechanism allows the user to mine process models that are easier to interpret, since unnecessary details are hidden, but key behaviors are clearly visible.

AB - Many hospital information systems nowadays record data about the executed medical process instances in the form of traces in an event log. In this paper we present a framework able to convert actions found in the traces into higher level concepts, on the basis of domain knowledge. Abstracted traces are then provided as an input to semantic process mining. The approach has been tested in stroke care, where we show how the abstraction mechanism allows the user to mine process models that are easier to interpret, since unnecessary details are hidden, but key behaviors are clearly visible.

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DO - 10.1007/978-3-319-59758-4_30

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AN - SCOPUS:85021691848

SN - 9783319597577

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 267

EP - 271

BT - Artificial Intelligence in Medicine - 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Proceedings

A2 - [surname]ten Teije, Annette

A2 - Popow, Christian

A2 - Sacchi, Lucia

A2 - Holmes, John H.

PB - Springer Verlag

T2 - 16th Conference on Artificial Intelligence in Medicine, AIME 2017

Y2 - 21 June 2017 through 24 June 2017

ER -

Montani S , Striani M, Quaglini S, Cavallini A, Leonardi G. Knowledge-based trace abstraction for semantic process mining. In [surname]ten Teije A, Popow C, Sacchi L, Holmes JH, editors, Artificial Intelligence in Medicine - 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Proceedings. Springer Verlag. 2017. p. 267-271. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-59758-4_30

Knowledge-based trace abstraction for semantic process mining

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