Data Preprocessing Method and API for Mining Processes from Cloud-Based Application Event Logs

Process mining (PM) exploits event logs to obtain meaningful information about the processes that produced them. As the number of applications developed on cloud infrastructures is increasing, it becomes important to study and discover their underlying processes. However, many current PM technologie...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Najah Mary El-Gharib [verfasserIn] Daniel Amyot [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	API clickstream cloud applications process mining scripting

Übergeordnetes Werk:	In: Algorithms - MDPI AG, 2008, 15(2022), 6, p 180
Übergeordnetes Werk:	volume:15 ; year:2022 ; number:6, p 180

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/a15060180

Katalog-ID:	DOAJ044203950

Internformat


LEADER	01000caa a22002652 4500
001	DOAJ044203950
003	DE-627
005	20240414210410.0
007	cr uuu---uuuuu
008	230227s2022 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.3390/a15060180 \|2 doi
035			\|a (DE-627)DOAJ044203950
035			\|a (DE-599)DOAJ9156af8c17e84bb28fec871326c2188b
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
050		0	\|a T55.4-60.8
050		0	\|a QA75.5-76.95
100	0		\|a Najah Mary El-Gharib \|e verfasserin \|4 aut
245	1	0	\|a Data Preprocessing Method and API for Mining Processes from Cloud-Based Application Event Logs
264		1	\|c 2022
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a Process mining (PM) exploits event logs to obtain meaningful information about the processes that produced them. As the number of applications developed on cloud infrastructures is increasing, it becomes important to study and discover their underlying processes. However, many current PM technologies face challenges in dealing with complex and large event logs from cloud applications, especially when they have little structure (e.g., clickstreams). By using Design Science Research, this paper introduces a new method, called cloud pattern API-process mining (CPA-PM), which enables the discovery and analysis of cloud-based application processes using PM in a way that addresses many of these challenges. CPA-PM exploits a new application programming interface, with an R implementation, for creating repeatable scripts that preprocess event logs collected from such applications. Applying CPA-PM to a case with real and evolving event logs related to the trial process of a software-as-a-service cloud application led to useful analyses and insights, with reusable scripts. CPA-PM helps producing executable scripts for filtering event logs from clickstream and cloud-based applications, where the scripts can be used in pipelines while minimizing the need for error-prone and time-consuming manual filtering.
650		4	\|a API
650		4	\|a clickstream
650		4	\|a cloud applications
650		4	\|a process mining
650		4	\|a scripting
653		0	\|a Industrial engineering. Management engineering
653		0	\|a Electronic computers. Computer science
700	0		\|a Daniel Amyot \|e verfasserin \|4 aut
773	0	8	\|i In \|t Algorithms \|d MDPI AG, 2008 \|g 15(2022), 6, p 180 \|w (DE-627)581036506 \|w (DE-600)2455149-1 \|x 19994893 \|7 nnns
773	1	8	\|g volume:15 \|g year:2022 \|g number:6, p 180
856	4	0	\|u https://doi.org/10.3390/a15060180 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/9156af8c17e84bb28fec871326c2188b \|z kostenfrei
856	4	0	\|u https://www.mdpi.com/1999-4893/15/6/180 \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/1999-4893 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_206
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 15 \|j 2022 \|e 6, p 180

Indexfelder

author_variant	n m e g nmeg d a da
matchkey_str	article:19994893:2022----::aarpoesnmtoadpfriigrcsefocodae
hierarchy_sort_str	2022
callnumber-subject-code	T
publishDate	2022
allfields	10.3390/a15060180 doi (DE-627)DOAJ044203950 (DE-599)DOAJ9156af8c17e84bb28fec871326c2188b DE-627 ger DE-627 rakwb eng T55.4-60.8 QA75.5-76.95 Najah Mary El-Gharib verfasserin aut Data Preprocessing Method and API for Mining Processes from Cloud-Based Application Event Logs 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Process mining (PM) exploits event logs to obtain meaningful information about the processes that produced them. As the number of applications developed on cloud infrastructures is increasing, it becomes important to study and discover their underlying processes. However, many current PM technologies face challenges in dealing with complex and large event logs from cloud applications, especially when they have little structure (e.g., clickstreams). By using Design Science Research, this paper introduces a new method, called cloud pattern API-process mining (CPA-PM), which enables the discovery and analysis of cloud-based application processes using PM in a way that addresses many of these challenges. CPA-PM exploits a new application programming interface, with an R implementation, for creating repeatable scripts that preprocess event logs collected from such applications. Applying CPA-PM to a case with real and evolving event logs related to the trial process of a software-as-a-service cloud application led to useful analyses and insights, with reusable scripts. CPA-PM helps producing executable scripts for filtering event logs from clickstream and cloud-based applications, where the scripts can be used in pipelines while minimizing the need for error-prone and time-consuming manual filtering. API clickstream cloud applications process mining scripting Industrial engineering. Management engineering Electronic computers. Computer science Daniel Amyot verfasserin aut In Algorithms MDPI AG, 2008 15(2022), 6, p 180 (DE-627)581036506 (DE-600)2455149-1 19994893 nnns volume:15 year:2022 number:6, p 180 https://doi.org/10.3390/a15060180 kostenfrei https://doaj.org/article/9156af8c17e84bb28fec871326c2188b kostenfrei https://www.mdpi.com/1999-4893/15/6/180 kostenfrei https://doaj.org/toc/1999-4893 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022 6, p 180
spelling	10.3390/a15060180 doi (DE-627)DOAJ044203950 (DE-599)DOAJ9156af8c17e84bb28fec871326c2188b DE-627 ger DE-627 rakwb eng T55.4-60.8 QA75.5-76.95 Najah Mary El-Gharib verfasserin aut Data Preprocessing Method and API for Mining Processes from Cloud-Based Application Event Logs 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Process mining (PM) exploits event logs to obtain meaningful information about the processes that produced them. As the number of applications developed on cloud infrastructures is increasing, it becomes important to study and discover their underlying processes. However, many current PM technologies face challenges in dealing with complex and large event logs from cloud applications, especially when they have little structure (e.g., clickstreams). By using Design Science Research, this paper introduces a new method, called cloud pattern API-process mining (CPA-PM), which enables the discovery and analysis of cloud-based application processes using PM in a way that addresses many of these challenges. CPA-PM exploits a new application programming interface, with an R implementation, for creating repeatable scripts that preprocess event logs collected from such applications. Applying CPA-PM to a case with real and evolving event logs related to the trial process of a software-as-a-service cloud application led to useful analyses and insights, with reusable scripts. CPA-PM helps producing executable scripts for filtering event logs from clickstream and cloud-based applications, where the scripts can be used in pipelines while minimizing the need for error-prone and time-consuming manual filtering. API clickstream cloud applications process mining scripting Industrial engineering. Management engineering Electronic computers. Computer science Daniel Amyot verfasserin aut In Algorithms MDPI AG, 2008 15(2022), 6, p 180 (DE-627)581036506 (DE-600)2455149-1 19994893 nnns volume:15 year:2022 number:6, p 180 https://doi.org/10.3390/a15060180 kostenfrei https://doaj.org/article/9156af8c17e84bb28fec871326c2188b kostenfrei https://www.mdpi.com/1999-4893/15/6/180 kostenfrei https://doaj.org/toc/1999-4893 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022 6, p 180
allfields_unstemmed	10.3390/a15060180 doi (DE-627)DOAJ044203950 (DE-599)DOAJ9156af8c17e84bb28fec871326c2188b DE-627 ger DE-627 rakwb eng T55.4-60.8 QA75.5-76.95 Najah Mary El-Gharib verfasserin aut Data Preprocessing Method and API for Mining Processes from Cloud-Based Application Event Logs 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Process mining (PM) exploits event logs to obtain meaningful information about the processes that produced them. As the number of applications developed on cloud infrastructures is increasing, it becomes important to study and discover their underlying processes. However, many current PM technologies face challenges in dealing with complex and large event logs from cloud applications, especially when they have little structure (e.g., clickstreams). By using Design Science Research, this paper introduces a new method, called cloud pattern API-process mining (CPA-PM), which enables the discovery and analysis of cloud-based application processes using PM in a way that addresses many of these challenges. CPA-PM exploits a new application programming interface, with an R implementation, for creating repeatable scripts that preprocess event logs collected from such applications. Applying CPA-PM to a case with real and evolving event logs related to the trial process of a software-as-a-service cloud application led to useful analyses and insights, with reusable scripts. CPA-PM helps producing executable scripts for filtering event logs from clickstream and cloud-based applications, where the scripts can be used in pipelines while minimizing the need for error-prone and time-consuming manual filtering. API clickstream cloud applications process mining scripting Industrial engineering. Management engineering Electronic computers. Computer science Daniel Amyot verfasserin aut In Algorithms MDPI AG, 2008 15(2022), 6, p 180 (DE-627)581036506 (DE-600)2455149-1 19994893 nnns volume:15 year:2022 number:6, p 180 https://doi.org/10.3390/a15060180 kostenfrei https://doaj.org/article/9156af8c17e84bb28fec871326c2188b kostenfrei https://www.mdpi.com/1999-4893/15/6/180 kostenfrei https://doaj.org/toc/1999-4893 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022 6, p 180
allfieldsGer	10.3390/a15060180 doi (DE-627)DOAJ044203950 (DE-599)DOAJ9156af8c17e84bb28fec871326c2188b DE-627 ger DE-627 rakwb eng T55.4-60.8 QA75.5-76.95 Najah Mary El-Gharib verfasserin aut Data Preprocessing Method and API for Mining Processes from Cloud-Based Application Event Logs 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Process mining (PM) exploits event logs to obtain meaningful information about the processes that produced them. As the number of applications developed on cloud infrastructures is increasing, it becomes important to study and discover their underlying processes. However, many current PM technologies face challenges in dealing with complex and large event logs from cloud applications, especially when they have little structure (e.g., clickstreams). By using Design Science Research, this paper introduces a new method, called cloud pattern API-process mining (CPA-PM), which enables the discovery and analysis of cloud-based application processes using PM in a way that addresses many of these challenges. CPA-PM exploits a new application programming interface, with an R implementation, for creating repeatable scripts that preprocess event logs collected from such applications. Applying CPA-PM to a case with real and evolving event logs related to the trial process of a software-as-a-service cloud application led to useful analyses and insights, with reusable scripts. CPA-PM helps producing executable scripts for filtering event logs from clickstream and cloud-based applications, where the scripts can be used in pipelines while minimizing the need for error-prone and time-consuming manual filtering. API clickstream cloud applications process mining scripting Industrial engineering. Management engineering Electronic computers. Computer science Daniel Amyot verfasserin aut In Algorithms MDPI AG, 2008 15(2022), 6, p 180 (DE-627)581036506 (DE-600)2455149-1 19994893 nnns volume:15 year:2022 number:6, p 180 https://doi.org/10.3390/a15060180 kostenfrei https://doaj.org/article/9156af8c17e84bb28fec871326c2188b kostenfrei https://www.mdpi.com/1999-4893/15/6/180 kostenfrei https://doaj.org/toc/1999-4893 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022 6, p 180
allfieldsSound	10.3390/a15060180 doi (DE-627)DOAJ044203950 (DE-599)DOAJ9156af8c17e84bb28fec871326c2188b DE-627 ger DE-627 rakwb eng T55.4-60.8 QA75.5-76.95 Najah Mary El-Gharib verfasserin aut Data Preprocessing Method and API for Mining Processes from Cloud-Based Application Event Logs 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Process mining (PM) exploits event logs to obtain meaningful information about the processes that produced them. As the number of applications developed on cloud infrastructures is increasing, it becomes important to study and discover their underlying processes. However, many current PM technologies face challenges in dealing with complex and large event logs from cloud applications, especially when they have little structure (e.g., clickstreams). By using Design Science Research, this paper introduces a new method, called cloud pattern API-process mining (CPA-PM), which enables the discovery and analysis of cloud-based application processes using PM in a way that addresses many of these challenges. CPA-PM exploits a new application programming interface, with an R implementation, for creating repeatable scripts that preprocess event logs collected from such applications. Applying CPA-PM to a case with real and evolving event logs related to the trial process of a software-as-a-service cloud application led to useful analyses and insights, with reusable scripts. CPA-PM helps producing executable scripts for filtering event logs from clickstream and cloud-based applications, where the scripts can be used in pipelines while minimizing the need for error-prone and time-consuming manual filtering. API clickstream cloud applications process mining scripting Industrial engineering. Management engineering Electronic computers. Computer science Daniel Amyot verfasserin aut In Algorithms MDPI AG, 2008 15(2022), 6, p 180 (DE-627)581036506 (DE-600)2455149-1 19994893 nnns volume:15 year:2022 number:6, p 180 https://doi.org/10.3390/a15060180 kostenfrei https://doaj.org/article/9156af8c17e84bb28fec871326c2188b kostenfrei https://www.mdpi.com/1999-4893/15/6/180 kostenfrei https://doaj.org/toc/1999-4893 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022 6, p 180
language	English
source	In Algorithms 15(2022), 6, p 180 volume:15 year:2022 number:6, p 180
sourceStr	In Algorithms 15(2022), 6, p 180 volume:15 year:2022 number:6, p 180
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	API clickstream cloud applications process mining scripting Industrial engineering. Management engineering Electronic computers. Computer science
isfreeaccess_bool	true
container_title	Algorithms
authorswithroles_txt_mv	Najah Mary El-Gharib @@aut@@ Daniel Amyot @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	581036506
id	DOAJ044203950
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">DOAJ044203950</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240414210410.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2022 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/a15060180</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ044203950</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ9156af8c17e84bb28fec871326c2188b</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="050" ind1=" " ind2="0"><subfield code="a">T55.4-60.8</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QA75.5-76.95</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Najah Mary El-Gharib</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Data Preprocessing Method and API for Mining Processes from Cloud-Based Application Event Logs</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</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">Process mining (PM) exploits event logs to obtain meaningful information about the processes that produced them. As the number of applications developed on cloud infrastructures is increasing, it becomes important to study and discover their underlying processes. However, many current PM technologies face challenges in dealing with complex and large event logs from cloud applications, especially when they have little structure (e.g., clickstreams). By using Design Science Research, this paper introduces a new method, called cloud pattern API-process mining (CPA-PM), which enables the discovery and analysis of cloud-based application processes using PM in a way that addresses many of these challenges. CPA-PM exploits a new application programming interface, with an R implementation, for creating repeatable scripts that preprocess event logs collected from such applications. Applying CPA-PM to a case with real and evolving event logs related to the trial process of a software-as-a-service cloud application led to useful analyses and insights, with reusable scripts. CPA-PM helps producing executable scripts for filtering event logs from clickstream and cloud-based applications, where the scripts can be used in pipelines while minimizing the need for error-prone and time-consuming manual filtering.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">API</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">clickstream</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">cloud applications</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">process mining</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">scripting</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Industrial engineering. Management engineering</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Electronic computers. Computer science</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Daniel Amyot</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Algorithms</subfield><subfield code="d">MDPI AG, 2008</subfield><subfield code="g">15(2022), 6, p 180</subfield><subfield code="w">(DE-627)581036506</subfield><subfield code="w">(DE-600)2455149-1</subfield><subfield code="x">19994893</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:15</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:6, p 180</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/a15060180</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/9156af8c17e84bb28fec871326c2188b</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.mdpi.com/1999-4893/15/6/180</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1999-4893</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</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_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">15</subfield><subfield code="j">2022</subfield><subfield code="e">6, p 180</subfield></datafield></record></collection>
callnumber-first	T - Technology
author	Najah Mary El-Gharib
spellingShingle	Najah Mary El-Gharib misc T55.4-60.8 misc QA75.5-76.95 misc API misc clickstream misc cloud applications misc process mining misc scripting misc Industrial engineering. Management engineering misc Electronic computers. Computer science Data Preprocessing Method and API for Mining Processes from Cloud-Based Application Event Logs
authorStr	Najah Mary El-Gharib
ppnlink_with_tag_str_mv	@@773@@(DE-627)581036506
format	electronic Article
delete_txt_mv	keep
author_role	aut aut
collection	DOAJ
remote_str	true
callnumber-label	T55
illustrated	Not Illustrated
issn	19994893
topic_title	T55.4-60.8 QA75.5-76.95 Data Preprocessing Method and API for Mining Processes from Cloud-Based Application Event Logs API clickstream cloud applications process mining scripting
topic	misc T55.4-60.8 misc QA75.5-76.95 misc API misc clickstream misc cloud applications misc process mining misc scripting misc Industrial engineering. Management engineering misc Electronic computers. Computer science
topic_unstemmed	misc T55.4-60.8 misc QA75.5-76.95 misc API misc clickstream misc cloud applications misc process mining misc scripting misc Industrial engineering. Management engineering misc Electronic computers. Computer science
topic_browse	misc T55.4-60.8 misc QA75.5-76.95 misc API misc clickstream misc cloud applications misc process mining misc scripting misc Industrial engineering. Management engineering misc Electronic computers. Computer science
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Algorithms
hierarchy_parent_id	581036506
hierarchy_top_title	Algorithms
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)581036506 (DE-600)2455149-1
title	Data Preprocessing Method and API for Mining Processes from Cloud-Based Application Event Logs
ctrlnum	(DE-627)DOAJ044203950 (DE-599)DOAJ9156af8c17e84bb28fec871326c2188b
title_full	Data Preprocessing Method and API for Mining Processes from Cloud-Based Application Event Logs
author_sort	Najah Mary El-Gharib
journal	Algorithms
journalStr	Algorithms
callnumber-first-code	T
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2022
contenttype_str_mv	txt
author_browse	Najah Mary El-Gharib Daniel Amyot
container_volume	15
class	T55.4-60.8 QA75.5-76.95
format_se	Elektronische Aufsätze
author-letter	Najah Mary El-Gharib
doi_str_mv	10.3390/a15060180
author2-role	verfasserin
title_sort	data preprocessing method and api for mining processes from cloud-based application event logs
callnumber	T55.4-60.8
title_auth	Data Preprocessing Method and API for Mining Processes from Cloud-Based Application Event Logs
abstract	Process mining (PM) exploits event logs to obtain meaningful information about the processes that produced them. As the number of applications developed on cloud infrastructures is increasing, it becomes important to study and discover their underlying processes. However, many current PM technologies face challenges in dealing with complex and large event logs from cloud applications, especially when they have little structure (e.g., clickstreams). By using Design Science Research, this paper introduces a new method, called cloud pattern API-process mining (CPA-PM), which enables the discovery and analysis of cloud-based application processes using PM in a way that addresses many of these challenges. CPA-PM exploits a new application programming interface, with an R implementation, for creating repeatable scripts that preprocess event logs collected from such applications. Applying CPA-PM to a case with real and evolving event logs related to the trial process of a software-as-a-service cloud application led to useful analyses and insights, with reusable scripts. CPA-PM helps producing executable scripts for filtering event logs from clickstream and cloud-based applications, where the scripts can be used in pipelines while minimizing the need for error-prone and time-consuming manual filtering.
abstractGer	Process mining (PM) exploits event logs to obtain meaningful information about the processes that produced them. As the number of applications developed on cloud infrastructures is increasing, it becomes important to study and discover their underlying processes. However, many current PM technologies face challenges in dealing with complex and large event logs from cloud applications, especially when they have little structure (e.g., clickstreams). By using Design Science Research, this paper introduces a new method, called cloud pattern API-process mining (CPA-PM), which enables the discovery and analysis of cloud-based application processes using PM in a way that addresses many of these challenges. CPA-PM exploits a new application programming interface, with an R implementation, for creating repeatable scripts that preprocess event logs collected from such applications. Applying CPA-PM to a case with real and evolving event logs related to the trial process of a software-as-a-service cloud application led to useful analyses and insights, with reusable scripts. CPA-PM helps producing executable scripts for filtering event logs from clickstream and cloud-based applications, where the scripts can be used in pipelines while minimizing the need for error-prone and time-consuming manual filtering.
abstract_unstemmed	Process mining (PM) exploits event logs to obtain meaningful information about the processes that produced them. As the number of applications developed on cloud infrastructures is increasing, it becomes important to study and discover their underlying processes. However, many current PM technologies face challenges in dealing with complex and large event logs from cloud applications, especially when they have little structure (e.g., clickstreams). By using Design Science Research, this paper introduces a new method, called cloud pattern API-process mining (CPA-PM), which enables the discovery and analysis of cloud-based application processes using PM in a way that addresses many of these challenges. CPA-PM exploits a new application programming interface, with an R implementation, for creating repeatable scripts that preprocess event logs collected from such applications. Applying CPA-PM to a case with real and evolving event logs related to the trial process of a software-as-a-service cloud application led to useful analyses and insights, with reusable scripts. CPA-PM helps producing executable scripts for filtering event logs from clickstream and cloud-based applications, where the scripts can be used in pipelines while minimizing the need for error-prone and time-consuming manual filtering.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
container_issue	6, p 180
title_short	Data Preprocessing Method and API for Mining Processes from Cloud-Based Application Event Logs
url	https://doi.org/10.3390/a15060180 https://doaj.org/article/9156af8c17e84bb28fec871326c2188b https://www.mdpi.com/1999-4893/15/6/180 https://doaj.org/toc/1999-4893
remote_bool	true
author2	Daniel Amyot
author2Str	Daniel Amyot
ppnlink	581036506
callnumber-subject	T - General Technology
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.3390/a15060180
callnumber-a	T55.4-60.8
up_date	2024-07-03T21:47:04.214Z
_version_	1803596043875516417
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">DOAJ044203950</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240414210410.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2022 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/a15060180</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ044203950</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ9156af8c17e84bb28fec871326c2188b</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="050" ind1=" " ind2="0"><subfield code="a">T55.4-60.8</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QA75.5-76.95</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Najah Mary El-Gharib</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Data Preprocessing Method and API for Mining Processes from Cloud-Based Application Event Logs</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</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">Process mining (PM) exploits event logs to obtain meaningful information about the processes that produced them. As the number of applications developed on cloud infrastructures is increasing, it becomes important to study and discover their underlying processes. However, many current PM technologies face challenges in dealing with complex and large event logs from cloud applications, especially when they have little structure (e.g., clickstreams). By using Design Science Research, this paper introduces a new method, called cloud pattern API-process mining (CPA-PM), which enables the discovery and analysis of cloud-based application processes using PM in a way that addresses many of these challenges. CPA-PM exploits a new application programming interface, with an R implementation, for creating repeatable scripts that preprocess event logs collected from such applications. Applying CPA-PM to a case with real and evolving event logs related to the trial process of a software-as-a-service cloud application led to useful analyses and insights, with reusable scripts. CPA-PM helps producing executable scripts for filtering event logs from clickstream and cloud-based applications, where the scripts can be used in pipelines while minimizing the need for error-prone and time-consuming manual filtering.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">API</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">clickstream</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">cloud applications</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">process mining</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">scripting</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Industrial engineering. Management engineering</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Electronic computers. Computer science</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Daniel Amyot</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Algorithms</subfield><subfield code="d">MDPI AG, 2008</subfield><subfield code="g">15(2022), 6, p 180</subfield><subfield code="w">(DE-627)581036506</subfield><subfield code="w">(DE-600)2455149-1</subfield><subfield code="x">19994893</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:15</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:6, p 180</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/a15060180</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/9156af8c17e84bb28fec871326c2188b</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.mdpi.com/1999-4893/15/6/180</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1999-4893</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</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_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">15</subfield><subfield code="j">2022</subfield><subfield code="e">6, p 180</subfield></datafield></record></collection>
score	7.401025

Nicht das Richtige dabei?

Schreiben Sie uns!

Data Preprocessing Method and API for Mining Processes from Cloud-Based Application Event Logs

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?