{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:47:52Z","timestamp":1742914072001,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030965990"},{"type":"electronic","value":"9783030966003"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-030-96600-3_15","type":"book-chapter","created":{"date-parts":[[2022,2,17]],"date-time":"2022-02-17T14:03:35Z","timestamp":1645106615000},"page":"209-218","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["GA-ProM: A Genetic Algorithm for Discovery of Complete Process Models from Unbalanced Logs"],"prefix":"10.1007","author":[{"given":"Sonia","family":"Deshmukh","sequence":"first","affiliation":[]},{"given":"Shikha","family":"Gupta","sequence":"additional","affiliation":[]},{"given":"Naveen","family":"Kumar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,2,18]]},"reference":[{"issue":"9","key":"15_CR1","doi-asserted-by":"publisher","first-page":"1128","DOI":"10.1109\/TKDE.2004.47","volume":"16","author":"W Van der Aalst","year":"2004","unstructured":"Van der Aalst, W., Weijters, T., Maruster, L.: Workflow mining: discovering process models from event logs. IEEE Trans. Knowl. Data Eng. 16(9), 1128\u20131142 (2004)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"15_CR2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-49851-4","volume-title":"Process Mining: Data Science in Action","author":"WM van der Aalst","year":"2016","unstructured":"van der Aalst, W.M.: Process Mining: Data Science in Action. Springer, Heidelberg (2016). https:\/\/doi.org\/10.1007\/978-3-662-49851-4"},{"key":"15_CR3","doi-asserted-by":"crossref","unstructured":"van der Aalst, W.M., Gunther, C.W.: Finding structure in unstructured processes: the case for process mining. In: Seventh International Conference on Application of Concurrency to System Design, ACSD 2007, pp. 3\u201312. IEEE (2007)","DOI":"10.1109\/ACSD.2007.50"},{"key":"15_CR4","doi-asserted-by":"crossref","unstructured":"vanden Broucke, S.K., De Weerdt, J.: Fodina: a robust and flexible heuristic process discovery technique. Decis. Support Syst. 100, 109\u2013118 (2017)","DOI":"10.1016\/j.dss.2017.04.005"},{"key":"15_CR5","doi-asserted-by":"crossref","unstructured":"vanden Broucke, S.K., De Weerdt, J., Vanthienen, J., Baesens, B.: A comprehensive benchmarking framework (CoBeFra) for conformance analysis between procedural process models and event logs in ProM. In: 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), pp. 254\u2013261. IEEE (2013)","DOI":"10.1109\/CIDM.2013.6597244"},{"key":"15_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1007\/978-3-642-33606-5_19","volume-title":"On the Move to Meaningful Internet Systems: OTM 2012","author":"JCAM Buijs","year":"2012","unstructured":"Buijs, J.C.A.M., van Dongen, B.F., van der Aalst, W.M.P.: On the role of fitness, precision, generalization and simplicity in process discovery. In: Meersman, R., Cruz, I.F. (eds.) OTM 2012. LNCS, vol. 7565, pp. 305\u2013322. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-33606-5_19"},{"issue":"6","key":"15_CR7","first-page":"351","volume":"32","author":"HJ Cheng","year":"2015","unstructured":"Cheng, H.J., Ou-Yang, C., Juan, Y.C.: A hybrid approach to extract business process models with high fitness and precision. J. Ind. Prod. Eng. 32(6), 351\u2013359 (2015)","journal-title":"J. Ind. Prod. Eng."},{"key":"15_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1007\/978-3-662-45563-0_26","volume-title":"On the Move to Meaningful Internet Systems: OTM 2014 Conferences","author":"J De Smedt","year":"2014","unstructured":"De Smedt, J., De Weerdt, J., Vanthienen, J.: Multi-paradigm process mining: retrieving better models by combining rules and sequences. In: Meersman, R., et al. (eds.) OTM 2014. LNCS, vol. 8841, pp. 446\u2013453. Springer, Heidelberg (2014). https:\/\/doi.org\/10.1007\/978-3-662-45563-0_26"},{"key":"15_CR9","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1007\/978-981-16-2709-5_21","volume-title":"Soft Computing for Problem Solving","author":"S Deshmukh","year":"2021","unstructured":"Deshmukh, S., Gupta, S., Varshney, S., Kumar, N.: A binary differential evolution approach to extract business process models. In: Tiwari, A., et al. (eds.) Soft Computing for Problem Solving. AISC, vol. 1392, pp. 279\u2013290. Springer, Singapore (2021). https:\/\/doi.org\/10.1007\/978-981-16-2709-5_21"},{"key":"15_CR10","unstructured":"van Dongen, B.F., Van der Aalst, W.M.: Multi-phase process mining: aggregating instance graphs into EPCs and petri nets. In: PNCWB 2005 Workshop, pp. 35\u201358 (2005)"},{"key":"15_CR11","unstructured":"van Eck, M.: Alignment-based process model repair and its application to the Evolutionary Tree Miner. Ph.D. thesis, Master\u2019s thesis, Technische Universiteit Eindhoven (2013)"},{"key":"15_CR12","first-page":"1305","volume":"10","author":"S Goedertier","year":"2009","unstructured":"Goedertier, S., Martens, D., Vanthienen, J., Baesens, B.: Robust process discovery with artificial negative events. J. Mach. Learn. Res. 10, 1305\u20131340 (2009)","journal-title":"J. Mach. Learn. Res."},{"key":"15_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1007\/978-3-642-38697-8_17","volume-title":"Application and Theory of Petri Nets and Concurrency","author":"SJJ Leemans","year":"2013","unstructured":"Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Discovering block-structured process models from event logs - a constructive approach. In: Colom, J.-M., Desel, J. (eds.) PETRI NETS 2013. LNCS, vol. 7927, pp. 311\u2013329. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-38697-8_17"},{"key":"15_CR14","doi-asserted-by":"crossref","unstructured":"Li, J., Liu, D., Yang, B.: Process mining: extending $$\\alpha $$-algorithm to mine duplicate tasks in process logs. In: Advances in Web and Network Technologies, and Information Management, pp. 396\u2013407 (2007)","DOI":"10.1007\/978-3-540-72909-9_43"},{"key":"15_CR15","unstructured":"Alves de Medeiros, A., Van Dongen, B., Van Der Aalst, W., Weijters, A.: Process mining: extending the $$\\alpha $$-algorithm to mine short loops. Technical report, BETA Working Paper Series (2004)"},{"key":"15_CR16","unstructured":"Alves de Medeiros, A.K.: Genetic process mining. CIP-Data Library Technische Universiteit Eindhoven (2006, printed in)"},{"key":"15_CR17","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1007\/978-3-642-19345-3","volume-title":"Process Mining: Discovery, Conformance and Enhancement of Business Processes","author":"WMP van der Aalst","year":"2011","unstructured":"van der Aalst, W.M.P.: Process Mining: Discovery, Conformance and Enhancement of Business Processes, vol. 8, p. 18. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-19345-3"},{"key":"15_CR18","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1016\/j.eswa.2019.05.003","volume":"133","author":"C dos Santos Garcia","year":"2019","unstructured":"dos Santos Garcia, C., et al.: Process mining techniques and applications-a systematic mapping study. Expert Syst. Appl. 133, 260\u2013295 (2019)","journal-title":"Expert Syst. Appl."},{"key":"15_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1007\/978-3-540-30464-7_29","volume-title":"Conceptual Modeling \u2013 ER 2004","author":"BF van Dongen","year":"2004","unstructured":"van Dongen, B.F., van der Aalst, W.M.P.: Multi-phase process mining: building instance graphs. In: Atzeni, P., Chu, W., Lu, H., Zhou, S., Ling, T.-W. (eds.) ER 2004. LNCS, vol. 3288, pp. 362\u2013376. Springer, Heidelberg (2004). https:\/\/doi.org\/10.1007\/978-3-540-30464-7_29"},{"key":"15_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1007\/978-3-319-10172-9_8","volume-title":"Business Process Management","author":"B V\u00e1zquez-Barreiros","year":"2014","unstructured":"V\u00e1zquez-Barreiros, B., Mucientes, M., Lama, M.: A genetic algorithm for process discovery guided by completeness, precision and simplicity. In: Sadiq, S., Soffer, P., V\u00f6lzer, H. (eds.) BPM 2014. LNCS, vol. 8659, pp. 118\u2013133. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10172-9_8"},{"key":"15_CR21","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1016\/j.ins.2014.09.057","volume":"294","author":"B V\u00e1zquez-Barreiros","year":"2015","unstructured":"V\u00e1zquez-Barreiros, B., Mucientes, M., Lama, M.: ProDiGen: mining complete, precise and minimal structure process models with a genetic algorithm. Inf. Sci. 294, 315\u2013333 (2015)","journal-title":"Inf. Sci."},{"key":"15_CR22","unstructured":"Weijters, A., van Der Aalst, W.M., De Medeiros, A.A.: Process mining with the heuristics miner-algorithm. Technische Universiteit Eindhoven, Technical report. WP 166, pp. 1\u201334 (2006)"},{"issue":"2","key":"15_CR23","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1007\/s10618-007-0065-y","volume":"15","author":"L Wen","year":"2007","unstructured":"Wen, L., van der Aalst, W.M., Wang, J., Sun, J.: Mining process models with non-free-choice constructs. Data Min. Knowl. Disc. 15(2), 145\u2013180 (2007)","journal-title":"Data Min. Knowl. Disc."},{"key":"15_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1007\/978-3-540-72524-4_38","volume-title":"Advances in Data and Web Management","author":"L Wen","year":"2007","unstructured":"Wen, L., Wang, J., Sun, J.: Mining invisible tasks from event logs. In: Dong, G., Lin, X., Wang, W., Yang, Y., Yu, J.X. (eds.) APWeb\/WAIM -2007. LNCS, vol. 4505, pp. 358\u2013365. Springer, Heidelberg (2007). https:\/\/doi.org\/10.1007\/978-3-540-72524-4_38"}],"container-title":["Lecture Notes in Computer Science","Big-Data-Analytics in Astronomy, Science, and Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-96600-3_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,2,17]],"date-time":"2022-02-17T14:04:41Z","timestamp":1645106681000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-96600-3_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030965990","9783030966003"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-96600-3_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"18 February 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BDA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Big Data Analytics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bigda2021a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/web-ext.u-aizu.ac.jp\/labs\/is-ds\/BASE2021.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"60","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"15","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"25% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2-3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2-3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}