{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T02:21:39Z","timestamp":1790302899898,"version":"4.1.0"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783319936970","type":"print"},{"value":"9783319936987","type":"electronic"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"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":[[2018]]},"DOI":"10.1007\/978-3-319-93698-7_40","type":"book-chapter","created":{"date-parts":[[2018,6,11]],"date-time":"2018-06-11T19:30:25Z","timestamp":1528745425000},"page":"524-539","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["RT-DBSCAN: Real-Time Parallel Clustering of Spatio-Temporal Data Using Spark-Streaming"],"prefix":"10.1007","author":[{"given":"Yikai","family":"Gong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard O.","family":"Sinnott","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul","family":"Rimba","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2018,6,12]]},"reference":[{"issue":"2","key":"40_CR1","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1145\/304181.304187","volume":"28","author":"Mihael Ankerst","year":"1999","unstructured":"Ankerst, M., Breunig, M.M., Kriegel, H.-P., Sander, J.: Optics: ordering points to identify the clustering structure. In: ACM Sigmod Record, vol. 28, pp. 49\u201360. ACM (1999)","journal-title":"ACM SIGMOD Record"},{"issue":"1","key":"40_CR2","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1016\/j.datak.2006.01.013","volume":"60","author":"D Birant","year":"2007","unstructured":"Birant, D., Kut, A.: ST-DBSCAN: an algorithm for clustering spatial-temporal data. Data Knowl. Eng. 60(1), 208\u2013221 (2007)","journal-title":"Data Knowl. Eng."},{"key":"40_CR3","doi-asserted-by":"crossref","unstructured":"Chandra, B.: Hybrid clustering algorithm. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pp. 1345\u20131348. IEEE (2009)","DOI":"10.1109\/ICSMC.2009.5346251"},{"key":"40_CR4","doi-asserted-by":"crossref","unstructured":"Chen, Y., Tu, L.: Density-based clustering for real-time stream data. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 133\u2013142. ACM (2007)","DOI":"10.1145\/1281192.1281210"},{"issue":"1","key":"40_CR5","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1145\/1327452.1327492","volume":"51","author":"J Dean","year":"2008","unstructured":"Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107\u2013113 (2008)","journal-title":"Commun. ACM"},{"issue":"3","key":"40_CR6","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1023\/A:1009805532638","volume":"3","author":"M Erwig","year":"1999","unstructured":"Erwig, M., Gu, R.H., Schneider, M., Vazirgiannis, M., et al.: Spatio-temporal data types: an approach to modeling and querying moving objects in databases. GeoInformatica 3(3), 269\u2013296 (1999)","journal-title":"GeoInformatica"},{"key":"40_CR7","unstructured":"Ester, M., Kriegel, H.-P., Sander, J., Wimmer, M., Xu, X.: Incremental clustering for mining in a data warehousing environment. In: VLDB, vol. 98, pp. 323\u2013333 (1998)"},{"key":"40_CR8","unstructured":"Ester, M., Kriegel, H.-P., Sander, J., , Xu, X., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, vol. 96, pp. 226\u2013231 (1996)"},{"key":"40_CR9","doi-asserted-by":"crossref","unstructured":"Gong, Y., Morandini, L., Sinnott, R.O.: The design and benchmarking of a cloud-based platform for processing and visualization of traffic data. In: IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 13\u201320. IEEE (2017)","DOI":"10.1109\/BIGCOMP.2017.7881699"},{"key":"40_CR10","unstructured":"Hagedorn, S., G\u00f6tze, P., Sattler, K.-U.: The STARK framework for spatio-temporal data analytics on spark. In: BTW, pp. 123\u2013142 (2017)"},{"issue":"2","key":"40_CR11","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1023\/A:1012801612483","volume":"17","author":"M Halkidi","year":"2001","unstructured":"Halkidi, M., Batistakis, Y., Vazirgiannis, M.: On clustering validation techniques. J. Intell. Inf. Syst. 17(2), 107\u2013145 (2001)","journal-title":"J. Intell. Inf. Syst."},{"issue":"1","key":"40_CR12","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1007\/s11704-013-3158-3","volume":"8","author":"Y He","year":"2014","unstructured":"He, Y., Tan, H., Luo, W., Feng, S., Fan, J.: MR-DBSCAN: a scalable MapReduce-based DBSCAN algorithm for heavily skewed data. Front. Comput. Sci. 8(1), 83\u201399 (2014)","journal-title":"Front. Comput. Sci."},{"key":"40_CR13","unstructured":"Hinneburg, A., Keim, D.A., et al.: An efficient approach to clustering in large multimedia databases with noise. In: KDD, vol. 98, pp. 58\u201365 (1998)"},{"key":"40_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1007\/978-3-540-45160-0_21","volume-title":"Advances in Web-Age Information Management","author":"S Ma","year":"2003","unstructured":"Ma, S., Wang, T.J., Tang, S.W., Yang, D.Q., Gao, J.: A new fast clustering algorithm based on reference and density. In: Dong, G., Tang, C., Wang, W. (eds.) WAIM 2003. LNCS, vol. 2762, pp. 214\u2013225. Springer, Heidelberg (2003). https:\/\/doi.org\/10.1007\/978-3-540-45160-0_21"},{"issue":"2","key":"40_CR15","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1023\/A:1009745219419","volume":"2","author":"J Sander","year":"1998","unstructured":"Sander, J., Ester, M., Kriegel, H.-P., Xiaowei, X.: Density-based clustering in spatial databases: the algorithm gdbscan and its applications. Data Min. Knowl. Disc. 2(2), 169\u2013194 (1998)","journal-title":"Data Min. Knowl. Disc."},{"key":"40_CR16","unstructured":"Sayad, S.: Real Time Data Mining (2017)"},{"key":"40_CR17","doi-asserted-by":"crossref","unstructured":"Spieth, C., Streichert, F., Speer, N., Zell, A.: Clustering-based approach to identify solutions for the inference of regulatory networks. In: The 2005 IEEE Congress on Evolutionary Computation, vol. 1, pp. 660\u2013667. IEEE (2005)","DOI":"10.1145\/1068009.1068084"},{"key":"40_CR18","doi-asserted-by":"crossref","unstructured":"Viswanath, P., Pinkesh, R.: L-DBSCAN: a fast hybrid density based clustering method. In: 18th International Conference on Pattern Recognition, ICPR 2006, vol. 1, pp. 912\u2013915. IEEE (2006)","DOI":"10.1109\/ICPR.2006.741"},{"issue":"1","key":"40_CR19","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1145\/503104.503108","volume":"20","author":"J-R Wen","year":"2002","unstructured":"Wen, J.-R., Nie, J.-Y., Zhang, H.-J.: Query clustering using user logs. ACM Trans. Inf. Syst. 20(1), 59\u201381 (2002)","journal-title":"ACM Trans. Inf. Syst."}],"container-title":["Lecture Notes in Computer Science","Computational Science \u2013 ICCS 2018"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-93698-7_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,11]],"date-time":"2022-06-11T00:07:06Z","timestamp":1654906026000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-93698-7_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783319936970","9783319936987"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-93698-7_40","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"12 June 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Wuxi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 June 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 June 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccs-computsci2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iccs-meeting.org\/iccs2018\/","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":"406","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":"148","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":"60","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":"36% - 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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Acceptance rate in the Main Track is 28%. Acceptance rate in the workshops is 43%.  A high acceptance rate in the workshops is explained by the nature of these thematic sessions, where many experts in a particular field are personally invited by workshop organisers to participate in their sessions.","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)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}