{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T11:55:29Z","timestamp":1770983729977,"version":"3.50.1"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031278143","type":"print"},{"value":"9783031278150","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,3,26]],"date-time":"2023-03-26T00:00:00Z","timestamp":1679788800000},"content-version":"vor","delay-in-days":84,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>During the last years, a number of studies have experimented with applying process mining (PM) techniques to smart spaces data. The general goal has been to automatically model human routines as if they were business processes. However, applying process-oriented techniques to smart spaces data comes with its own set of challenges. This paper surveys existing approaches that apply PM to smart spaces and analyses how they deal with the following challenges identified in the literature: choosing a modelling formalism for human behaviour; bridging the abstraction gap between sensor and event logs; and segmenting logs in traces. The added value of this article lies in providing the research community with a common ground for some important challenges that exist in this field and their respective solutions, and to assist further research efforts by outlining opportunities for future work.<\/jats:p>","DOI":"10.1007\/978-3-031-27815-0_5","type":"book-chapter","created":{"date-parts":[[2023,3,25]],"date-time":"2023-03-25T10:03:04Z","timestamp":1679738584000},"page":"57-70","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Survey on\u00a0the\u00a0Application of\u00a0Process Mining to\u00a0Smart Spaces Data"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6407-7221","authenticated-orcid":false,"given":"Yannis","family":"Bertrand","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bram","family":"Van den Abbeele","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2164-5954","authenticated-orcid":false,"given":"Silvestro","family":"Veneruso","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9216-8502","authenticated-orcid":false,"given":"Francesco","family":"Leotta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9730-8882","authenticated-orcid":false,"given":"Massimo","family":"Mecella","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7579-910X","authenticated-orcid":false,"given":"Estefan\u00eda","family":"Serral","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,26]]},"reference":[{"key":"5_CR1","doi-asserted-by":"crossref","unstructured":"Aztiria, A., Izaguirre, A., Basagoiti, R., Augusto, J.C., Cook, D.J.: Automatic modeling of frequent user behaviours in intelligent environments. In: 2010 IE, pp. 7\u201312 (2010)","DOI":"10.1109\/IE.2010.9"},{"issue":"9","key":"5_CR2","first-page":"8440","volume":"7","author":"M Cameranesi","year":"2020","unstructured":"Cameranesi, M., Diamantini, C., Mircoli, A., Potena, D., Storti, E.: Extraction of user daily behavior from home sensors through process discovery. IEEE IoT J. 7(9), 8440\u20138450 (2020)","journal-title":"IEEE IoT J."},{"key":"5_CR3","series-title":"Lecture Notes in Business Information Processing","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1007\/978-3-319-74030-0_21","volume-title":"Business Process Management Workshops","author":"M Cameranesi","year":"2018","unstructured":"Cameranesi, M., Diamantini, C., Potena, D.: Discovering process models of activities of daily living from sensors. In: Teniente, E., Weidlich, M. (eds.) BPM 2017. LNBIP, vol. 308, pp. 285\u2013297. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-74030-0_21"},{"key":"5_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1007\/978-3-319-14112-1_16","volume-title":"Ambient Intelligence","author":"BD Carolis","year":"2014","unstructured":"Carolis, B.D., Ferilli, S., Mallardi, G.: Learning and recognizing routines and activities in SOFiA. In: Aarts, E., et al. (eds.) AmI 2014. LNCS, vol. 8850, pp. 191\u2013204. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-14112-1_16"},{"issue":"4","key":"5_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2675063","volume":"4","author":"BD Carolis","year":"2015","unstructured":"Carolis, B.D., Ferilli, S., Redavid, D.: Incremental learning of daily routines as workflows in a smart home environment. ACM TiiS 4(4), 1\u201323 (2015)","journal-title":"ACM TiiS"},{"issue":"7","key":"5_CR6","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1109\/MC.2012.328","volume":"46","author":"DJ Cook","year":"2012","unstructured":"Cook, D.J., Crandall, A.S., Thomas, B.L., Krishnan, N.C.: CASAS: a smart home in a box. Computer 46(7), 62\u201369 (2012)","journal-title":"Computer"},{"key":"5_CR7","doi-asserted-by":"crossref","unstructured":"Dimaggio, M., Leotta, F., Mecella, M., Sora, D.: Process-based habit mining: experiments and techniques. In: 2016 Intl IEEE Conferences UIC, pp. 145\u2013152 (2016)","DOI":"10.1109\/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0043"},{"issue":"1","key":"5_CR8","doi-asserted-by":"publisher","first-page":"139","DOI":"10.33422\/ejest.v3i1.250","volume":"3","author":"O Dogan","year":"2020","unstructured":"Dogan, O.: Discovering customer paths from location data with process mining. EJEST 3(1), 139\u2013145 (2020)","journal-title":"EJEST"},{"issue":"3","key":"5_CR9","doi-asserted-by":"publisher","first-page":"557","DOI":"10.3390\/s19030557","volume":"19","author":"O Dogan","year":"2019","unstructured":"Dogan, O., Bayo-Monton, J.L., Fernandez-Llatas, C., Oztaysi, B.: Analyzing of gender behaviors from paths using process mining: a shopping mall application. Sensors 19(3), 557 (2019)","journal-title":"Sensors"},{"key":"5_CR10","doi-asserted-by":"crossref","unstructured":"Esposito, L., Leotta, F., Mecella, M., Veneruso, S.: Unsupervised segmentation of smart home logs for human habit discovery. In: 2022 18th International Conference on Intelligent Environments (IE), pp. 1\u20138. IEEE (2022)","DOI":"10.1109\/IE54923.2022.9826776"},{"issue":"11","key":"5_CR11","doi-asserted-by":"publisher","first-page":"15434","DOI":"10.3390\/s131115434","volume":"13","author":"C Fern\u00e1ndez-Llatas","year":"2013","unstructured":"Fern\u00e1ndez-Llatas, C., Benedi, J.M., Garc\u00eda-G\u00f3mez, J.M., Traver, V.: Process mining for individualized behavior modeling using wireless tracking in nursing homes. Sensors 13(11), 15434\u201315451 (2013)","journal-title":"Sensors"},{"key":"5_CR12","doi-asserted-by":"crossref","unstructured":"Fernandez-Llatas, C., Pileggi, S.F., Traver, V., Benedi, J.M.: Timed parallel automaton: a mathematical tool for defining highly expressive formal workflows. In: Fifth Asia Modelling Symposium, pp. 56\u201361 (2011)","DOI":"10.1109\/AMS.2011.22"},{"issue":"1","key":"5_CR13","first-page":"77","volume":"10","author":"F Folino","year":"2021","unstructured":"Folino, F., Pontieri, L.: Ai-empowered process mining for complex application scenarios: survey and discussion. JoDS 10(1), 77\u2013106 (2021)","journal-title":"JoDS"},{"key":"5_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1007\/978-3-540-75183-0_24","volume-title":"Business Process Management","author":"CW G\u00fcnther","year":"2007","unstructured":"G\u00fcnther, C.W., van der Aalst, W.M.P.: Fuzzy mining \u2013 adaptive process simplification based on multi-perspective metrics. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 328\u2013343. Springer, Heidelberg (2007). https:\/\/doi.org\/10.1007\/978-3-540-75183-0_24"},{"key":"5_CR15","series-title":"Lecture Notes in Business Information Processing","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1007\/978-3-030-72693-5_6","volume-title":"Process Mining Workshops","author":"D Janssen","year":"2021","unstructured":"Janssen, D., Mannhardt, F., Koschmider, A., van Zelst, S.J.: Process model discovery from sensor event data. In: Leemans, S., Leopold, H. (eds.) ICPM 2020. LNBIP, vol. 406, pp. 69\u201381. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-72693-5_6"},{"key":"5_CR16","doi-asserted-by":"publisher","first-page":"698","DOI":"10.1016\/j.procs.2019.08.100","volume":"155","author":"C Jobanputra","year":"2019","unstructured":"Jobanputra, C., Bavishi, J., Doshi, N.: Human activity recognition: a survey. Proc. Comput. Sci. 155, 698\u2013703 (2019)","journal-title":"Proc. Comput. Sci."},{"issue":"2004","key":"5_CR17","first-page":"1","volume":"33","author":"B Kitchenham","year":"2004","unstructured":"Kitchenham, B.: Procedures for performing systematic reviews. Keele UK Keele Univ. 33(2004), 1\u201326 (2004)","journal-title":"Keele UK Keele Univ."},{"key":"5_CR18","series-title":"Lecture Notes in Business Information Processing","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1007\/978-3-030-94343-1_31","volume-title":"Business Process Management Workshops","author":"M de Leoni","year":"2022","unstructured":"de Leoni, M., Pellattiero, L.: The benefits of sensor-measurement aggregation in discovering IoT process models: a smart-house case study. In: Marrella, A., Weber, B. (eds.) BPM 2021. LNBIP, vol. 436, pp. 403\u2013415. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-030-94343-1_31"},{"key":"5_CR19","series-title":"Lecture Notes in Business Information Processing","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1007\/978-3-319-19243-7_28","volume-title":"Advanced Information Systems Engineering Workshops","author":"F Leotta","year":"2015","unstructured":"Leotta, F., Mecella, M., Mendling, J.: Applying process mining to smart spaces: perspectives and research challenges. In: Persson, A., Stirna, J. (eds.) CAiSE 2015. LNBIP, vol. 215, pp. 298\u2013304. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-19243-7_28"},{"issue":"5","key":"5_CR20","first-page":"1997","volume":"11","author":"F Leotta","year":"2020","unstructured":"Leotta, F., Mecella, M., Sora, D.: Visual process maps: a visualization tool for discovering habits in smart homes. JAIHC 11(5), 1997\u20132025 (2020)","journal-title":"JAIHC"},{"issue":"1","key":"5_CR21","doi-asserted-by":"publisher","first-page":"23","DOI":"10.3390\/fi11010023","volume":"11","author":"F Leotta","year":"2019","unstructured":"Leotta, F., Mecella, M., Sora, D., Catarci, T.: Surveying human habit modeling and mining techniques in smart spaces. Future Internet 11(1), 23 (2019)","journal-title":"Future Internet"},{"key":"5_CR22","series-title":"Health Informatics","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1007\/978-3-030-53993-1_13","volume-title":"Interactive Process Mining in Healthcare","author":"JJ Lull","year":"2021","unstructured":"Lull, J.J., Bayo, J.L., Shirali, M., Ghassemian, M., Fernandez-Llatas, C.: Interactive process mining in IoT and human behaviour modelling. In: Fernandez-Llatas, C. (ed.) Interactive Process Mining in Healthcare. HI, pp. 217\u2013231. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-53993-1_13"},{"key":"5_CR23","doi-asserted-by":"crossref","unstructured":"Ma\u2019arif, M.R.: Revealing daily human activity pattern using process mining approach. In: EECSI 2017, pp. 1\u20135 (2017)","DOI":"10.1109\/EECSI.2017.8239160"},{"key":"5_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1007\/978-3-030-03496-2_10","volume-title":"Intelligent Data Engineering and Automated Learning \u2013 IDEAL 2018","author":"F Mannhardt","year":"2018","unstructured":"Mannhardt, F., Bovo, R., Oliveira, M.F., Julier, S.: A taxonomy for combining activity recognition and process discovery in industrial environments. In: Yin, H., Camacho, D., Novais, P., Tall\u00f3n-Ballesteros, A.J. (eds.) IDEAL 2018. LNCS, vol. 11315, pp. 84\u201393. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-03496-2_10"},{"issue":"5","key":"5_CR25","doi-asserted-by":"publisher","first-page":"5460","DOI":"10.3390\/s130505460","volume":"13","author":"FJ Ord\u00f3nez","year":"2013","unstructured":"Ord\u00f3nez, F.J., De Toledo, P., Sanchis, A.: Activity recognition using hybrid generative\/discriminative models on home environments using binary sensors. Sensors 13(5), 5460\u20135477 (2013)","journal-title":"Sensors"},{"key":"5_CR26","doi-asserted-by":"crossref","unstructured":"Prathama, F., Yahya, B.N., Lee, S.L.: A multi-case perspective analytical framework for discovering human daily behavior from sensors using process mining. In: COMPSAC 2021, pp. 638\u2013644 (2021)","DOI":"10.1109\/COMPSAC51774.2021.00093"},{"key":"5_CR27","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-40172-6_1","volume-title":"Process Mining in Action","author":"L Reinkemeyer","year":"2020","unstructured":"Reinkemeyer, L.: Process mining in a nutshell. In: Reinkemeyer, L. (ed.) Process Mining in Action, pp. 3\u201310. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-40172-6_1"},{"key":"5_CR28","series-title":"Lecture Notes in Business Information Processing","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1007\/978-3-030-94343-1_30","volume-title":"Business Process Management Workshops","author":"E Serral","year":"2022","unstructured":"Serral, E., Schuster, D., Bertrand, Y.: Supporting Users in the continuous evolution of automated routines in their smart spaces. In: Marrella, A., Weber, B. (eds.) BPM 2021. LNBIP, vol. 436, pp. 391\u2013402. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-030-94343-1_30"},{"key":"5_CR29","unstructured":"Sora, D., Leotta, F., Mecella, M.: Addressing multi-users open challenge in habit mining for a process mining-based approach. In: Integrating Research Agendas and Devising Joint Challenges, pp. 266\u2013273 (2018)"},{"key":"5_CR30","unstructured":"Sztyler, T., Carmona, J.J.: Activities of daily living of several individuals (2015)"},{"key":"5_CR31","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1007\/978-3-540-24646-6_10","volume":"3001","author":"E Tapia","year":"2004","unstructured":"Tapia, E., Intille, S., Larson, K.: Activity recognisation in home using simple state changing sensors. Pervasive Comput. 3001, 158\u2013175 (2004)","journal-title":"Pervasive Comput."},{"key":"5_CR32","unstructured":"Tax, N., Alasgarov, E., Sidorova, N., Haakma, R.: On generation of time-based label refinements. arXiv preprint arXiv:1609.03333 (2016)"},{"issue":"2","key":"5_CR33","first-page":"165","volume":"11","author":"N Tax","year":"2019","unstructured":"Tax, N., Alasgarov, E., Sidorova, N., Haakma, R., van der Aalst, W.M.: Generating time-based label refinements to discover more precise process models. JAISE 11(2), 165\u2013182 (2019)","journal-title":"JAISE"},{"key":"5_CR34","doi-asserted-by":"crossref","unstructured":"Tax, N., Sidorova, N., van der Aalst, W.M., Haakma, R.: Heuristic approaches for generating local process models through log projections. In: 2016 IEEE SSCI, pp. 1\u20138 (2016)","DOI":"10.1109\/SSCI.2016.7849948"},{"key":"5_CR35","series-title":"Studies in Computational Intelligence","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1007\/978-3-319-69266-1_5","volume-title":"Intelligent Systems and Applications","author":"N Tax","year":"2018","unstructured":"Tax, N., Sidorova, N., Haakma, R., van der Aalst, W.: Mining process model descriptions of daily life through event abstraction. In: Bi, Y., Kapoor, S., Bhatia, R. (eds.) IntelliSys 2016. SCI, vol. 751, pp. 83\u2013104. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-69266-1_5"},{"key":"5_CR36","series-title":"Lecture Notes in Networks and Systems","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1007\/978-3-319-56994-9_18","volume-title":"Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016","author":"N Tax","year":"2018","unstructured":"Tax, N., Sidorova, N., Haakma, R., van der Aalst, W.M.P.: Event abstraction for process mining using supervised learning techniques. In: Bi, Y., Kapoor, S., Bhatia, R. (eds.) IntelliSys 2016. LNNS, vol. 15, pp. 251\u2013269. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-56994-9_18"},{"key":"5_CR37","doi-asserted-by":"crossref","unstructured":"Theodoropoulou, G., Bousdekis, A., Miaoulis, G., Voulodimos, A.: Process mining for activities of daily living in smart homecare. In: PCI 2020, pp. 197\u2013201 (2020)","DOI":"10.1145\/3437120.3437306"},{"key":"5_CR38","doi-asserted-by":"crossref","unstructured":"Van Kasteren, T., Noulas, A., Englebienne, G., Kr\u00f6se, B.: Accurate activity recognition in a home setting. In: Proceedings of UbiComp 2008, pp. 1\u20139 (2008)","DOI":"10.1145\/1409635.1409637"},{"issue":"3","key":"5_CR39","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1007\/s41066-020-00226-2","volume":"6","author":"SJ van Zelst","year":"2021","unstructured":"van Zelst, S.J., Mannhardt, F., de Leoni, M., Koschmider, A.: Event abstraction in process mining: literature review and taxonomy. Granular Comput. 6(3), 719\u2013736 (2021)","journal-title":"Granular Comput."},{"key":"5_CR40","unstructured":"Zerbato, F., Seiger, R., Di Federico, G., Burattin, A., Weber, B.: Granularity in process mining: Can we fix it? In: CEUR Workshop Proceedings, vol. 2938, pp. 40\u201344 (2021)"}],"container-title":["Lecture Notes in Business Information Processing","Process Mining Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-27815-0_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T13:05:53Z","timestamp":1693832753000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-27815-0_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031278143","9783031278150"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-27815-0_5","relation":{},"ISSN":["1865-1348","1865-1356"],"issn-type":[{"value":"1865-1348","type":"print"},{"value":"1865-1356","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"26 March 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Process Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bozen-Bolzano","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpm2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpmconference.org\/2022\/","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":"89","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":"42","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":"0","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":"47% - 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":"2.93","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","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}