AIMED: An automatic and incremental approach for business process model repair under concept drift

Real-life business processes may change over time in response to new business requirements, market changes, new policies or regulations, etc., which is called concept drift in the data mining area. How to identify and deal with the concept drift problem is a significant challenge in business process...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Guan, Wei [verfasserIn] Cao, Jian [verfasserIn] Gu, Yang [verfasserIn] Qian, Shiyou [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Concept drift Process mining WF-net Process model repair Conformance checking

Übergeordnetes Werk:	Enthalten in: Information systems - Oxford [u.a.] : Pergamon Press, 1975, 119
Übergeordnetes Werk:	volume:119

DOI / URN:	10.1016/j.is.2023.102285

Katalog-ID:	ELV06526391X

Internformat


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520			\|a Real-life business processes may change over time in response to new business requirements, market changes, new policies or regulations, etc., which is called concept drift in the data mining area. How to identify and deal with the concept drift problem is a significant challenge in business process mining. Currently, the research mainly focuses on the problems of concept drift detection. However, the proposed approaches do not intuitively explain how the process changes. In this paper, we provide an integrated framework whose core technique is an Automatic and Incremental approach for business process Model rEpair under concept Drift (AIMED). AIMED streamlines the functions of concept drift detection, localization and model repair. More specifically, when concept drift is detected, it updates the process model automatically by precisely localizing the sub-structure of the process model that concept drift affects and updating this sub-structure accordingly. Concept drift is explained intuitively by presenting the repaired model. In particular, AIMED can resist the noise that greatly affects the performance of current concept drift detection and process model repair techniques. The properties of AIMED are theoretically proven. We also conduct extensive experiments on synthetic logs as well as real-life logs. The experiment results show that AIMED outperforms the state-of-the-art methods in both concept drift detection and process model repair and works well even when there is noise in logs.
650		4	\|a Concept drift
650		4	\|a Process mining
650		4	\|a WF-net
650		4	\|a Process model repair
650		4	\|a Conformance checking
700	1		\|a Cao, Jian \|e verfasserin \|4 aut
700	1		\|a Gu, Yang \|e verfasserin \|4 aut
700	1		\|a Qian, Shiyou \|e verfasserin \|4 aut
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matchkey_str	article:03064379:2023----::ieaatmtcniceetlprahobsnspoesoer
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bklnumber	54.64 06.74
publishDate	2023
allfields	10.1016/j.is.2023.102285 doi (DE-627)ELV06526391X (ELSEVIER)S0306-4379(23)00121-7 DE-627 ger DE-627 rda eng 070 VZ 54.64 bkl 06.74 bkl Guan, Wei verfasserin (orcid)0000-0002-8979-6847 aut AIMED: An automatic and incremental approach for business process model repair under concept drift 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Real-life business processes may change over time in response to new business requirements, market changes, new policies or regulations, etc., which is called concept drift in the data mining area. How to identify and deal with the concept drift problem is a significant challenge in business process mining. Currently, the research mainly focuses on the problems of concept drift detection. However, the proposed approaches do not intuitively explain how the process changes. In this paper, we provide an integrated framework whose core technique is an Automatic and Incremental approach for business process Model rEpair under concept Drift (AIMED). AIMED streamlines the functions of concept drift detection, localization and model repair. More specifically, when concept drift is detected, it updates the process model automatically by precisely localizing the sub-structure of the process model that concept drift affects and updating this sub-structure accordingly. Concept drift is explained intuitively by presenting the repaired model. In particular, AIMED can resist the noise that greatly affects the performance of current concept drift detection and process model repair techniques. The properties of AIMED are theoretically proven. We also conduct extensive experiments on synthetic logs as well as real-life logs. The experiment results show that AIMED outperforms the state-of-the-art methods in both concept drift detection and process model repair and works well even when there is noise in logs. Concept drift Process mining WF-net Process model repair Conformance checking Cao, Jian verfasserin aut Gu, Yang verfasserin aut Qian, Shiyou verfasserin aut Enthalten in Information systems Oxford [u.a.] : Pergamon Press, 1975 119 Online-Ressource (DE-627)320502864 (DE-600)2012447-8 (DE-576)094056714 0306-4379 nnns volume:119 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-BBI GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.64 Datenbanken VZ 06.74 Informationssysteme VZ AR 119
spelling	10.1016/j.is.2023.102285 doi (DE-627)ELV06526391X (ELSEVIER)S0306-4379(23)00121-7 DE-627 ger DE-627 rda eng 070 VZ 54.64 bkl 06.74 bkl Guan, Wei verfasserin (orcid)0000-0002-8979-6847 aut AIMED: An automatic and incremental approach for business process model repair under concept drift 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Real-life business processes may change over time in response to new business requirements, market changes, new policies or regulations, etc., which is called concept drift in the data mining area. How to identify and deal with the concept drift problem is a significant challenge in business process mining. Currently, the research mainly focuses on the problems of concept drift detection. However, the proposed approaches do not intuitively explain how the process changes. In this paper, we provide an integrated framework whose core technique is an Automatic and Incremental approach for business process Model rEpair under concept Drift (AIMED). AIMED streamlines the functions of concept drift detection, localization and model repair. More specifically, when concept drift is detected, it updates the process model automatically by precisely localizing the sub-structure of the process model that concept drift affects and updating this sub-structure accordingly. Concept drift is explained intuitively by presenting the repaired model. In particular, AIMED can resist the noise that greatly affects the performance of current concept drift detection and process model repair techniques. The properties of AIMED are theoretically proven. We also conduct extensive experiments on synthetic logs as well as real-life logs. The experiment results show that AIMED outperforms the state-of-the-art methods in both concept drift detection and process model repair and works well even when there is noise in logs. Concept drift Process mining WF-net Process model repair Conformance checking Cao, Jian verfasserin aut Gu, Yang verfasserin aut Qian, Shiyou verfasserin aut Enthalten in Information systems Oxford [u.a.] : Pergamon Press, 1975 119 Online-Ressource (DE-627)320502864 (DE-600)2012447-8 (DE-576)094056714 0306-4379 nnns volume:119 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-BBI GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.64 Datenbanken VZ 06.74 Informationssysteme VZ AR 119
allfields_unstemmed	10.1016/j.is.2023.102285 doi (DE-627)ELV06526391X (ELSEVIER)S0306-4379(23)00121-7 DE-627 ger DE-627 rda eng 070 VZ 54.64 bkl 06.74 bkl Guan, Wei verfasserin (orcid)0000-0002-8979-6847 aut AIMED: An automatic and incremental approach for business process model repair under concept drift 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Real-life business processes may change over time in response to new business requirements, market changes, new policies or regulations, etc., which is called concept drift in the data mining area. How to identify and deal with the concept drift problem is a significant challenge in business process mining. Currently, the research mainly focuses on the problems of concept drift detection. However, the proposed approaches do not intuitively explain how the process changes. In this paper, we provide an integrated framework whose core technique is an Automatic and Incremental approach for business process Model rEpair under concept Drift (AIMED). AIMED streamlines the functions of concept drift detection, localization and model repair. More specifically, when concept drift is detected, it updates the process model automatically by precisely localizing the sub-structure of the process model that concept drift affects and updating this sub-structure accordingly. Concept drift is explained intuitively by presenting the repaired model. In particular, AIMED can resist the noise that greatly affects the performance of current concept drift detection and process model repair techniques. The properties of AIMED are theoretically proven. We also conduct extensive experiments on synthetic logs as well as real-life logs. The experiment results show that AIMED outperforms the state-of-the-art methods in both concept drift detection and process model repair and works well even when there is noise in logs. Concept drift Process mining WF-net Process model repair Conformance checking Cao, Jian verfasserin aut Gu, Yang verfasserin aut Qian, Shiyou verfasserin aut Enthalten in Information systems Oxford [u.a.] : Pergamon Press, 1975 119 Online-Ressource (DE-627)320502864 (DE-600)2012447-8 (DE-576)094056714 0306-4379 nnns volume:119 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-BBI GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.64 Datenbanken VZ 06.74 Informationssysteme VZ AR 119
allfieldsGer	10.1016/j.is.2023.102285 doi (DE-627)ELV06526391X (ELSEVIER)S0306-4379(23)00121-7 DE-627 ger DE-627 rda eng 070 VZ 54.64 bkl 06.74 bkl Guan, Wei verfasserin (orcid)0000-0002-8979-6847 aut AIMED: An automatic and incremental approach for business process model repair under concept drift 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Real-life business processes may change over time in response to new business requirements, market changes, new policies or regulations, etc., which is called concept drift in the data mining area. How to identify and deal with the concept drift problem is a significant challenge in business process mining. Currently, the research mainly focuses on the problems of concept drift detection. However, the proposed approaches do not intuitively explain how the process changes. In this paper, we provide an integrated framework whose core technique is an Automatic and Incremental approach for business process Model rEpair under concept Drift (AIMED). AIMED streamlines the functions of concept drift detection, localization and model repair. More specifically, when concept drift is detected, it updates the process model automatically by precisely localizing the sub-structure of the process model that concept drift affects and updating this sub-structure accordingly. Concept drift is explained intuitively by presenting the repaired model. In particular, AIMED can resist the noise that greatly affects the performance of current concept drift detection and process model repair techniques. The properties of AIMED are theoretically proven. We also conduct extensive experiments on synthetic logs as well as real-life logs. The experiment results show that AIMED outperforms the state-of-the-art methods in both concept drift detection and process model repair and works well even when there is noise in logs. Concept drift Process mining WF-net Process model repair Conformance checking Cao, Jian verfasserin aut Gu, Yang verfasserin aut Qian, Shiyou verfasserin aut Enthalten in Information systems Oxford [u.a.] : Pergamon Press, 1975 119 Online-Ressource (DE-627)320502864 (DE-600)2012447-8 (DE-576)094056714 0306-4379 nnns volume:119 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-BBI GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.64 Datenbanken VZ 06.74 Informationssysteme VZ AR 119
allfieldsSound	10.1016/j.is.2023.102285 doi (DE-627)ELV06526391X (ELSEVIER)S0306-4379(23)00121-7 DE-627 ger DE-627 rda eng 070 VZ 54.64 bkl 06.74 bkl Guan, Wei verfasserin (orcid)0000-0002-8979-6847 aut AIMED: An automatic and incremental approach for business process model repair under concept drift 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Real-life business processes may change over time in response to new business requirements, market changes, new policies or regulations, etc., which is called concept drift in the data mining area. How to identify and deal with the concept drift problem is a significant challenge in business process mining. Currently, the research mainly focuses on the problems of concept drift detection. However, the proposed approaches do not intuitively explain how the process changes. In this paper, we provide an integrated framework whose core technique is an Automatic and Incremental approach for business process Model rEpair under concept Drift (AIMED). AIMED streamlines the functions of concept drift detection, localization and model repair. More specifically, when concept drift is detected, it updates the process model automatically by precisely localizing the sub-structure of the process model that concept drift affects and updating this sub-structure accordingly. Concept drift is explained intuitively by presenting the repaired model. In particular, AIMED can resist the noise that greatly affects the performance of current concept drift detection and process model repair techniques. The properties of AIMED are theoretically proven. We also conduct extensive experiments on synthetic logs as well as real-life logs. The experiment results show that AIMED outperforms the state-of-the-art methods in both concept drift detection and process model repair and works well even when there is noise in logs. Concept drift Process mining WF-net Process model repair Conformance checking Cao, Jian verfasserin aut Gu, Yang verfasserin aut Qian, Shiyou verfasserin aut Enthalten in Information systems Oxford [u.a.] : Pergamon Press, 1975 119 Online-Ressource (DE-627)320502864 (DE-600)2012447-8 (DE-576)094056714 0306-4379 nnns volume:119 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-BBI GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.64 Datenbanken VZ 06.74 Informationssysteme VZ AR 119
language	English
source	Enthalten in Information systems 119 volume:119
sourceStr	Enthalten in Information systems 119 volume:119
format_phy_str_mv	Article
bklname	Datenbanken Informationssysteme
institution	findex.gbv.de
topic_facet	Concept drift Process mining WF-net Process model repair Conformance checking
dewey-raw	070
isfreeaccess_bool	false
container_title	Information systems
authorswithroles_txt_mv	Guan, Wei @@aut@@ Cao, Jian @@aut@@ Gu, Yang @@aut@@ Qian, Shiyou @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	320502864
dewey-sort	270
id	ELV06526391X
language_de	englisch
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author	Guan, Wei
spellingShingle	Guan, Wei ddc 070 bkl 54.64 bkl 06.74 misc Concept drift misc Process mining misc WF-net misc Process model repair misc Conformance checking AIMED: An automatic and incremental approach for business process model repair under concept drift
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topic_title	070 VZ 54.64 bkl 06.74 bkl AIMED: An automatic and incremental approach for business process model repair under concept drift Concept drift Process mining WF-net Process model repair Conformance checking
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title	AIMED: An automatic and incremental approach for business process model repair under concept drift
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title_full	AIMED: An automatic and incremental approach for business process model repair under concept drift
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title_sort	aimed: an automatic and incremental approach for business process model repair under concept drift
title_auth	AIMED: An automatic and incremental approach for business process model repair under concept drift
abstract	Real-life business processes may change over time in response to new business requirements, market changes, new policies or regulations, etc., which is called concept drift in the data mining area. How to identify and deal with the concept drift problem is a significant challenge in business process mining. Currently, the research mainly focuses on the problems of concept drift detection. However, the proposed approaches do not intuitively explain how the process changes. In this paper, we provide an integrated framework whose core technique is an Automatic and Incremental approach for business process Model rEpair under concept Drift (AIMED). AIMED streamlines the functions of concept drift detection, localization and model repair. More specifically, when concept drift is detected, it updates the process model automatically by precisely localizing the sub-structure of the process model that concept drift affects and updating this sub-structure accordingly. Concept drift is explained intuitively by presenting the repaired model. In particular, AIMED can resist the noise that greatly affects the performance of current concept drift detection and process model repair techniques. The properties of AIMED are theoretically proven. We also conduct extensive experiments on synthetic logs as well as real-life logs. The experiment results show that AIMED outperforms the state-of-the-art methods in both concept drift detection and process model repair and works well even when there is noise in logs.
abstractGer	Real-life business processes may change over time in response to new business requirements, market changes, new policies or regulations, etc., which is called concept drift in the data mining area. How to identify and deal with the concept drift problem is a significant challenge in business process mining. Currently, the research mainly focuses on the problems of concept drift detection. However, the proposed approaches do not intuitively explain how the process changes. In this paper, we provide an integrated framework whose core technique is an Automatic and Incremental approach for business process Model rEpair under concept Drift (AIMED). AIMED streamlines the functions of concept drift detection, localization and model repair. More specifically, when concept drift is detected, it updates the process model automatically by precisely localizing the sub-structure of the process model that concept drift affects and updating this sub-structure accordingly. Concept drift is explained intuitively by presenting the repaired model. In particular, AIMED can resist the noise that greatly affects the performance of current concept drift detection and process model repair techniques. The properties of AIMED are theoretically proven. We also conduct extensive experiments on synthetic logs as well as real-life logs. The experiment results show that AIMED outperforms the state-of-the-art methods in both concept drift detection and process model repair and works well even when there is noise in logs.
abstract_unstemmed	Real-life business processes may change over time in response to new business requirements, market changes, new policies or regulations, etc., which is called concept drift in the data mining area. How to identify and deal with the concept drift problem is a significant challenge in business process mining. Currently, the research mainly focuses on the problems of concept drift detection. However, the proposed approaches do not intuitively explain how the process changes. In this paper, we provide an integrated framework whose core technique is an Automatic and Incremental approach for business process Model rEpair under concept Drift (AIMED). AIMED streamlines the functions of concept drift detection, localization and model repair. More specifically, when concept drift is detected, it updates the process model automatically by precisely localizing the sub-structure of the process model that concept drift affects and updating this sub-structure accordingly. Concept drift is explained intuitively by presenting the repaired model. In particular, AIMED can resist the noise that greatly affects the performance of current concept drift detection and process model repair techniques. The properties of AIMED are theoretically proven. We also conduct extensive experiments on synthetic logs as well as real-life logs. The experiment results show that AIMED outperforms the state-of-the-art methods in both concept drift detection and process model repair and works well even when there is noise in logs.
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title_short	AIMED: An automatic and incremental approach for business process model repair under concept drift
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author2	Cao, Jian Gu, Yang Qian, Shiyou
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up_date	2024-07-06T22:25:10.812Z
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