A composite machine-learning-based framework for supporting low-level event logs to high-level business process model activities mappings enhanced by flexible BPMN model translation
Abstract Process mining is an emerging discipline that aims to analyze business processes using event data logged by IT systems. In process mining, the focus is on how to effectively and efficiently predict the next process/trace to be activated among all the possible processes/traces that are avail...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Al-Ali, H. [verfasserIn] Cuzzocrea, A. [verfasserIn] Damiani, E. [verfasserIn] Mizouni, R. [verfasserIn] Tello, G. [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2019 |
|---|
| Schlagwörter: |
Business process management systems |
|---|
| Übergeordnetes Werk: |
Enthalten in: Soft Computing - Springer-Verlag, 2003, 24(2019), 10 vom: 27. Sept., Seite 7557-7578 |
|---|---|
| Übergeordnetes Werk: |
volume:24 ; year:2019 ; number:10 ; day:27 ; month:09 ; pages:7557-7578 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00500-019-04385-6 |
|---|
| Katalog-ID: |
SPR039360725 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | SPR039360725 | ||
| 003 | DE-627 | ||
| 005 | 20201126003138.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 201007s2019 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s00500-019-04385-6 |2 doi | |
| 035 | |a (DE-627)SPR039360725 | ||
| 035 | |a (SPR)s00500-019-04385-6-e | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Al-Ali, H. |e verfasserin |4 aut | |
| 245 | 1 | 2 | |a A composite machine-learning-based framework for supporting low-level event logs to high-level business process model activities mappings enhanced by flexible BPMN model translation |
| 264 | 1 | |c 2019 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a Abstract Process mining is an emerging discipline that aims to analyze business processes using event data logged by IT systems. In process mining, the focus is on how to effectively and efficiently predict the next process/trace to be activated among all the possible processes/traces that are available in the process schema (usually modeled as a graph). Most of the existing process mining techniques assume that there is a one-to-one mapping between process model activities and the events that are recorded during process execution. However, event logs and process model activities are at different level of granularity. In this paper, we present a machine-learning-based approach to map low-level event logs to high-level activities. With this work, we can bridge the abstraction levels when the high-level labels of the low-level events are not available. The proposed approach consists of two main phases: automatic labeling and machine-learning-based classification. In automatic labeling, a modified k-prototypes clustering approach has been used in order to obtain the labeled examples. Then, in the second phase, we trained different ML classifiers using the obtained labeled examples. Since, in real-life applications and systems, business processes are expressed according to the Business Process Model and Notation (BPMN) format, we improve our proposed framework by means of an innovative, flexible BPMN model translation methodology that acts at the first phase. We demonstrate the applicability of our proposed framework using two case studies with real-world event logs, and provide its experimental assessment and analysis. | ||
| 650 | 4 | |a Business process management systems |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Business process intelligence |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Low-level event logs |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a High-level business process model activities |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a BPMN model translation |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Cuzzocrea, A. |e verfasserin |4 aut | |
| 700 | 1 | |a Damiani, E. |e verfasserin |4 aut | |
| 700 | 1 | |a Mizouni, R. |e verfasserin |4 aut | |
| 700 | 1 | |a Tello, G. |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Soft Computing |d Springer-Verlag, 2003 |g 24(2019), 10 vom: 27. Sept., Seite 7557-7578 |w (DE-627)SPR006469531 |7 nnns |
| 773 | 1 | 8 | |g volume:24 |g year:2019 |g number:10 |g day:27 |g month:09 |g pages:7557-7578 |
| 856 | 4 | 0 | |u https://dx.doi.org/10.1007/s00500-019-04385-6 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_SPRINGER | ||
| 951 | |a AR | ||
| 952 | |d 24 |j 2019 |e 10 |b 27 |c 09 |h 7557-7578 | ||
| author_variant |
h a a haa a c ac e d ed r m rm g t gt |
|---|---|
| matchkey_str |
alalihcuzzocreaadamianiemizounirtellog:2019----:cmoieahnlannbsdrmwrfruprigolvlvnlgthglvluiesrcsmdlciiisapn |
| hierarchy_sort_str |
2019 |
| publishDate |
2019 |
| allfields |
10.1007/s00500-019-04385-6 doi (DE-627)SPR039360725 (SPR)s00500-019-04385-6-e DE-627 ger DE-627 rakwb eng Al-Ali, H. verfasserin aut A composite machine-learning-based framework for supporting low-level event logs to high-level business process model activities mappings enhanced by flexible BPMN model translation 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Process mining is an emerging discipline that aims to analyze business processes using event data logged by IT systems. In process mining, the focus is on how to effectively and efficiently predict the next process/trace to be activated among all the possible processes/traces that are available in the process schema (usually modeled as a graph). Most of the existing process mining techniques assume that there is a one-to-one mapping between process model activities and the events that are recorded during process execution. However, event logs and process model activities are at different level of granularity. In this paper, we present a machine-learning-based approach to map low-level event logs to high-level activities. With this work, we can bridge the abstraction levels when the high-level labels of the low-level events are not available. The proposed approach consists of two main phases: automatic labeling and machine-learning-based classification. In automatic labeling, a modified k-prototypes clustering approach has been used in order to obtain the labeled examples. Then, in the second phase, we trained different ML classifiers using the obtained labeled examples. Since, in real-life applications and systems, business processes are expressed according to the Business Process Model and Notation (BPMN) format, we improve our proposed framework by means of an innovative, flexible BPMN model translation methodology that acts at the first phase. We demonstrate the applicability of our proposed framework using two case studies with real-world event logs, and provide its experimental assessment and analysis. Business process management systems (dpeaa)DE-He213 Business process intelligence (dpeaa)DE-He213 Low-level event logs (dpeaa)DE-He213 High-level business process model activities (dpeaa)DE-He213 BPMN model translation (dpeaa)DE-He213 Cuzzocrea, A. verfasserin aut Damiani, E. verfasserin aut Mizouni, R. verfasserin aut Tello, G. verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 24(2019), 10 vom: 27. Sept., Seite 7557-7578 (DE-627)SPR006469531 nnns volume:24 year:2019 number:10 day:27 month:09 pages:7557-7578 https://dx.doi.org/10.1007/s00500-019-04385-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 24 2019 10 27 09 7557-7578 |
| spelling |
10.1007/s00500-019-04385-6 doi (DE-627)SPR039360725 (SPR)s00500-019-04385-6-e DE-627 ger DE-627 rakwb eng Al-Ali, H. verfasserin aut A composite machine-learning-based framework for supporting low-level event logs to high-level business process model activities mappings enhanced by flexible BPMN model translation 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Process mining is an emerging discipline that aims to analyze business processes using event data logged by IT systems. In process mining, the focus is on how to effectively and efficiently predict the next process/trace to be activated among all the possible processes/traces that are available in the process schema (usually modeled as a graph). Most of the existing process mining techniques assume that there is a one-to-one mapping between process model activities and the events that are recorded during process execution. However, event logs and process model activities are at different level of granularity. In this paper, we present a machine-learning-based approach to map low-level event logs to high-level activities. With this work, we can bridge the abstraction levels when the high-level labels of the low-level events are not available. The proposed approach consists of two main phases: automatic labeling and machine-learning-based classification. In automatic labeling, a modified k-prototypes clustering approach has been used in order to obtain the labeled examples. Then, in the second phase, we trained different ML classifiers using the obtained labeled examples. Since, in real-life applications and systems, business processes are expressed according to the Business Process Model and Notation (BPMN) format, we improve our proposed framework by means of an innovative, flexible BPMN model translation methodology that acts at the first phase. We demonstrate the applicability of our proposed framework using two case studies with real-world event logs, and provide its experimental assessment and analysis. Business process management systems (dpeaa)DE-He213 Business process intelligence (dpeaa)DE-He213 Low-level event logs (dpeaa)DE-He213 High-level business process model activities (dpeaa)DE-He213 BPMN model translation (dpeaa)DE-He213 Cuzzocrea, A. verfasserin aut Damiani, E. verfasserin aut Mizouni, R. verfasserin aut Tello, G. verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 24(2019), 10 vom: 27. Sept., Seite 7557-7578 (DE-627)SPR006469531 nnns volume:24 year:2019 number:10 day:27 month:09 pages:7557-7578 https://dx.doi.org/10.1007/s00500-019-04385-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 24 2019 10 27 09 7557-7578 |
| allfields_unstemmed |
10.1007/s00500-019-04385-6 doi (DE-627)SPR039360725 (SPR)s00500-019-04385-6-e DE-627 ger DE-627 rakwb eng Al-Ali, H. verfasserin aut A composite machine-learning-based framework for supporting low-level event logs to high-level business process model activities mappings enhanced by flexible BPMN model translation 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Process mining is an emerging discipline that aims to analyze business processes using event data logged by IT systems. In process mining, the focus is on how to effectively and efficiently predict the next process/trace to be activated among all the possible processes/traces that are available in the process schema (usually modeled as a graph). Most of the existing process mining techniques assume that there is a one-to-one mapping between process model activities and the events that are recorded during process execution. However, event logs and process model activities are at different level of granularity. In this paper, we present a machine-learning-based approach to map low-level event logs to high-level activities. With this work, we can bridge the abstraction levels when the high-level labels of the low-level events are not available. The proposed approach consists of two main phases: automatic labeling and machine-learning-based classification. In automatic labeling, a modified k-prototypes clustering approach has been used in order to obtain the labeled examples. Then, in the second phase, we trained different ML classifiers using the obtained labeled examples. Since, in real-life applications and systems, business processes are expressed according to the Business Process Model and Notation (BPMN) format, we improve our proposed framework by means of an innovative, flexible BPMN model translation methodology that acts at the first phase. We demonstrate the applicability of our proposed framework using two case studies with real-world event logs, and provide its experimental assessment and analysis. Business process management systems (dpeaa)DE-He213 Business process intelligence (dpeaa)DE-He213 Low-level event logs (dpeaa)DE-He213 High-level business process model activities (dpeaa)DE-He213 BPMN model translation (dpeaa)DE-He213 Cuzzocrea, A. verfasserin aut Damiani, E. verfasserin aut Mizouni, R. verfasserin aut Tello, G. verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 24(2019), 10 vom: 27. Sept., Seite 7557-7578 (DE-627)SPR006469531 nnns volume:24 year:2019 number:10 day:27 month:09 pages:7557-7578 https://dx.doi.org/10.1007/s00500-019-04385-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 24 2019 10 27 09 7557-7578 |
| allfieldsGer |
10.1007/s00500-019-04385-6 doi (DE-627)SPR039360725 (SPR)s00500-019-04385-6-e DE-627 ger DE-627 rakwb eng Al-Ali, H. verfasserin aut A composite machine-learning-based framework for supporting low-level event logs to high-level business process model activities mappings enhanced by flexible BPMN model translation 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Process mining is an emerging discipline that aims to analyze business processes using event data logged by IT systems. In process mining, the focus is on how to effectively and efficiently predict the next process/trace to be activated among all the possible processes/traces that are available in the process schema (usually modeled as a graph). Most of the existing process mining techniques assume that there is a one-to-one mapping between process model activities and the events that are recorded during process execution. However, event logs and process model activities are at different level of granularity. In this paper, we present a machine-learning-based approach to map low-level event logs to high-level activities. With this work, we can bridge the abstraction levels when the high-level labels of the low-level events are not available. The proposed approach consists of two main phases: automatic labeling and machine-learning-based classification. In automatic labeling, a modified k-prototypes clustering approach has been used in order to obtain the labeled examples. Then, in the second phase, we trained different ML classifiers using the obtained labeled examples. Since, in real-life applications and systems, business processes are expressed according to the Business Process Model and Notation (BPMN) format, we improve our proposed framework by means of an innovative, flexible BPMN model translation methodology that acts at the first phase. We demonstrate the applicability of our proposed framework using two case studies with real-world event logs, and provide its experimental assessment and analysis. Business process management systems (dpeaa)DE-He213 Business process intelligence (dpeaa)DE-He213 Low-level event logs (dpeaa)DE-He213 High-level business process model activities (dpeaa)DE-He213 BPMN model translation (dpeaa)DE-He213 Cuzzocrea, A. verfasserin aut Damiani, E. verfasserin aut Mizouni, R. verfasserin aut Tello, G. verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 24(2019), 10 vom: 27. Sept., Seite 7557-7578 (DE-627)SPR006469531 nnns volume:24 year:2019 number:10 day:27 month:09 pages:7557-7578 https://dx.doi.org/10.1007/s00500-019-04385-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 24 2019 10 27 09 7557-7578 |
| allfieldsSound |
10.1007/s00500-019-04385-6 doi (DE-627)SPR039360725 (SPR)s00500-019-04385-6-e DE-627 ger DE-627 rakwb eng Al-Ali, H. verfasserin aut A composite machine-learning-based framework for supporting low-level event logs to high-level business process model activities mappings enhanced by flexible BPMN model translation 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Process mining is an emerging discipline that aims to analyze business processes using event data logged by IT systems. In process mining, the focus is on how to effectively and efficiently predict the next process/trace to be activated among all the possible processes/traces that are available in the process schema (usually modeled as a graph). Most of the existing process mining techniques assume that there is a one-to-one mapping between process model activities and the events that are recorded during process execution. However, event logs and process model activities are at different level of granularity. In this paper, we present a machine-learning-based approach to map low-level event logs to high-level activities. With this work, we can bridge the abstraction levels when the high-level labels of the low-level events are not available. The proposed approach consists of two main phases: automatic labeling and machine-learning-based classification. In automatic labeling, a modified k-prototypes clustering approach has been used in order to obtain the labeled examples. Then, in the second phase, we trained different ML classifiers using the obtained labeled examples. Since, in real-life applications and systems, business processes are expressed according to the Business Process Model and Notation (BPMN) format, we improve our proposed framework by means of an innovative, flexible BPMN model translation methodology that acts at the first phase. We demonstrate the applicability of our proposed framework using two case studies with real-world event logs, and provide its experimental assessment and analysis. Business process management systems (dpeaa)DE-He213 Business process intelligence (dpeaa)DE-He213 Low-level event logs (dpeaa)DE-He213 High-level business process model activities (dpeaa)DE-He213 BPMN model translation (dpeaa)DE-He213 Cuzzocrea, A. verfasserin aut Damiani, E. verfasserin aut Mizouni, R. verfasserin aut Tello, G. verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 24(2019), 10 vom: 27. Sept., Seite 7557-7578 (DE-627)SPR006469531 nnns volume:24 year:2019 number:10 day:27 month:09 pages:7557-7578 https://dx.doi.org/10.1007/s00500-019-04385-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 24 2019 10 27 09 7557-7578 |
| language |
English |
| source |
Enthalten in Soft Computing 24(2019), 10 vom: 27. Sept., Seite 7557-7578 volume:24 year:2019 number:10 day:27 month:09 pages:7557-7578 |
| sourceStr |
Enthalten in Soft Computing 24(2019), 10 vom: 27. Sept., Seite 7557-7578 volume:24 year:2019 number:10 day:27 month:09 pages:7557-7578 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Business process management systems Business process intelligence Low-level event logs High-level business process model activities BPMN model translation |
| isfreeaccess_bool |
false |
| container_title |
Soft Computing |
| authorswithroles_txt_mv |
Al-Ali, H. @@aut@@ Cuzzocrea, A. @@aut@@ Damiani, E. @@aut@@ Mizouni, R. @@aut@@ Tello, G. @@aut@@ |
| publishDateDaySort_date |
2019-09-27T00:00:00Z |
| hierarchy_top_id |
SPR006469531 |
| id |
SPR039360725 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR039360725</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20201126003138.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2019 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00500-019-04385-6</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR039360725</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00500-019-04385-6-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Al-Ali, H.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A composite machine-learning-based framework for supporting low-level event logs to high-level business process model activities mappings enhanced by flexible BPMN model translation</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Process mining is an emerging discipline that aims to analyze business processes using event data logged by IT systems. In process mining, the focus is on how to effectively and efficiently predict the next process/trace to be activated among all the possible processes/traces that are available in the process schema (usually modeled as a graph). Most of the existing process mining techniques assume that there is a one-to-one mapping between process model activities and the events that are recorded during process execution. However, event logs and process model activities are at different level of granularity. In this paper, we present a machine-learning-based approach to map low-level event logs to high-level activities. With this work, we can bridge the abstraction levels when the high-level labels of the low-level events are not available. The proposed approach consists of two main phases: automatic labeling and machine-learning-based classification. In automatic labeling, a modified k-prototypes clustering approach has been used in order to obtain the labeled examples. Then, in the second phase, we trained different ML classifiers using the obtained labeled examples. Since, in real-life applications and systems, business processes are expressed according to the Business Process Model and Notation (BPMN) format, we improve our proposed framework by means of an innovative, flexible BPMN model translation methodology that acts at the first phase. We demonstrate the applicability of our proposed framework using two case studies with real-world event logs, and provide its experimental assessment and analysis.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Business process management systems</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Business process intelligence</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Low-level event logs</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">High-level business process model activities</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">BPMN model translation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Cuzzocrea, A.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Damiani, E.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mizouni, R.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tello, G.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Soft Computing</subfield><subfield code="d">Springer-Verlag, 2003</subfield><subfield code="g">24(2019), 10 vom: 27. Sept., Seite 7557-7578</subfield><subfield code="w">(DE-627)SPR006469531</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:24</subfield><subfield code="g">year:2019</subfield><subfield code="g">number:10</subfield><subfield code="g">day:27</subfield><subfield code="g">month:09</subfield><subfield code="g">pages:7557-7578</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s00500-019-04385-6</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">24</subfield><subfield code="j">2019</subfield><subfield code="e">10</subfield><subfield code="b">27</subfield><subfield code="c">09</subfield><subfield code="h">7557-7578</subfield></datafield></record></collection>
|
| author |
Al-Ali, H. |
| spellingShingle |
Al-Ali, H. misc Business process management systems misc Business process intelligence misc Low-level event logs misc High-level business process model activities misc BPMN model translation A composite machine-learning-based framework for supporting low-level event logs to high-level business process model activities mappings enhanced by flexible BPMN model translation |
| authorStr |
Al-Ali, H. |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)SPR006469531 |
| format |
electronic Article |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut aut |
| collection |
springer |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
A composite machine-learning-based framework for supporting low-level event logs to high-level business process model activities mappings enhanced by flexible BPMN model translation Business process management systems (dpeaa)DE-He213 Business process intelligence (dpeaa)DE-He213 Low-level event logs (dpeaa)DE-He213 High-level business process model activities (dpeaa)DE-He213 BPMN model translation (dpeaa)DE-He213 |
| topic |
misc Business process management systems misc Business process intelligence misc Low-level event logs misc High-level business process model activities misc BPMN model translation |
| topic_unstemmed |
misc Business process management systems misc Business process intelligence misc Low-level event logs misc High-level business process model activities misc BPMN model translation |
| topic_browse |
misc Business process management systems misc Business process intelligence misc Low-level event logs misc High-level business process model activities misc BPMN model translation |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
cr |
| hierarchy_parent_title |
Soft Computing |
| hierarchy_parent_id |
SPR006469531 |
| hierarchy_top_title |
Soft Computing |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)SPR006469531 |
| title |
A composite machine-learning-based framework for supporting low-level event logs to high-level business process model activities mappings enhanced by flexible BPMN model translation |
| ctrlnum |
(DE-627)SPR039360725 (SPR)s00500-019-04385-6-e |
| title_full |
A composite machine-learning-based framework for supporting low-level event logs to high-level business process model activities mappings enhanced by flexible BPMN model translation |
| author_sort |
Al-Ali, H. |
| journal |
Soft Computing |
| journalStr |
Soft Computing |
| lang_code |
eng |
| isOA_bool |
false |
| recordtype |
marc |
| publishDateSort |
2019 |
| contenttype_str_mv |
txt |
| container_start_page |
7557 |
| author_browse |
Al-Ali, H. Cuzzocrea, A. Damiani, E. Mizouni, R. Tello, G. |
| container_volume |
24 |
| format_se |
Elektronische Aufsätze |
| author-letter |
Al-Ali, H. |
| doi_str_mv |
10.1007/s00500-019-04385-6 |
| author2-role |
verfasserin |
| title_sort |
composite machine-learning-based framework for supporting low-level event logs to high-level business process model activities mappings enhanced by flexible bpmn model translation |
| title_auth |
A composite machine-learning-based framework for supporting low-level event logs to high-level business process model activities mappings enhanced by flexible BPMN model translation |
| abstract |
Abstract Process mining is an emerging discipline that aims to analyze business processes using event data logged by IT systems. In process mining, the focus is on how to effectively and efficiently predict the next process/trace to be activated among all the possible processes/traces that are available in the process schema (usually modeled as a graph). Most of the existing process mining techniques assume that there is a one-to-one mapping between process model activities and the events that are recorded during process execution. However, event logs and process model activities are at different level of granularity. In this paper, we present a machine-learning-based approach to map low-level event logs to high-level activities. With this work, we can bridge the abstraction levels when the high-level labels of the low-level events are not available. The proposed approach consists of two main phases: automatic labeling and machine-learning-based classification. In automatic labeling, a modified k-prototypes clustering approach has been used in order to obtain the labeled examples. Then, in the second phase, we trained different ML classifiers using the obtained labeled examples. Since, in real-life applications and systems, business processes are expressed according to the Business Process Model and Notation (BPMN) format, we improve our proposed framework by means of an innovative, flexible BPMN model translation methodology that acts at the first phase. We demonstrate the applicability of our proposed framework using two case studies with real-world event logs, and provide its experimental assessment and analysis. |
| abstractGer |
Abstract Process mining is an emerging discipline that aims to analyze business processes using event data logged by IT systems. In process mining, the focus is on how to effectively and efficiently predict the next process/trace to be activated among all the possible processes/traces that are available in the process schema (usually modeled as a graph). Most of the existing process mining techniques assume that there is a one-to-one mapping between process model activities and the events that are recorded during process execution. However, event logs and process model activities are at different level of granularity. In this paper, we present a machine-learning-based approach to map low-level event logs to high-level activities. With this work, we can bridge the abstraction levels when the high-level labels of the low-level events are not available. The proposed approach consists of two main phases: automatic labeling and machine-learning-based classification. In automatic labeling, a modified k-prototypes clustering approach has been used in order to obtain the labeled examples. Then, in the second phase, we trained different ML classifiers using the obtained labeled examples. Since, in real-life applications and systems, business processes are expressed according to the Business Process Model and Notation (BPMN) format, we improve our proposed framework by means of an innovative, flexible BPMN model translation methodology that acts at the first phase. We demonstrate the applicability of our proposed framework using two case studies with real-world event logs, and provide its experimental assessment and analysis. |
| abstract_unstemmed |
Abstract Process mining is an emerging discipline that aims to analyze business processes using event data logged by IT systems. In process mining, the focus is on how to effectively and efficiently predict the next process/trace to be activated among all the possible processes/traces that are available in the process schema (usually modeled as a graph). Most of the existing process mining techniques assume that there is a one-to-one mapping between process model activities and the events that are recorded during process execution. However, event logs and process model activities are at different level of granularity. In this paper, we present a machine-learning-based approach to map low-level event logs to high-level activities. With this work, we can bridge the abstraction levels when the high-level labels of the low-level events are not available. The proposed approach consists of two main phases: automatic labeling and machine-learning-based classification. In automatic labeling, a modified k-prototypes clustering approach has been used in order to obtain the labeled examples. Then, in the second phase, we trained different ML classifiers using the obtained labeled examples. Since, in real-life applications and systems, business processes are expressed according to the Business Process Model and Notation (BPMN) format, we improve our proposed framework by means of an innovative, flexible BPMN model translation methodology that acts at the first phase. We demonstrate the applicability of our proposed framework using two case studies with real-world event logs, and provide its experimental assessment and analysis. |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER |
| container_issue |
10 |
| title_short |
A composite machine-learning-based framework for supporting low-level event logs to high-level business process model activities mappings enhanced by flexible BPMN model translation |
| url |
https://dx.doi.org/10.1007/s00500-019-04385-6 |
| remote_bool |
true |
| author2 |
Cuzzocrea, A. Damiani, E. Mizouni, R. Tello, G. |
| author2Str |
Cuzzocrea, A. Damiani, E. Mizouni, R. Tello, G. |
| ppnlink |
SPR006469531 |
| mediatype_str_mv |
c |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s00500-019-04385-6 |
| up_date |
2024-07-03T23:32:19.267Z |
| _version_ |
1803602665690628096 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR039360725</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20201126003138.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2019 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00500-019-04385-6</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR039360725</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00500-019-04385-6-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Al-Ali, H.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A composite machine-learning-based framework for supporting low-level event logs to high-level business process model activities mappings enhanced by flexible BPMN model translation</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Process mining is an emerging discipline that aims to analyze business processes using event data logged by IT systems. In process mining, the focus is on how to effectively and efficiently predict the next process/trace to be activated among all the possible processes/traces that are available in the process schema (usually modeled as a graph). Most of the existing process mining techniques assume that there is a one-to-one mapping between process model activities and the events that are recorded during process execution. However, event logs and process model activities are at different level of granularity. In this paper, we present a machine-learning-based approach to map low-level event logs to high-level activities. With this work, we can bridge the abstraction levels when the high-level labels of the low-level events are not available. The proposed approach consists of two main phases: automatic labeling and machine-learning-based classification. In automatic labeling, a modified k-prototypes clustering approach has been used in order to obtain the labeled examples. Then, in the second phase, we trained different ML classifiers using the obtained labeled examples. Since, in real-life applications and systems, business processes are expressed according to the Business Process Model and Notation (BPMN) format, we improve our proposed framework by means of an innovative, flexible BPMN model translation methodology that acts at the first phase. We demonstrate the applicability of our proposed framework using two case studies with real-world event logs, and provide its experimental assessment and analysis.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Business process management systems</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Business process intelligence</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Low-level event logs</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">High-level business process model activities</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">BPMN model translation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Cuzzocrea, A.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Damiani, E.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mizouni, R.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tello, G.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Soft Computing</subfield><subfield code="d">Springer-Verlag, 2003</subfield><subfield code="g">24(2019), 10 vom: 27. Sept., Seite 7557-7578</subfield><subfield code="w">(DE-627)SPR006469531</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:24</subfield><subfield code="g">year:2019</subfield><subfield code="g">number:10</subfield><subfield code="g">day:27</subfield><subfield code="g">month:09</subfield><subfield code="g">pages:7557-7578</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s00500-019-04385-6</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">24</subfield><subfield code="j">2019</subfield><subfield code="e">10</subfield><subfield code="b">27</subfield><subfield code="c">09</subfield><subfield code="h">7557-7578</subfield></datafield></record></collection>
|
| score |
7.4019346 |