{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T13:55:03Z","timestamp":1742997303285,"version":"3.40.3"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030954079"},{"type":"electronic","value":"9783030954086"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-030-95408-6_22","type":"book-chapter","created":{"date-parts":[[2022,1,31]],"date-time":"2022-01-31T00:04:07Z","timestamp":1643587447000},"page":"288-300","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Automatic Quality Improvement of Data on the Evolution of 2D Regions"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2306-7585","authenticated-orcid":false,"given":"Rog\u00e9rio Lu\u00eds","family":"de C. Costa","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4633-6944","authenticated-orcid":false,"given":"Jos\u00e9","family":"Moreira","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,31]]},"reference":[{"key":"22_CR1","doi-asserted-by":"crossref","unstructured":"Adam, N.R., Janeja, V.P., Atluri, V.: Neighborhood based detection of anomalies in high dimensional spatio-temporal sensor datasets. In: Proceedings of the 2004 ACM Symposium on Applied Computing, pp. 576\u2013583. SAC 04 (2004)","DOI":"10.1145\/967900.968020"},{"key":"22_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-319-47578-3_1","volume-title":"Outlier Analysis","author":"CC Aggarwal","year":"2017","unstructured":"Aggarwal, C.C.: An introduction to outlier analysis. In: Outlier Analysis, pp. 1\u201334. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-47578-3_1"},{"key":"22_CR3","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1016\/j.ins.2013.12.009","volume":"285","author":"A Appice","year":"2014","unstructured":"Appice, A., Guccione, P., Malerba, D., Ciampi, A.: Dealing with temporal and spatial correlations to classify outliers in geophysical data streams. Inf. Sci. 285, 162\u2013180 (2014)","journal-title":"Inf. Sci."},{"key":"22_CR4","doi-asserted-by":"crossref","unstructured":"Birant, D., Kut, A.: Spatio-temporal outlier detection in large databases. In: 28th International Conference on Information Technology Interfaces, pp. 179\u2013184 (2006)","DOI":"10.1109\/ITI.2006.1708474"},{"issue":"2","key":"22_CR5","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1111\/j.1467-9671.2006.00256.x","volume":"10","author":"T Cheng","year":"2006","unstructured":"Cheng, T., Li, Z.: A multiscale approach for spatio-temporal outlier detection. Trans. GIS 10(2), 253\u2013263 (2006)","journal-title":"Trans. GIS"},{"issue":"1","key":"22_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3428155","volume":"13","author":"RLC Costa","year":"2021","unstructured":"Costa, R.L.C., Miranda, E., Dias, P., Moreira, J.: Experience: quality assessment and improvement on a forest fire dataset. J. Data Inf. Q. 13(1), 1\u201313 (2021). https:\/\/doi.org\/10.1145\/3428155","journal-title":"J. Data Inf. Q."},{"issue":"3","key":"22_CR7","first-page":"1","volume":"7","author":"C D\u2019Urso","year":"2016","unstructured":"D\u2019Urso, C.: Experience: glitches in databases, how to ensure data quality by outlier detection techniques. J. Data Inf. Q. 7(3), 1\u201322 (2016)","journal-title":"J. Data Inf. Q."},{"key":"22_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.datak.2017.12.001","volume":"122","author":"MB Duggimpudi","year":"2019","unstructured":"Duggimpudi, M.B., Abbady, S., Chen, J., Raghavan, V.V.: Spatio-temporal outlier detection algorithms based on computing behavioral outlierness factor. Data Knowl. Eng. 122, 1\u201324 (2019)","journal-title":"Data Knowl. Eng."},{"key":"22_CR9","unstructured":"Facebook: Prophet: forecasting at scale (2017). https:\/\/research.fb.com\/blog\/2017\/02\/prophet-forecasting-at-scale\/"},{"issue":"9","key":"22_CR10","doi-asserted-by":"publisher","first-page":"2250","DOI":"10.1109\/TKDE.2013.184","volume":"26","author":"M Gupta","year":"2014","unstructured":"Gupta, M., Gao, J., Aggarwal, C.C., Han, J.: Outlier detection for temporal data: a survey. IEEE Trans. Knowl. Data Eng. 26(9), 2250\u20132267 (2014)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"1","key":"22_CR11","doi-asserted-by":"publisher","first-page":"949","DOI":"10.1007\/s10586-017-1117-8","volume":"22","author":"D Kwon","year":"2017","unstructured":"Kwon, D., Kim, H., Kim, J., Suh, S.C., Kim, I., Kim, K.J.: A survey of deep learning-based network anomaly detection. Cluster Comput. 22(1), 949\u2013961 (2017). https:\/\/doi.org\/10.1007\/s10586-017-1117-8","journal-title":"Cluster Comput."},{"key":"22_CR12","doi-asserted-by":"publisher","unstructured":"Miranda., E., Costa., R.L.C., Dias., P., Moreira., J.: Matching-aware shape simplification. In: Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - GRAPP, pp. 279\u2013286. INSTICC, SciTePress (2020). https:\/\/doi.org\/10.5220\/0008969402790286","DOI":"10.5220\/0008969402790286"},{"key":"22_CR13","unstructured":"MoST-Team: Most forest fire dataset (2020). http:\/\/most.web.ua.pt\/Video15minImagesWKT.zip"},{"issue":"1","key":"22_CR14","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1179\/caj.1993.30.1.46","volume":"30","author":"M Visvalingam","year":"1993","unstructured":"Visvalingam, M., Whyatt, J.D.: Line generalisation by repeated elimination of points. The cartographic J. 30(1), 46\u201351 (1993)","journal-title":"The cartographic J."},{"key":"22_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/978-3-642-12519-5_7","volume-title":"Knowledge Discovery from Sensor Data","author":"E Wu","year":"2010","unstructured":"Wu, E., Liu, W., Chawla, S.: Spatio-temporal outlier detection in precipitation data. In: Gaber, M.M., Vatsavai, R.R., Omitaomu, O.A., Gama, J., Chawla, N.V., Ganguly, A.R. (eds.) Sensor-KDD 2008. LNCS, vol. 5840, pp. 115\u2013133. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-12519-5_7"},{"key":"22_CR16","doi-asserted-by":"crossref","unstructured":"Yi, B., Sidiropoulos, N.D., Johnson, T., Jagadish, H.V., Faloutsos, C., Biliris, A.: Online data mining for co-evolving time sequences. In: Proceedings of 16th International Conference on Data Engineering, pp. 13\u201322 (2000)","DOI":"10.21236\/ADA371154"}],"container-title":["Lecture Notes in Computer Science","Advanced Data Mining and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-95408-6_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,31]],"date-time":"2022-01-31T00:08:44Z","timestamp":1643587724000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-95408-6_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030954079","9783030954086"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-95408-6_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"31 January 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Data Mining and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sydney, NSW","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","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":"2 February 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 February 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adma2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/adma2021.net\/","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":"CMT3","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"116","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":"26","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":"35","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":"22% - 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":"5","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)"}}]}}