{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T05:00:18Z","timestamp":1743051618680,"version":"3.40.3"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319417059"},{"type":"electronic","value":"9783319417066"}],"license":[{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016]]},"DOI":"10.1007\/978-3-319-41706-6_11","type":"book-chapter","created":{"date-parts":[[2016,7,2]],"date-time":"2016-07-02T11:54:11Z","timestamp":1467460451000},"page":"221-233","source":"Crossref","is-referenced-by-count":10,"title":["On Event Detection from Spatial Time Series for Urban Traffic Applications"],"prefix":"10.1007","author":[{"given":"Gustavo","family":"Souto","sequence":"first","affiliation":[]},{"given":"Thomas","family":"Liebig","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2016,7,3]]},"reference":[{"key":"11_CR1","volume-title":"Outlier Detection","author":"CC Aggarwal","year":"2013","unstructured":"Aggarwal, C.C.: Outlier Detection. Springer, New York (2013)"},{"key":"11_CR2","doi-asserted-by":"crossref","unstructured":"Allen, J.F.: Maintaining knowledge about temporal intervals. Commun. ACM 26(11), 832\u2013843 (1983). http:\/\/doi.acm.org\/10.1145\/182.358434","DOI":"10.1145\/182.358434"},{"key":"11_CR3","doi-asserted-by":"crossref","unstructured":"Artikis, A., Weidlich, M., Gal, A., Kalogeraki, V., Gunopulos, D.: Self-adaptive event recognition for intelligent transport management. In: 2013 IEEE International Conference on Big Data, pp. 319\u2013325, October 2013","DOI":"10.1109\/BigData.2013.6691590"},{"key":"11_CR4","unstructured":"Artikis, A., Weidlich, M., Schnitzler, F., Boutsis, I., Liebig, T., Piatkowski, N., Bockermann, C., Morik, K., Kalogeraki, V., Marecek, J., Gal, A., Mannor, S., Gunopulos, D., Kinane, D.: Heterogeneous stream processing and crowdsourcing for urban traffic management. In: Proceedings of 17th International Conference on Extending Database Technology (EDBT), Athens, Greece, 24\u201328 March 2014, pp. 712\u2013723 (2014). OpenProceedings.org"},{"key":"11_CR5","unstructured":"Berry, J.K.: Gis modeling and analysis. In: Madden, M. (ed.) Manual of Geographic Information Systems, pp. 527\u2013585. American Society for Photogrammetry and Remote Sensing (2009). http:\/\/books.google.de\/books?id=ek-IQAAACAAJ"},{"key":"11_CR6","unstructured":"Bifet, A., Kirkby, R.: Data stream mining a practical approach (2009)"},{"key":"11_CR7","unstructured":"Bockermann, C.: A survey of the stream processing landscape. Technical report 6, TU Dortmund University, May 2014. http:\/\/www-ai.cs.uni-dortmund.de\/PublicPublicationFiles\/bockermann_2014b.pdf"},{"key":"11_CR8","unstructured":"Demers, A., Gehrke, J., Panda, B., Riedewald, M., Sharma, V., White, W.: Cayuga: a general purpose event monitoring system, pp. 412\u2013422 (2007)"},{"key":"11_CR9","unstructured":"Diao, Y., Immerman, N., Gyllstrom, D.: Sase+: an agile language for kleene closure over event streams. Analysis (UM-CS-07-03), 1\u201313 (2007)"},{"issue":"3\u20134","key":"11_CR10","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1057\/PALGRAVE.IVS.9500182","volume":"7","author":"S Dodge","year":"2008","unstructured":"Dodge, S., Weibel, R., Lautensch\u00fctz, A.K.: Towards a taxonomy of movement patterns. Inf. Vis. 7(3\u20134), 240\u2013252 (2008)","journal-title":"Inf. Vis."},{"key":"11_CR11","doi-asserted-by":"crossref","unstructured":"Dongre, P.B., Makik, L.G.: A review on real time data stream classification and adapting to various concept drift scenarios. In: IEEE International Advance Computing Conference, vol. 1, pp. 533\u2013537, February 2014","DOI":"10.1109\/IAdCC.2014.6779381"},{"key":"11_CR12","unstructured":"Florescu, S., K\u00f6rner, C., Mock, M., May, M.: Efficient mobility pattern stream matching on mobile devices. In: Proceedings of the Ubiquitous Data Mining Workshop (UDM 2012), pp. 23\u201327 (2012)"},{"key":"11_CR13","doi-asserted-by":"crossref","unstructured":"Fuchs, G., Andrienko, N., Andrienko, G., Bothe, S., Stange, H.: Tracing the German centennial flood in the stream of tweets: first lessons learned (2013)","DOI":"10.1145\/2534732.2534741"},{"key":"11_CR14","doi-asserted-by":"crossref","unstructured":"Gal, A., Keren, S., Sondak, M., Weidlich, M., Blom, H., Bockermann, C.: Grand challenge: the techniball system. In: Proceedings of the 7th ACM International Conference on Distributed Event-Based Systems, DEBS 2013, pp. 319\u2013324. ACM, New York (2013)","DOI":"10.1145\/2488222.2488282"},{"key":"11_CR15","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.trc.2014.07.005","volume":"50","author":"J Guo","year":"2014","unstructured":"Guo, J., Huang, W., Williams, B.M.: Real time traffic flow outlier detection using short-term traffic conditional variance prediction. Transp. Res. Part C Emerg. Technol. 50, 160\u2013172 (2014)","journal-title":"Transp. Res. Part C Emerg. Technol."},{"issue":"1","key":"11_CR16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2200\/S00573ED1V01Y201403DMK008","volume":"5","author":"M Gupta","year":"2014","unstructured":"Gupta, M., Gao, J., Aggarwal, C., Han, J.: Outlier detection for temporal data. Synth. Lect. Data Min. Knowl. Disc. 5(1), 1\u2013129 (2014)","journal-title":"Synth. Lect. Data Min. Knowl. Disc."},{"key":"11_CR17","first-page":"407","volume":"1","author":"D Gyllstrom","year":"2007","unstructured":"Gyllstrom, D., Diao, Y., Wu, E., Stahlberg, P., Anderson, G.: SASE: complex event processing over streams. Science 1, 407\u2013411 (2007)","journal-title":"Science"},{"key":"11_CR18","doi-asserted-by":"crossref","unstructured":"Gyllstrom, D., Agrawal, J., Diao, Y., Immerman, N.: On supporting kleene closure over event streams. In: Alonso, G., Blakeley, J.A., Chen, A.L.P. (eds.) ICDE, pp. 1391\u20131393. IEEE (2008)","DOI":"10.1109\/ICDE.2008.4497566"},{"key":"11_CR19","unstructured":"Liebig, T., Morik, K.: Report on end-user requirements, test data, and on prototype definitions. Technical report FP7-318225 D5.1, TU Dortmund and Insight Consortium Members, August 2013"},{"key":"11_CR20","unstructured":"Liebig, T., Piatkowski, N., Bockermann, C., Morik, K.: Route planning with real-time traffic predictions. In: Proceedings of the 16th LWA Workshops: KDML, IR and FGWM, pp. 83\u201394 (2014)"},{"key":"11_CR21","doi-asserted-by":"crossref","unstructured":"Liu, W., Zheng, Y., Chawla, S., Yuan, J., Xing, X.: Discovering spatio-temporal causal interactions in traffic data streams. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2011, NY, USA, pp. 1010\u20131018 (2011). http:\/\/doi.acm.org\/10.1145\/2020408.2020571","DOI":"10.1145\/2020408.2020571"},{"key":"11_CR22","unstructured":"Marz, N.: Big Data: Principles and Best Practices of Scalable Realtime Data Systems. O\u2019Reilly Media, Sebastopol (2013). http:\/\/www.amazon.de\/Big-Data-Principles-Practices-Scalable\/dp\/1617290343"},{"key":"11_CR23","doi-asserted-by":"crossref","unstructured":"du Mouza, C., Rigaux, P., Scholl, M.: Efficient evaluation of parameterized pattern queries. In: Herzog, O., Schek, H., Fuhr, N., Chowdhury, A., Teiken, W. (eds.) CIKM, pp. 728\u2013735. ACM (2005)","DOI":"10.1145\/1099554.1099731"},{"key":"11_CR24","doi-asserted-by":"crossref","unstructured":"Pan, B., Zheng, Y., Wilkie, D., Shahabi, C.: Crowd sensing of traffic anomalies based on human mobility and social media. In: Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2013, NY, USA, pp. 344\u2013353 (2013). http:\/\/doi.acm.org\/10.1145\/2525314.2525343","DOI":"10.1145\/2525314.2525343"},{"key":"11_CR25","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.datak.2013.05.002","volume":"87","author":"LX Pang","year":"2013","unstructured":"Pang, L.X., Chawla, S., Liu, W., Zheng, Y.: On detection of emerging anomalous traffic patterns using GPS data. Data Knowl. Eng. 87, 357\u2013373 (2013). http:\/\/dx.doi.org\/10.1016\/j.datak.2013.05.002","journal-title":"Data Knowl. Eng."},{"key":"11_CR26","doi-asserted-by":"crossref","unstructured":"Passow, B.N., Elizondo, D., Chiclana, F., Witheridge, S., Goodyer, E.: Adapting traffic simulation for traffic management: a neural network approach. In: IEEE Annual Conference on Intelligent Transportation Systems (ITSC 2013), pp. 1402\u20131407, October 2013","DOI":"10.1109\/ITSC.2013.6728427"},{"key":"11_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1007\/978-3-642-34156-4_25","volume-title":"Advances in Intelligent Data Analysis XI","author":"S Peter","year":"2012","unstructured":"Peter, S., H\u00f6ppner, F., Berthold, M.R.: Learning pattern graphs for multivariate temporal pattern retrieval. In: Hollm\u00e9n, J., Klawonn, F., Tucker, A. (eds.) IDA 2012. LNCS, vol. 7619, pp. 264\u2013275. Springer, Heidelberg (2012)"},{"key":"11_CR28","unstructured":"Randell, D.A., Cui, Z., Cohn, A.G.: A spatial logic based on regions and connection. In: Nebel, B., Rich, C., Swartout, W.R. (eds.) KR, pp. 165\u2013176. Morgan Kaufmann (1992)"},{"key":"11_CR29","volume-title":"Data Stream Mining: A Practical Approach","author":"A Bifet","year":"2011","unstructured":"Bifet, A., Kirkby, R., Pfahringer, B.: Data Stream Mining: A Practical Approach. The University of Waikato, Hamilton (2011)"},{"issue":"3","key":"11_CR30","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1007\/s10707-010-0114-3","volume":"15","author":"MA Sakr","year":"2011","unstructured":"Sakr, M.A., G\u00fcting, R.H.: Spatiotemporal pattern queries. GeoInformatica 15(3), 497\u2013540 (2011)","journal-title":"GeoInformatica"},{"key":"11_CR31","doi-asserted-by":"crossref","unstructured":"Schnitzler, F., Liebig, T., Mannor, S., Souto, G., Bothe, S., Stange, H.: Heterogeneous stream processing for disaster detection and alarming. In: IEEE International Conference on Big Data, pp. 914\u2013923. IEEE Press (2014)","DOI":"10.1109\/BigData.2014.7004323"},{"key":"11_CR32","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/978-3-642-24908-2_19","volume-title":"Rule-Based Modeling and Computing on the Semantic Web","author":"A Skarlatidis","year":"2011","unstructured":"Skarlatidis, A., Paliouras, G., Vouros, G.A., Artikis, A.: Probabilistic event calculus based on Markov logic networks. In: Palmirani, M. (ed.) RuleML - America 2011. LNCS, vol. 7018, pp. 155\u2013170. Springer, Heidelberg (2011)"},{"key":"11_CR33","doi-asserted-by":"crossref","unstructured":"Trilles, S., Schade, S., Belmonte, \u00d3., Huerta, J.: Real-time anomaly detection from environmental data streams. In: Bacao, F., Santos, M.Y., Painho, M. (eds.) AGILE 2015. Lecture Notes in Geoinformation and Cartography, pp. 125\u2013144. Springer International Publishing, Cham (2015). http:\/\/dx.doi.org\/10.1007\/978-3-319-16787-9_8","DOI":"10.1007\/978-3-319-16787-9_8"},{"issue":"5","key":"11_CR34","doi-asserted-by":"crossref","first-page":"1936","DOI":"10.1109\/TITS.2014.2305334","volume":"15","author":"S Yang","year":"2014","unstructured":"Yang, S., Kalpakis, K., Biem, A.: Detecting road traffic events by coupling multiple timeseries with a nonparametric Bayesian method. IEEE Trans. Intell. Transp. Syst. 15(5), 1936\u20131946 (2014)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"11_CR35","unstructured":"Yang, S., Liu, W.: Anomaly detection on collective moving patterns. In: IEEE International Conference on Privacy, Security, Risk, and Trust and IEEE International Conference on Social Computing, vol. 7, pp. 291\u2013296 (2011)"},{"key":"11_CR36","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Guan, W.: Outlier detection of handover data for innersuburban freeway traffic information estimation using mobile probes. In: IEEE Vehicular Technology Conference (VTC Spring), pp. 1\u20135, May 2011","DOI":"10.1109\/VETECS.2011.5956321"}],"container-title":["Lecture Notes in Computer Science","Solving Large Scale Learning Tasks. Challenges and Algorithms"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-41706-6_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,10]],"date-time":"2019-09-10T12:13:31Z","timestamp":1568117611000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-41706-6_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"ISBN":["9783319417059","9783319417066"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-41706-6_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2016]]}}}