{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T04:20:46Z","timestamp":1743135646507,"version":"3.40.3"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031424472"},{"type":"electronic","value":"9783031424489"}],"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\/978-3-031-42448-9_7","type":"book-chapter","created":{"date-parts":[[2023,9,10]],"date-time":"2023-09-10T23:02:21Z","timestamp":1694386941000},"page":"72-84","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Trend Detection in\u00a0Crime-Related Time Series with\u00a0Change Point Detection Methods"],"prefix":"10.1007","author":[{"given":"Apostolos","family":"Konstantinou","sequence":"first","affiliation":[]},{"given":"Despoina","family":"Chatzakou","sequence":"additional","affiliation":[]},{"given":"Ourania","family":"Theodosiadou","sequence":"additional","affiliation":[]},{"given":"Theodora","family":"Tsikrika","sequence":"additional","affiliation":[]},{"given":"Stefanos","family":"Vrochidis","sequence":"additional","affiliation":[]},{"given":"Ioannis","family":"Kompatsiaris","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,11]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Abrar, M.F., Arefin, M.S., Hossain, M.S.: A framework for analyzing real-time tweets to detect terrorist activities. In: ECCE, pp. 1\u20136. IEEE (2019)","key":"7_CR1","DOI":"10.1109\/ECACE.2019.8679430"},{"issue":"2","key":"7_CR2","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1007\/s10115-016-0987-z","volume":"51","author":"S Aminikhanghahi","year":"2017","unstructured":"Aminikhanghahi, S., Cook, D.J.: A survey of methods for time series change point detection. Knowl. Inf. Syst. 51(2), 339\u2013367 (2017)","journal-title":"Knowl. Inf. Syst."},{"issue":"3","key":"7_CR3","first-page":"208","volume":"22","author":"SA Asongu","year":"2019","unstructured":"Asongu, S.A., Orim, S.M.I., Nting, R.T.: Terrorism and social media: global evidence. J. Glob. Inf. Technol. 22(3), 208\u2013228 (2019)","journal-title":"J. Glob. Inf. Technol."},{"issue":"1","key":"7_CR4","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/S0092-8240(89)80047-3","volume":"51","author":"IE Auger","year":"1989","unstructured":"Auger, I.E., Lawrence, C.E.: Algorithms for the optimal identification of segment neighborhoods. Bull. Math. Biol. 51(1), 39\u201354 (1989)","journal-title":"Bull. Math. Biol."},{"issue":"1","key":"7_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/19361610.2020.1857170","volume":"16","author":"D Augusto","year":"2021","unstructured":"Augusto, D.: Change-point analysis of Houston crime during hurricane Harvey. J. Appl. Secur. Research 16(1), 1\u201318 (2021)","journal-title":"J. Appl. Secur. Research"},{"doi-asserted-by":"crossref","unstructured":"Borowik, G., Wawrzyniak, Z.M., Cichosz, P.: Time series analysis for crime forecasting. In: ICSEng, pp. 1\u201310. IEEE (2018)","key":"7_CR6","DOI":"10.1109\/ICSENG.2018.8638179"},{"unstructured":"Boston, A.: Crimes in boston (2023). https:\/\/kaggle.com\/datasets\/AnalyzeBoston\/crimes-in-boston","key":"7_CR7"},{"issue":"3","key":"7_CR8","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1016\/S0267-3649(03)00306-6","volume":"19","author":"K Burden","year":"2003","unstructured":"Burden, K., Palmer, C.: Internet crime: cyber crime-a new breed of criminal? Comput. Law Secur. Rev. 19(3), 222\u2013227 (2003)","journal-title":"Comput. Law Secur. Rev."},{"unstructured":"Van den Burg, G.J., Williams, C.K.: An evaluation of change point detection algorithms. arXiv preprint arXiv:2003.06222 (2020)","key":"7_CR9"},{"doi-asserted-by":"crossref","unstructured":"Burkatovskaya, Y., Kabanova, T., Khaustov, P.: Choice of the parameters of the CUSUM algorithms for parameter estimation in the Markov modulated poisson process. In: ITSMSSM, pp. 456\u2013462. Atlantis Press (2016)","key":"7_CR10","DOI":"10.2991\/itsmssm-16.2016.90"},{"key":"7_CR11","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/s10772-011-9116-2","volume":"15","author":"MFR Chowdhury","year":"2012","unstructured":"Chowdhury, M.F.R., Selouani, S.A., O\u2019Shaughnessy, D.: Bayesian on-line spectral change point detection: a soft computing approach for on-line ASR. Int. J. Speech Technol. 15, 5\u201323 (2012)","journal-title":"Int. J. Speech Technol."},{"issue":"9","key":"7_CR12","doi-asserted-by":"publisher","first-page":"15861","DOI":"10.3390\/s140915861","volume":"14","author":"I Cleland","year":"2014","unstructured":"Cleland, I., et al.: Evaluation of prompted annotation of activity data recorded from a smart phone. Sensors 14(9), 15861\u201315879 (2014)","journal-title":"Sensors"},{"unstructured":"Dane, S.: London police records (2023). https:\/\/kaggle.com\/datasets\/sohier\/london-police-records","key":"7_CR13"},{"issue":"7","key":"7_CR14","first-page":"919","volume":"26","author":"TR Derrick","year":"1994","unstructured":"Derrick, T.R., Bates, B.T., Dufek, J.S.: Evaluation of time-series data sets using the Pearson product-moment correlation coefficient. MSSE 26(7), 919\u2013928 (1994)","journal-title":"MSSE"},{"unstructured":"Granjon, P.: The CUSUM algorithm-a small review (2013)","key":"7_CR15"},{"key":"7_CR16","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1007\/s10994-010-5230-7","volume":"83","author":"M Grzegorczyk","year":"2011","unstructured":"Grzegorczyk, M., Husmeier, D.: Non-homogeneous dynamic Bayesian networks for continuous data. Mach. Learn. 83, 355\u2013419 (2011)","journal-title":"Mach. Learn."},{"issue":"9","key":"7_CR17","doi-asserted-by":"publisher","first-page":"12588","DOI":"10.3390\/s120912588","volume":"12","author":"M Han","year":"2012","unstructured":"Han, M., Vinh, L.T., Lee, Y.K., Lee, S.: Comprehensive context recognizer based on multimodal sensors in a smartphone. Sensors 12(9), 12588\u201312605 (2012)","journal-title":"Sensors"},{"issue":"9","key":"7_CR18","doi-asserted-by":"publisher","first-page":"2242","DOI":"10.1109\/TIFS.2017.2704906","volume":"12","author":"X Han","year":"2017","unstructured":"Han, X., Wang, L., Cui, C., Ma, J., Zhang, S.: Linking multiple online identities in criminal investigations: a spectral co-clustering framework. IEEE Trans. Inf. Forensics Secur. 12(9), 2242\u20132255 (2017)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"issue":"1","key":"7_CR19","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1080\/10618600.2015.1116445","volume":"26","author":"K Haynes","year":"2017","unstructured":"Haynes, K., Eckley, I.A., Fearnhead, P.: Computationally efficient changepoint detection for a range of penalties. J. Comput. Graph. Stat. 26(1), 134\u2013143 (2017)","journal-title":"J. Comput. Graph. Stat."},{"issue":"1","key":"7_CR20","first-page":"61","volume":"39","author":"SY Jeon","year":"2020","unstructured":"Jeon, S.Y., Ryou, H.S., Kim, Y., Oh, K.J.: Using change-point detection to identify structural changes in stock market: application to Russell 2000. Quant. Bio-Sci. 39(1), 61\u201369 (2020)","journal-title":"Quant. Bio-Sci."},{"doi-asserted-by":"crossref","unstructured":"Keogh, E., Chu, S., Hart, D., Pazzani, M.: An online algorithm for segmenting time series. In: International Conference on Data Mining, pp. 289\u2013296. IEEE (2001)","key":"7_CR21","DOI":"10.1109\/ICDM.2001.989531"},{"issue":"500","key":"7_CR22","doi-asserted-by":"publisher","first-page":"1590","DOI":"10.1080\/01621459.2012.737745","volume":"107","author":"R Killick","year":"2012","unstructured":"Killick, R., Fearnhead, P., Eckley, I.A.: Optimal detection of changepoints with a linear computational cost. JASA 107(500), 1590\u20131598 (2012)","journal-title":"JASA"},{"unstructured":"Knoblauch, J., Damoulas, T.: Spatio-temporal Bayesian on-line changepoint detection with model selection. In: International Conference on Machine Learning, pp. 2718\u20132727. PMLR (2018)","key":"7_CR23"},{"issue":"4","key":"7_CR24","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1080\/07418825.2019.1666903","volume":"38","author":"R Lu","year":"2021","unstructured":"Lu, R., et al.: The cannabis effect on crime: time-series analysis of crime in Colorado and Washington state. Justice Q. 38(4), 565\u2013595 (2021)","journal-title":"Justice Q."},{"issue":"2","key":"7_CR25","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1007\/s11222-011-9240-5","volume":"22","author":"A Lung-Yut-Fong","year":"2012","unstructured":"Lung-Yut-Fong, A., L\u00e9vy-Leduc, C., Capp\u00e9, O.: Distributed detection\/localization of change-points in high-dimensional network traffic data. Stat. Comput. 22(2), 485\u2013496 (2012)","journal-title":"Stat. Comput."},{"unstructured":"Maidstone, R.: Efficient Analysis of Complex Changepoint Problems. Lancaster University (United Kingdom) (2016)","key":"7_CR26"},{"key":"7_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40163-015-0023-8","volume":"4","author":"N Malleson","year":"2015","unstructured":"Malleson, N., Andresen, M.A.: Spatio-temporal crime hotspots and the ambient population. Crime Sci. 4, 1\u20138 (2015)","journal-title":"Crime Sci."},{"issue":"1\/2","key":"7_CR28","doi-asserted-by":"publisher","first-page":"100","DOI":"10.2307\/2333009","volume":"41","author":"ES Page","year":"1954","unstructured":"Page, E.S.: Continuous inspection schemes. Biometrika 41(1\/2), 100\u2013115 (1954)","journal-title":"Biometrika"},{"issue":"2","key":"7_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1689239.1689243","volume":"6","author":"S Reddy","year":"2010","unstructured":"Reddy, S., Mun, M., Burke, J., Estrin, D., Hansen, M., Srivastava, M.: Using mobile phones to determine transportation modes. TOSN 6(2), 1\u201327 (2010)","journal-title":"TOSN"},{"key":"7_CR30","first-page":"1","volume":"24","author":"G Romano","year":"2023","unstructured":"Romano, G., Eckley, I.A., Fearnhead, P., Rigaill, G.: Fast online changepoint detection via functional pruning CUSUM statistics. J. Mach. Learn. Res. 24, 1\u201336 (2023)","journal-title":"J. Mach. Learn. Res."},{"key":"7_CR31","doi-asserted-by":"publisher","first-page":"507","DOI":"10.2307\/2529204","volume":"30","author":"AJ Scott","year":"1974","unstructured":"Scott, A.J., Knott, M.: A cluster analysis method for grouping means in the analysis of variance. Biometrics 30, 507\u2013512 (1974)","journal-title":"Biometrics"},{"doi-asserted-by":"crossref","unstructured":"Shamsuddin, N.H.M., Ali, N.A., Alwee, R.: An overview on crime prediction methods. In: ICT-ISPC, pp. 1\u20135. IEEE (2017)","key":"7_CR32","DOI":"10.1109\/ICT-ISPC.2017.8075335"},{"issue":"4","key":"7_CR33","first-page":"482","volume":"18","author":"JI Takeuchi","year":"2006","unstructured":"Takeuchi, J.I., Yamanishi, K.: A unifying framework for detecting outliers and change points from time series. TKDE 18(4), 482\u2013492 (2006)","journal-title":"TKDE"},{"issue":"1","key":"7_CR34","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1080\/00031305.2017.1380080","volume":"72","author":"SJ Taylor","year":"2018","unstructured":"Taylor, S.J., Letham, B.: Forecasting at scale. Am. Stat. 72(1), 37\u201345 (2018)","journal-title":"Am. Stat."},{"issue":"7","key":"7_CR35","doi-asserted-by":"publisher","first-page":"274","DOI":"10.3390\/info12070274","volume":"12","author":"O Theodosiadou","year":"2021","unstructured":"Theodosiadou, O., et al.: Change point detection in terrorism-related online content using deep learning derived indicators. Information 12(7), 274 (2021)","journal-title":"Information"},{"issue":"6","key":"7_CR36","doi-asserted-by":"publisher","first-page":"581","DOI":"10.11648\/j.ajtas.20150406.30","volume":"4","author":"GD Wambui","year":"2015","unstructured":"Wambui, G.D., Waititu, G.A., Wanjoya, A.: The power of the pruned exact linear time (pelt) test in multiple changepoint detection. AJTAS 4(6), 581 (2015)","journal-title":"AJTAS"},{"unstructured":"Wei, S., Xie, Y.: Online kernel CUSUM for change-point detection. arXiv preprint arXiv:2211.15070 (2022)","key":"7_CR37"}],"container-title":["Lecture Notes in Computer Science","Experimental IR Meets Multilinguality, Multimodality, and Interaction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-42448-9_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,27]],"date-time":"2024-10-27T21:04:57Z","timestamp":1730063097000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-42448-9_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031424472","9783031424489"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-42448-9_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"11 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CLEF","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference of the Cross-Language Evaluation Forum for European Languages","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Thessaloniki","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","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":"18 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"clef2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/clef2023.clef-initiative.eu\/","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":"35","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":"10","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":"29% - 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","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)"}},{"value":"7 Best of 2022 Labs + 13 Lab Overviews","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}