{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T04:10:36Z","timestamp":1781064636760,"version":"3.54.1"},"publisher-location":"Cham","reference-count":46,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031440632","type":"print"},{"value":"9783031440649","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:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-44064-9_11","type":"book-chapter","created":{"date-parts":[[2023,10,29]],"date-time":"2023-10-29T04:19:19Z","timestamp":1698553159000},"page":"177-194","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["iPDP: On Partial Dependence Plots in\u00a0Dynamic Modeling Scenarios"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6921-0204","authenticated-orcid":false,"given":"Maximilian","family":"Muschalik","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3955-3510","authenticated-orcid":false,"given":"Fabian","family":"Fumagalli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7604-256X","authenticated-orcid":false,"given":"Rohit","family":"Jagtani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0935-5591","authenticated-orcid":false,"given":"Barbara","family":"Hammer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9944-4108","authenticated-orcid":false,"given":"Eyke","family":"H\u00fcllermeier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,10,30]]},"reference":[{"key":"11_CR1","doi-asserted-by":"publisher","first-page":"52138","DOI":"10.1109\/ACCESS.2018.2870052","volume":"6","author":"A Adadi","year":"2018","unstructured":"Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access 6, 52138\u201352160 (2018). https:\/\/doi.org\/10.1109\/ACCESS.2018.2870052","journal-title":"IEEE Access"},{"issue":"6","key":"11_CR2","doi-asserted-by":"publisher","first-page":"914","DOI":"10.1109\/69.250074","volume":"5","author":"R Agrawal","year":"1993","unstructured":"Agrawal, R., Imielinski, T., Swami, A.: Database mining: a performance perspective. IEEE Trans. Knowl. Data Eng. 5(6), 914\u2013925 (1993). https:\/\/doi.org\/10.1109\/69.250074","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"4","key":"11_CR3","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.1111\/rssb.12377","volume":"82","author":"DW Apley","year":"2020","unstructured":"Apley, D.W., Zhu, J.: Visualizing the effects of predictor variables in black box supervised learning models. J. R. Stat. Soc. Ser. B Stat Methodol. 82(4), 1059\u20131086 (2020). https:\/\/doi.org\/10.1111\/rssb.12377","journal-title":"J. R. Stat. Soc. Ser. B Stat Methodol."},{"key":"11_CR4","doi-asserted-by":"publisher","first-page":"849","DOI":"10.5555\/1756006.1756034","volume":"11","author":"Y Ben-Haim","year":"2010","unstructured":"Ben-Haim, Y., Tom-Tov, E.: A streaming parallel decision tree algorithm. J. Mach. Learn. Res. 11, 849\u2013872 (2010). https:\/\/doi.org\/10.5555\/1756006.1756034","journal-title":"J. Mach. Learn. Res."},{"key":"11_CR5","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1111\/1745-9133.12047","volume":"12","author":"RA Berk","year":"2013","unstructured":"Berk, R.A., Bleich, J.: Statistical procedures for forecasting criminal behavior: a comparative assessment. Criminol. Public Policy 12, 513 (2013)","journal-title":"Criminol. Public Policy"},{"key":"11_CR6","doi-asserted-by":"publisher","unstructured":"Bifet, A., Gavald\u00e0, R.: Learning from time-changing data with adaptive windowing. In: Proceedings of the Seventh SIAM International Conference on Data Mining (SIAM 2007), pp. 443\u2013448 (2007). https:\/\/doi.org\/10.1137\/1.9781611972771.42","DOI":"10.1137\/1.9781611972771.42"},{"key":"11_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1007\/978-3-642-03915-7_22","volume-title":"Advances in Intelligent Data Analysis VIII","author":"A Bifet","year":"2009","unstructured":"Bifet, A., Gavald\u00e0, R.: Adaptive learning from evolving data streams. In: Adams, N.M., Robardet, C., Siebes, A., Boulicaut, J.-F. (eds.) IDA 2009. LNCS, vol. 5772, pp. 249\u2013260. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-03915-7_22"},{"issue":"1","key":"11_CR8","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001)","journal-title":"Mach. Learn."},{"key":"11_CR9","unstructured":"Britton, M.: VINE: visualizing statistical interactions in black box models. CoRR abs\/1904.00561 (2019). http:\/\/arxiv.org\/abs\/1904.00561"},{"key":"11_CR10","doi-asserted-by":"publisher","unstructured":"Cassidy, A.P., Deviney, F.A.: Calculating feature importance in data streams with concept drift using online random forest. In: 2014 IEEE International Conference on Big Data (Big Data 2014), pp. 23\u201328 (2014). https:\/\/doi.org\/10.1109\/BigData.2014.7004352","DOI":"10.1109\/BigData.2014.7004352"},{"key":"11_CR11","unstructured":"Clements, J.M., Xu, D., Yousefi, N., Efimov, D.: Sequential deep learning for credit risk monitoring with tabular financial data. CoRR abs\/2012.15330 (2020). https:\/\/arxiv.org\/abs\/2012.15330"},{"key":"11_CR12","unstructured":"Covert, I., Lundberg, S.M., Lee, S.: Understanding global feature contributions with additive importance measures. In: Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020 (NeurIPS 2020) (2020)"},{"key":"11_CR13","doi-asserted-by":"publisher","unstructured":"Davari, N., Veloso, B., Ribeiro, R.P., Pereira, P.M., Gama, J.: Predictive maintenance based on anomaly detection using deep learning for air production unit in the railway industry. In: 8th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2021), pp. 1\u201310. IEEE (2021). https:\/\/doi.org\/10.1109\/DSAA53316.2021.9564181","DOI":"10.1109\/DSAA53316.2021.9564181"},{"key":"11_CR14","doi-asserted-by":"publisher","unstructured":"Domingos, P., Hulten, G.: Mining high-speed data streams. In: Proceedings of International Conference on Knowledge Discovery and Data Mining (KDD 2000), pp. 71\u201380 (2000). https:\/\/doi.org\/10.1145\/347090.347107","DOI":"10.1145\/347090.347107"},{"issue":"1","key":"11_CR15","doi-asserted-by":"publisher","first-page":"23017","DOI":"10.1038\/s41598-021-02481-y","volume":"11","author":"C Duckworth","year":"2021","unstructured":"Duckworth, C., et al.: Using explainable machine learning to characterize data drift and detect emergent health risks for emergency department admissions during COVID-19. Sci. Rep. 11(1), 23017 (2021). https:\/\/doi.org\/10.1038\/s41598-021-02481-y","journal-title":"Sci. Rep."},{"issue":"4","key":"11_CR16","doi-asserted-by":"publisher","first-page":"802","DOI":"10.1111\/j.1365-2656.2008.01390.x","volume":"77","author":"J Elith","year":"2008","unstructured":"Elith, J., Leathwick, J.R., Hastie, T.: A working guide to boosted regression trees. J. Anim. Ecol. 77(4), 802\u2013813 (2008). https:\/\/doi.org\/10.1111\/j.1365-2656.2008.01390.x","journal-title":"J. Anim. Ecol."},{"key":"11_CR17","doi-asserted-by":"crossref","unstructured":"Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189\u20131232 (2001). http:\/\/www.jstor.org\/stable\/2699986","DOI":"10.1214\/aos\/1013203451"},{"key":"11_CR18","unstructured":"Frye, C., Mijolla, D.D., Begley, T., Cowton, L., Stanley, M., Feige, I.: Shapley explainability on the data manifold. In: International Conference on Learning Representations (ICLR 2021) (2021). https:\/\/openreview.net\/forum?id=OPyWRrcjVQw"},{"key":"11_CR19","doi-asserted-by":"publisher","unstructured":"Fumagalli, F., Muschalik, M., H\u00fcllermeier, E., Hammer, B.: Incremental permutation feature importance (iPFI): towards online explanations on data streams. CoRR abs\/2209.01939 (2022). https:\/\/doi.org\/10.48550\/arXiv.2209.01939","DOI":"10.48550\/arXiv.2209.01939"},{"key":"11_CR20","doi-asserted-by":"publisher","unstructured":"Gama, J., Zliobaite, I., Bifet, A., Pechenizkiy, M., Bouchachia, A.: A survey on concept drift adaptation. ACM Comput. Surv. 46(4), 44:1\u201344:37 (2014). https:\/\/doi.org\/10.1145\/2523813","DOI":"10.1145\/2523813"},{"key":"11_CR21","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.jpdc.2019.07.007","volume":"134","author":"E Garc\u00eda-Mart\u00edn","year":"2019","unstructured":"Garc\u00eda-Mart\u00edn, E., Rodrigues, C.F., Riley, G., Grahn, H.: Estimation of energy consumption in machine learning. J. Parallel Distrib. Comput. 134, 75\u201388 (2019). https:\/\/doi.org\/10.1016\/j.jpdc.2019.07.007","journal-title":"J. Parallel Distrib. Comput."},{"issue":"1","key":"11_CR22","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1080\/10618600.2014.907095","volume":"24","author":"A Goldstein","year":"2015","unstructured":"Goldstein, A., Kapelner, A., Bleich, J., Pitkin, E.: Peeking inside the black box: visualizing statistical learning with plots of individual conditional expectation. J. Comput. Graph. Stat. 24(1), 44\u201365 (2015). https:\/\/doi.org\/10.1080\/10618600.2014.907095","journal-title":"J. Comput. Graph. Stat."},{"issue":"9","key":"11_CR23","doi-asserted-by":"publisher","first-page":"1469","DOI":"10.1007\/s10994-017-5642-8","volume":"106","author":"HM Gomes","year":"2017","unstructured":"Gomes, H.M., et al.: Adaptive random forests for evolving data stream classification. Mach. Learn. 106(9), 1469\u20131495 (2017)","journal-title":"Mach. Learn."},{"key":"11_CR24","doi-asserted-by":"crossref","unstructured":"Gomes, H.M., Mello, R.F.D., Pfahringer, B., Bifet, A.: Feature scoring using tree-based ensembles for evolving data streams. In: 2019 IEEE International Conference on Big Data (Big Data 2019), pp. 761\u2013769 (2019)","DOI":"10.1109\/BigData47090.2019.9006366"},{"key":"11_CR25","unstructured":"Greenwell, B.M., Boehmke, B.C., McCarthy, A.J.: A simple and effective model-based variable importance measure. CoRR abs\/1805.04755 (2018). http:\/\/arxiv.org\/abs\/1805.04755"},{"key":"11_CR26","unstructured":"Gr\u00f6mping, U.: Model-agnostic effects plots for interpreting machine learning models. In: Reports in Mathematics, Physics and Chemistry: Department II. Beuth University of Applied Sciences Berlin (2020). http:\/\/www1.beuth-hochschule.de\/FB_II\/reports\/"},{"key":"11_CR27","unstructured":"Harries, M.: SPLICE-2 comparative evaluation: electricity pricing. Technical report, The University of South Wales (1999)"},{"key":"11_CR28","doi-asserted-by":"publisher","unstructured":"Haug, J., Braun, A., Z\u00fcrn, S., Kasneci, G.: Change detection for local explainability in evolving data streams. In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management (CIKIM 2022), pp. 706\u2013716. ACM (2022). https:\/\/doi.org\/10.1145\/3511808.3557257","DOI":"10.1145\/3511808.3557257"},{"key":"11_CR29","unstructured":"Herbinger, J., Bischl, B., Casalicchio, G.: REPID: regional effect plots with implicit interaction detection. In: International Conference on Artificial Intelligence and Statistics, (AISTATS 2022). Proceedings of Machine Learning Research, vol. 151, pp. 10209\u201310233. PMLR (2022). https:\/\/proceedings.mlr.press\/v151\/herbinger22a.html"},{"key":"11_CR30","doi-asserted-by":"publisher","unstructured":"Hinder, F., Vaquet, V., Brinkrolf, J., Hammer, B.: Model based explanations of concept drift. CoRR abs\/2303.09331 (2023). https:\/\/doi.org\/10.48550\/arXiv.2303.09331","DOI":"10.48550\/arXiv.2303.09331"},{"key":"11_CR31","doi-asserted-by":"publisher","unstructured":"Hulten, G., Spencer, L., Domingos, P.: Mining time-changing data streams. In: Proceedings of International Conference on Knowledge Discovery and Data Mining (KDD 2001), pp. 97\u2013106 (2001). https:\/\/doi.org\/10.1145\/502512.502529","DOI":"10.1145\/502512.502529"},{"key":"11_CR32","unstructured":"Janzing, D., Minorics, L., Bl\u00f6baum, P.: Feature relevance quantification in explainable AI: a causal problem. In: International Conference on Artificial Intelligence and Statistics (AISTATS 2020). Proceedings of Machine Learning Research, vol. 108, pp. 2907\u20132916. PMLR (2020). http:\/\/proceedings.mlr.press\/v108\/janzing20a"},{"key":"11_CR33","doi-asserted-by":"publisher","first-page":"1261","DOI":"10.1016\/j.neucom.2017.06.084","volume":"275","author":"V Losing","year":"2018","unstructured":"Losing, V., Hammer, B., Wersing, H.: Incremental on-line learning: a review and comparison of state of the art algorithms. Neurocomputing 275, 1261\u20131274 (2018). https:\/\/doi.org\/10.1016\/j.neucom.2017.06.084","journal-title":"Neurocomputing"},{"key":"11_CR34","doi-asserted-by":"publisher","unstructured":"Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, G.: Learning under concept drift: a review. IEEE Trans. Knowl. Data Eng. 2346\u20132363 (2018). https:\/\/doi.org\/10.1109\/TKDE.2018.2876857","DOI":"10.1109\/TKDE.2018.2876857"},{"key":"11_CR35","unstructured":"Lundberg, S.M., Erion, G.G., Lee, S.: Consistent individualized feature attribution for tree ensembles. CoRR abs\/1802.03888 (2018). http:\/\/arxiv.org\/abs\/1802.03888"},{"key":"11_CR36","unstructured":"Molnar, C.: Interpretable Machine Learning, 2 edn. (2022). Lulu.com, https:\/\/christophm.github.io\/interpretable-ml-book"},{"key":"11_CR37","unstructured":"Molnar, C., K\u00f6nig, G., Bischl, B., Casalicchio, G.: Model-agnostic feature importance and effects with dependent features - a conditional subgroup approach. CoRR abs\/2006.04628 (2020). https:\/\/arxiv.org\/abs\/2006.04628"},{"key":"11_CR38","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/978-3-031-04083-2_4","volume-title":"xxAI - Beyond Explainable AI","author":"C Molnar","year":"2020","unstructured":"Molnar, C., et al.: General pitfalls of model-agnostic interpretation methods for machine learning models. In: Holzinger, A., Goebel, R., Fong, R., Moon, T., M\u00fcller, K.R., Samek, W. (eds.) xxAI 2020. LNCS, vol. 13200, pp. 39\u201368. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-031-04083-2_4"},{"key":"11_CR39","unstructured":"Moosbauer, J., Herbinger, J., Casalicchio, G., Lindauer, M., Bischl, B.: Explaining hyperparameter optimization via partial dependence plots. In: Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021 (NeurIPS 2021), pp. 2280\u20132291 (2021). https:\/\/proceedings.neurips.cc\/paper\/2021\/hash\/12ced2db6f0193dda91ba86224ea1cd8-Abstract.html"},{"issue":"3","key":"11_CR40","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s13218-022-00766-6","volume":"36","author":"M Muschalik","year":"2022","unstructured":"Muschalik, M., Fumagalli, F., Hammer, B., H\u00fcllermeier, E.: Agnostic explanation of model change based on feature importance. K\u00fcnstliche Intell. 36(3), 211\u2013224 (2022). https:\/\/doi.org\/10.1007\/s13218-022-00766-6","journal-title":"K\u00fcnstliche Intell."},{"key":"11_CR41","doi-asserted-by":"publisher","unstructured":"Muschalik, M., Fumagalli, F., Hammer, B., H\u00fcllermeier, E.: iSAGE: an incremental version of SAGE for online explanation on data streams. CoRR abs\/2303.01181 (2023). https:\/\/doi.org\/10.48550\/arXiv.2303.01181","DOI":"10.48550\/arXiv.2303.01181"},{"key":"11_CR42","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.neunet.2019.01.012","volume":"113","author":"GI Parisi","year":"2019","unstructured":"Parisi, G.I., Kemker, R., Part, J.L., Kanan, C., Wermter, S.: Continual lifelong learning with neural networks: a review. Neural Netw. 113, 54\u201371 (2019). https:\/\/doi.org\/10.1016\/j.neunet.2019.01.012","journal-title":"Neural Netw."},{"key":"11_CR43","doi-asserted-by":"publisher","first-page":"116565","DOI":"10.1016\/j.apenergy.2021.116565","volume":"287","author":"J Rouleau","year":"2021","unstructured":"Rouleau, J., Gosselin, L.: Impacts of the COVID-19 lockdown on energy consumption in a Canadian social housing building. Appl. Energy 287, 116565 (2021). https:\/\/doi.org\/10.1016\/j.apenergy.2021.116565","journal-title":"Appl. Energy"},{"issue":"1","key":"11_CR44","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1140\/epjds\/s13688-023-00387-5","volume":"12","author":"T Susnjak","year":"2023","unstructured":"Susnjak, T., Maddigan, P.: Forecasting patient flows with pandemic induced concept drift using explainable machine learning. EPJ Data Sci. 12(1), 11 (2023). https:\/\/doi.org\/10.1140\/epjds\/s13688-023-00387-5","journal-title":"EPJ Data Sci."},{"key":"11_CR45","doi-asserted-by":"publisher","unstructured":"Ta, V.D., Liu, C.M., Nkabinde, G.W.: Big data stream computing in healthcare real-time analytics. In: Proceddings of International Conference on Cloud Computing and Big Data Analysis (ICCCBDA 2016), pp. 37\u201342 (2016). https:\/\/doi.org\/10.1109\/ICCCBDA.2016.7529531","DOI":"10.1109\/ICCCBDA.2016.7529531"},{"key":"11_CR46","doi-asserted-by":"publisher","first-page":"127343","DOI":"10.1016\/j.physa.2022.127343","volume":"598","author":"X Zhao","year":"2022","unstructured":"Zhao, X., Yang, H., Yao, Y., Qi, H., Guo, M., Su, Y.: Factors affecting traffic risks on bridge sections of freeways based on partial dependence plots. Phys. A 598, 127343 (2022). https:\/\/doi.org\/10.1016\/j.physa.2022.127343","journal-title":"Phys. A"}],"container-title":["Communications in Computer and Information Science","Explainable Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44064-9_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,29]],"date-time":"2023-10-29T04:20:43Z","timestamp":1698553243000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44064-9_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031440632","9783031440649"],"references-count":46,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44064-9_11","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"30 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"xAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"World Conference on Explainable Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lisbon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"xai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/xaiworldconference.com\/","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":"220","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":"94","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":"43% - 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":"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":"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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}