{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T06:07:25Z","timestamp":1770530845530,"version":"3.49.0"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2022,10,7]],"date-time":"2022-10-07T00:00:00Z","timestamp":1665100800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,10,7]],"date-time":"2022-10-07T00:00:00Z","timestamp":1665100800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.U1931207"],"award-info":[{"award-number":["No.U1931207"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.61702306"],"award-info":[{"award-number":["No.61702306"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Sci. & Tech. Development Fund of Shandong Province of China","award":["No.ZR2017BF015"],"award-info":[{"award-number":["No.ZR2017BF015"]}]},{"name":"Sci. & Tech. Development Fund of Shandong Province of China","award":["No.ZR2017MF027"],"award-info":[{"award-number":["No.ZR2017MF027"]}]},{"name":"Humanities and Social Science Research Project of the Ministry of Education","award":["No.18YJAZH017"],"award-info":[{"award-number":["No.18YJAZH017"]}]},{"name":"Shandong Chongqing Science and technology cooperation project","award":["No.cstc2020jscx-lyjsAX0008"],"award-info":[{"award-number":["No.cstc2020jscx-lyjsAX0008"]}]},{"name":"Sci. & Tech. Development Fund of Qingdao","award":["No.21-1-5-zlyj-1-zc"],"award-info":[{"award-number":["No.21-1-5-zlyj-1-zc"]}]},{"name":"Taishan Scholar Program of Shandong Province, SDUST Research Fund","award":["No.2015TDJH102"],"award-info":[{"award-number":["No.2015TDJH102"]}]},{"name":"Taishan Scholar Program of Shandong Province, SDUST Research Fund","award":["No.2019KJN024"],"award-info":[{"award-number":["No.2019KJN024"]}]},{"name":"National Statistical Science Research Project in 2019","award":["No.2019LY49"],"award-info":[{"award-number":["No.2019LY49"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s10489-022-04192-x","type":"journal-article","created":{"date-parts":[[2022,10,7]],"date-time":"2022-10-07T07:05:17Z","timestamp":1665126317000},"page":"13178-13191","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Business process remaining time prediction using explainable reachability graph from gated RNNs"],"prefix":"10.1007","volume":"53","author":[{"given":"Rui","family":"Cao","sequence":"first","affiliation":[]},{"given":"Qingtian","family":"Zeng","sequence":"additional","affiliation":[]},{"given":"Weijian","family":"Ni","sequence":"additional","affiliation":[]},{"given":"Hua","family":"Duan","sequence":"additional","affiliation":[]},{"given":"Cong","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Faming","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Ziqi","family":"Zhao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,7]]},"reference":[{"issue":"4","key":"4192_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3331449","volume":"10","author":"I Verenich","year":"2019","unstructured":"Verenich I, Dumas M, Rosa ML, Maggi FM, Teinemaa I (2019) Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoring. ACM Trans Intell Syst Technol (TIST) 10(4):1\u201334","journal-title":"ACM Trans Intell Syst Technol (TIST)"},{"key":"4192_CR2","doi-asserted-by":"crossref","unstructured":"Rama-Maneiro E, Vidal J, Lama M (2021) Deep learning for predictive business process monitoring: review and benchmark. IEEE Trans Serv Comput","DOI":"10.1109\/TSC.2021.3139807"},{"issue":"2","key":"4192_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3301300","volume":"13","author":"I Teinemaa","year":"2019","unstructured":"Teinemaa I, Dumas M, Rosa ML, Maggi FM (2019) Outcome-oriented predictive process monitoring: review and benchmark. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(2):1\u201357","journal-title":"ACM Transactions on Knowledge Discovery from Data (TKDD)"},{"key":"4192_CR4","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1007\/978-3-031-08848-3_10","volume":"448","author":"C Di Francescomarino","year":"2022","unstructured":"Di Francescomarino C, Ghidini C (2022) Predictive process monitoring. Process Mining Handbook LNBIP 448:320\u2013346","journal-title":"Process Mining Handbook LNBIP"},{"key":"4192_CR5","doi-asserted-by":"crossref","unstructured":"Pepper N, Crespo L, Montomoli F (2022) Adaptive learning for reliability analysis using support vector machines. Reliability Engineering & System Safety, 1\u201314","DOI":"10.1016\/j.ress.2022.108635"},{"key":"4192_CR6","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.renene.2022.01.011","volume":"187","author":"SN Shorabeh","year":"2022","unstructured":"Shorabeh SN, Samany NN, Minaei F, Firozjaei HK, Homaee M, Boloorani AD (2022) A decision model based on decision tree and particle swarm optimization algorithms to identify optimal locations for solar power plants construction in iran. Renew Energy 187:56\u201367","journal-title":"Renew Energy"},{"key":"4192_CR7","doi-asserted-by":"publisher","first-page":"108773","DOI":"10.1016\/j.knosys.2022.108773","volume":"248","author":"B Wickramanayake","year":"2022","unstructured":"Wickramanayake B, He Z, Ouyang C, Moreira C, Xu Y, Sindhgatta R (2022) Building interpretable models for business process prediction using shared and specialised attention mechanisms. Knowl-Based Syst 248:108773","journal-title":"Knowl-Based Syst"},{"issue":"1","key":"4192_CR8","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1145\/3359786","volume":"63","author":"M Du","year":"2019","unstructured":"Du M, Liu N, Hu X (2019) Techniques for interpretable machine learning. Commun ACM 63(1):68\u201377","journal-title":"Commun ACM"},{"key":"4192_CR9","doi-asserted-by":"publisher","first-page":"108773","DOI":"10.1016\/j.knosys.2022.108773","volume":"248","author":"B Wickramanayake","year":"2022","unstructured":"Wickramanayake B, He Z, Ouyang C, Moreira C, Xu Y, Sindhgatta R (2022) Building interpretable models for business process prediction using shared and specialised attention mechanisms. Knowl-Based Syst 248:108773","journal-title":"Knowl-Based Syst"},{"key":"4192_CR10","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1016\/j.ssci.2019.02.009","volume":"115","author":"S Kabir","year":"2019","unstructured":"Kabir S, Papadopoulos Y (2019) Applications of bayesian networks and petri nets in safety, reliability, and risk assessments: a review. Safety Sci 115:154\u2013175","journal-title":"Safety Sci"},{"key":"4192_CR11","doi-asserted-by":"crossref","unstructured":"Tax N, Verenich I, Rosa ML, Dumas M (2017) Predictive business process monitoring with lstm neural networks. In: International conference on advanced information systems engineering, p Springer","DOI":"10.1007\/978-3-319-59536-8_30"},{"key":"4192_CR12","doi-asserted-by":"crossref","unstructured":"Navarin N, Vincenzi B, Polato M, Sperduti A (2017) Lstm networks for data-aware remaining time prediction of business process instances. In: 2017 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, pp 1\u20137","DOI":"10.1109\/SSCI.2017.8285184"},{"key":"4192_CR13","doi-asserted-by":"crossref","unstructured":"Camargo M, Dumas M, Gonz\u00e1lez-Rojas O (2019) Learning accurate lstm models of business processes. In: International conference on business process management. Springer, pp 286\u2013302","DOI":"10.1007\/978-3-030-26619-6_19"},{"issue":"2\/3","key":"4192_CR14","first-page":"134","volume":"4","author":"T Liu","year":"2020","unstructured":"Liu T, Ni W, Sun Y, Zeng Q (2020) Predicting remaining business time with deep transfer learning. Data Anal Knowl Discov 4(2\/3):134\u2013142","journal-title":"Data Anal Knowl Discov"},{"issue":"6","key":"4192_CR15","first-page":"1564","volume":"26","author":"W Ni","year":"2020","unstructured":"Ni W, Sun Y, Liu T, Zeng Q (2020) Business process remaining time prediction using bidirectional recurrent neural networks with attention. Comput Integr Manuf Syst 26(6):1564\u20131572","journal-title":"Comput Integr Manuf Syst"},{"key":"4192_CR16","doi-asserted-by":"publisher","first-page":"109134","DOI":"10.1016\/j.asoc.2022.109134","volume":"125","author":"H Weytjens","year":"2022","unstructured":"Weytjens H, De Weerdt J (2022) Learning uncertainty with artificial neural networks for predictive process monitoring. Appl Soft Comput 125:109134","journal-title":"Appl Soft Comput"},{"issue":"21","key":"4192_CR17","doi-asserted-by":"publisher","first-page":"9848","DOI":"10.3390\/app11219848","volume":"11","author":"NA Wahid","year":"2021","unstructured":"Wahid NA, Bae H, Adi TN, Choi Y, Iskandar YA (2021) Parallel-structure deep learning for prediction of remaining time of process instances. Appl Sci 11(21):9848","journal-title":"Appl Sci"},{"key":"4192_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.artint.2018.07.007","volume":"267","author":"T Miller","year":"2019","unstructured":"Miller T (2019) Explanation in artificial intelligence: insights from the social sciences. Artif Intell 267:1\u201338","journal-title":"Artif Intell"},{"key":"4192_CR19","doi-asserted-by":"publisher","first-page":"1551","DOI":"10.1016\/j.apenergy.2018.11.081","volume":"235","author":"C Fan","year":"2019","unstructured":"Fan C, Xiao F, Yan C, Liu C, Li Z, Wang J (2019) A novel methodology to explain and evaluate data-driven building energy performance models based on interpretable machine learning. Appl Energy 235:1551\u20131560","journal-title":"Appl Energy"},{"key":"4192_CR20","unstructured":"Ayache S, Eyraud R, Goudian N (2019) Explaining black boxes on sequential data using weighted automata. In: International conference on grammatical inference. PMLR, pp 81\u2013103"},{"key":"4192_CR21","doi-asserted-by":"crossref","unstructured":"Weiss G, Goldberg Y, Yahav E (2022) Extracting automata from recurrent neural networks using queries and counterexamples (extended version). Mach Learn, 1\u201343","DOI":"10.1007\/s10994-022-06163-2"},{"issue":"7","key":"4192_CR22","first-page":"2267","volume":"31","author":"B-J Hou","year":"2020","unstructured":"Hou B-J, Zhou Z-H (2020) Learning with interpretable structure from gated rnn. IEEE Trans Neural Netw Learn Syst 31(7):2267\u20132279","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"4192_CR23","volume-title":"Representing formal languages: a comparison between finite automata and recurrent neural networks","author":"JJ Michalenko","year":"2019","unstructured":"Michalenko JJ (2019) Representing formal languages: a comparison between finite automata and recurrent neural networks. Rice University, PhD thesis"},{"key":"4192_CR24","doi-asserted-by":"crossref","unstructured":"Sindhgatta R, Moreira C, Ouyang C, Barros A (2020) Exploring interpretable predictive models for business processes. In: International conference on business process management. Springer, pp 257\u2013272","DOI":"10.1007\/978-3-030-58666-9_15"},{"issue":"sup1","key":"4192_CR25","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1080\/12460125.2020.1780780","volume":"29","author":"M Harl","year":"2020","unstructured":"Harl M, Weinzierl S, Stierle M, Matzner M (2020) Explainable predictive business process monitoring using gated graph neural networks. J Decis Syst 29(sup1):312\u2013327","journal-title":"J Decis Syst"},{"key":"4192_CR26","doi-asserted-by":"crossref","unstructured":"Rizzi W, Di Francescomarino C, Maggi FM (2020) Explainability in predictive process monitoring: when understanding helps improving. In: International conference on business process management. Springer, pp 141\u2013158","DOI":"10.1007\/978-3-030-58638-6_9"},{"key":"4192_CR27","doi-asserted-by":"publisher","first-page":"110861","DOI":"10.1016\/j.chaos.2021.110861","volume":"146","author":"K ArunKumar","year":"2021","unstructured":"ArunKumar K, Kalaga DV, Kumar CMS, Kawaji M, Brenza TM (2021) Forecasting of covid-19 using deep layer recurrent neural networks (rnns) with gated recurrent units (grus) and long short-term memory (lstm) cells. Chaos, Solitons & Fractals 146:110861","journal-title":"Chaos, Solitons & Fractals"},{"issue":"8","key":"4192_CR28","doi-asserted-by":"publisher","first-page":"5929","DOI":"10.1007\/s10462-020-09838-1","volume":"53","author":"G Van Houdt","year":"2020","unstructured":"Van Houdt G, Mosquera C, N\u00e1poles G (2020) A review on the long short-term memory model. Artif Intell Rev 53(8):5929\u20135955","journal-title":"Artif Intell Rev"},{"key":"4192_CR29","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/j.neucom.2021.02.046","volume":"441","author":"PB Weerakody","year":"2021","unstructured":"Weerakody PB, Wong KW, Wang G, Ela W (2021) A review of irregular time series data handling with gated recurrent neural networks. Neurocomputing 441:161\u2013178","journal-title":"Neurocomputing"},{"issue":"11","key":"4192_CR30","doi-asserted-by":"publisher","first-page":"76","DOI":"10.23919\/JCC.2021.11.006","volume":"18","author":"R Cao","year":"2021","unstructured":"Cao R, Ni W, Zeng Q, Lu F, Liu C, Duan H (2021) Remaining time prediction for business processes with concurrency based on log representation. China Commun 18(11):76\u201391. https:\/\/doi.org\/10.23919\/JCC.2021.11.006https:\/\/doi.org\/10.23919\/JCC.2021.11.006","journal-title":"China Commun"},{"key":"4192_CR31","doi-asserted-by":"publisher","DOI":"10.5204\/thesis.eprints.124037","volume-title":"Explainable predictive monitoring of temporal measures of business processes","author":"I Verenich","year":"2018","unstructured":"Verenich I (2018) Explainable predictive monitoring of temporal measures of business processes. Queensland University of Technology, PhD thesis"},{"issue":"1","key":"4192_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3422822","volume":"68","author":"W Czerwi\u0144ski","year":"2020","unstructured":"Czerwi\u0144ski W, Lasota S, Lazi\u0107 R, Leroux J, Mazowiecki F (2020) The reachability problem for petri nets is not elementary. J ACM (JACM) 68(1):1\u201328","journal-title":"J ACM (JACM)"},{"key":"4192_CR33","doi-asserted-by":"publisher","first-page":"1080","DOI":"10.1016\/j.procs.2019.11.219","volume":"161","author":"NA Wahid","year":"2019","unstructured":"Wahid NA, Adi TN, Bae H, Choi Y (2019) Predictive business process monitoring\u2013remaining time prediction using deep neural network with entity embedding. Procedia Comput Sci 161:1080\u20131088","journal-title":"Procedia Comput Sci"},{"key":"4192_CR34","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.jmsy.2020.06.003","volume":"56","author":"AC Choueiri","year":"2020","unstructured":"Choueiri AC, Sato DMV, Scalabrin EE, Santos EAP (2020) An extended model for remaining time prediction in manufacturing systems using process mining. J Manuf Syst 56:188\u2013201","journal-title":"J Manuf Syst"},{"issue":"2","key":"4192_CR35","doi-asserted-by":"publisher","first-page":"543","DOI":"10.3233\/IDA-215755","volume":"26","author":"W Ni","year":"2022","unstructured":"Ni W, Yan M, Liu T, Zeng Q (2022) Predicting remaining execution time of business process instances via auto-encoded transition system. Intell Data Anal 26(2):543\u2013562","journal-title":"Intell Data Anal"},{"issue":"1","key":"4192_CR36","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1007\/s10489-018-1262-7","volume":"49","author":"LH Son","year":"2019","unstructured":"Son LH, Fujita H (2019) Neural-fuzzy with representative sets for prediction of student performance. Appl Intell 49(1):172\u2013187","journal-title":"Appl Intell"},{"issue":"8","key":"4192_CR37","doi-asserted-by":"publisher","first-page":"1321","DOI":"10.1109\/JPROC.2018.2846568","volume":"106","author":"SX Wu","year":"2018","unstructured":"Wu SX, Wai H-T, Li L, Scaglione A (2018) A review of distributed algorithms for principal component analysis. Proc IEEE 106(8):1321\u20131340","journal-title":"Proc IEEE"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-04192-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-022-04192-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-04192-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,31]],"date-time":"2023-05-31T10:20:48Z","timestamp":1685528448000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-022-04192-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,7]]},"references-count":37,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["4192"],"URL":"https:\/\/doi.org\/10.1007\/s10489-022-04192-x","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,7]]},"assertion":[{"value":"18 September 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 October 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and\/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled \u201cBusiness process remaining time prediction using explainable reachability graph from gated RNNs\u201d.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of Interests"}}]}}