Pattern Discovery in Conceptual Models Using Frequent Itemset Mining

Mattia Fumagalli; Tiago Prince Sales; Giancarlo Guizzardi

doi:10.1007/978-3-031-17995-2_4

Pattern Discovery in Conceptual Models Using Frequent Itemset Mining

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

6 Citations (Scopus)

267 Downloads (Pure)

Abstract

Patterns are recurrent structures that provide key insights for Conceptual Modeling. Typically, patterns emerge from the repeated modeling practice in a given field. However, their discovery, if performed manually, is a slow and highly laborious task and, hence, it usually takes years for pattern catalogs to emerge in new domains. For this reason, the field would greatly benefit from the creation of automated data-driven techniques for the empirical discovery of patterns. In this paper, we propose a highly automated interactive approach for the discovery of patterns from conceptual model catalogs. The approach combines graph manipulation and Frequent Itemset Mining techniques. We also advance a computational tool implementing our proposal, which is then validated in an experiment with a dataset of 105 UML models.

Original language	English
Title of host publication	Conceptual Modeling
Subtitle of host publication	41st International Conference, ER 2022, Hyderabad, India, October 17-20, 2022, Proceedings
Editors	Jolita Ralyté, Sharma Chakravarthy, Mukesh Mohania, Manfred A. Jeusfeld, Kamalakar Karlapalem
Publisher	Springer
Pages	52-62
Number of pages	11
ISBN (Electronic)	978-3-031-17995-2
ISBN (Print)	978-3-031-17994-5
DOIs	https://doi.org/10.1007/978-3-031-17995-2_4
Publication status	Published - 10 Oct 2022
Event	41st International Conference on Conceptual Modeling, ER 2022 - Virtual Event Duration: 17 Oct 2022 → 20 Oct 2022 Conference number: 41 https://er2022web.github.io/ER2022/

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	13607

Conference

Conference	41st International Conference on Conceptual Modeling, ER 2022
Abbreviated title	ER 2022
City	Virtual Event
Period	17/10/22 → 20/10/22
Internet address	https://er2022web.github.io/ER2022/

Keywords

Modeling patterns
Pattern discovery
Itemset mining
2023 OA procedure

Access to Document

10.1007/978-3-031-17995-2_4

978-3-031-17995-2_4Final published version, 1.29 MBLicence: Taverne

Cite this

Fumagalli, M., Sales, T. P., & Guizzardi, G. (2022). Pattern Discovery in Conceptual Models Using Frequent Itemset Mining. In J. Ralyté, S. Chakravarthy, M. Mohania, M. A. Jeusfeld, & K. Karlapalem (Eds.), Conceptual Modeling : 41st International Conference, ER 2022, Hyderabad, India, October 17-20, 2022, Proceedings (pp. 52-62). (Lecture Notes in Computer Science; Vol. 13607). Springer. https://doi.org/10.1007/978-3-031-17995-2_4

Fumagalli, Mattia ; Sales, Tiago Prince ; Guizzardi, Giancarlo. / Pattern Discovery in Conceptual Models Using Frequent Itemset Mining. Conceptual Modeling : 41st International Conference, ER 2022, Hyderabad, India, October 17-20, 2022, Proceedings. editor / Jolita Ralyté ; Sharma Chakravarthy ; Mukesh Mohania ; Manfred A. Jeusfeld ; Kamalakar Karlapalem. Springer, 2022. pp. 52-62 (Lecture Notes in Computer Science).

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title = "Pattern Discovery in Conceptual Models Using Frequent Itemset Mining",

abstract = "Patterns are recurrent structures that provide key insights for Conceptual Modeling. Typically, patterns emerge from the repeated modeling practice in a given field. However, their discovery, if performed manually, is a slow and highly laborious task and, hence, it usually takes years for pattern catalogs to emerge in new domains. For this reason, the field would greatly benefit from the creation of automated data-driven techniques for the empirical discovery of patterns. In this paper, we propose a highly automated interactive approach for the discovery of patterns from conceptual model catalogs. The approach combines graph manipulation and Frequent Itemset Mining techniques. We also advance a computational tool implementing our proposal, which is then validated in an experiment with a dataset of 105 UML models.",

keywords = "Modeling patterns, Pattern discovery, Itemset mining, 2023 OA procedure",

author = "Mattia Fumagalli and Sales, \{Tiago Prince\} and Giancarlo Guizzardi",

note = "1. E.g. https://github.com/wilmerkrisp/patterns, http://www.bpmpatterns.org. 2. The bisections are balanced in terms of the number of nodes and edges. 3. Example at https://purl.org/mining-cm-patterns/pattern-example. 4. Source code is available at https://purl.org/krdb-core/mining-cm. 5. https://purl.org/mining-cm-patterns/experiment. 6. https://purl.org/mining-cm-patterns/performance.; 41st International Conference on Conceptual Modeling, ER 2022, ER 2022 ; Conference date: 17-10-2022 Through 20-10-2022",

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doi = "10.1007/978-3-031-17995-2\_4",

language = "English",

isbn = "978-3-031-17994-5",

series = "Lecture Notes in Computer Science",

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pages = "52--62",

editor = "Jolita Ralyt{\'e} and Sharma Chakravarthy and Mukesh Mohania and Jeusfeld, \{Manfred A.\} and Kamalakar Karlapalem",

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Fumagalli, M, Sales, TP & Guizzardi, G 2022, Pattern Discovery in Conceptual Models Using Frequent Itemset Mining. in J Ralyté, S Chakravarthy, M Mohania, MA Jeusfeld & K Karlapalem (eds), Conceptual Modeling : 41st International Conference, ER 2022, Hyderabad, India, October 17-20, 2022, Proceedings. Lecture Notes in Computer Science, vol. 13607, Springer, pp. 52-62, 41st International Conference on Conceptual Modeling, ER 2022, Virtual Event, 17/10/22. https://doi.org/10.1007/978-3-031-17995-2_4

Pattern Discovery in Conceptual Models Using Frequent Itemset Mining. / Fumagalli, Mattia; Sales, Tiago Prince ; Guizzardi, Giancarlo.
Conceptual Modeling : 41st International Conference, ER 2022, Hyderabad, India, October 17-20, 2022, Proceedings. ed. / Jolita Ralyté; Sharma Chakravarthy; Mukesh Mohania; Manfred A. Jeusfeld; Kamalakar Karlapalem. Springer, 2022. p. 52-62 (Lecture Notes in Computer Science; Vol. 13607).

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

TY - GEN

T1 - Pattern Discovery in Conceptual Models Using Frequent Itemset Mining

AU - Fumagalli, Mattia

AU - Sales, Tiago Prince

AU - Guizzardi, Giancarlo

N1 - Conference code: 41

PY - 2022/10/10

Y1 - 2022/10/10

N2 - Patterns are recurrent structures that provide key insights for Conceptual Modeling. Typically, patterns emerge from the repeated modeling practice in a given field. However, their discovery, if performed manually, is a slow and highly laborious task and, hence, it usually takes years for pattern catalogs to emerge in new domains. For this reason, the field would greatly benefit from the creation of automated data-driven techniques for the empirical discovery of patterns. In this paper, we propose a highly automated interactive approach for the discovery of patterns from conceptual model catalogs. The approach combines graph manipulation and Frequent Itemset Mining techniques. We also advance a computational tool implementing our proposal, which is then validated in an experiment with a dataset of 105 UML models.

AB - Patterns are recurrent structures that provide key insights for Conceptual Modeling. Typically, patterns emerge from the repeated modeling practice in a given field. However, their discovery, if performed manually, is a slow and highly laborious task and, hence, it usually takes years for pattern catalogs to emerge in new domains. For this reason, the field would greatly benefit from the creation of automated data-driven techniques for the empirical discovery of patterns. In this paper, we propose a highly automated interactive approach for the discovery of patterns from conceptual model catalogs. The approach combines graph manipulation and Frequent Itemset Mining techniques. We also advance a computational tool implementing our proposal, which is then validated in an experiment with a dataset of 105 UML models.

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KW - Pattern discovery

KW - Itemset mining

KW - 2023 OA procedure

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T3 - Lecture Notes in Computer Science

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BT - Conceptual Modeling

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A2 - Karlapalem, Kamalakar

PB - Springer

T2 - 41st International Conference on Conceptual Modeling, ER 2022

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Fumagalli M, Sales TP , Guizzardi G. Pattern Discovery in Conceptual Models Using Frequent Itemset Mining. In Ralyté J, Chakravarthy S, Mohania M, Jeusfeld MA, Karlapalem K, editors, Conceptual Modeling : 41st International Conference, ER 2022, Hyderabad, India, October 17-20, 2022, Proceedings. Springer. 2022. p. 52-62. (Lecture Notes in Computer Science). doi: 10.1007/978-3-031-17995-2_4

Pattern Discovery in Conceptual Models Using Frequent Itemset Mining

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