{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T07:52:37Z","timestamp":1726041157328},"publisher-location":"Cham","reference-count":16,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030276140"},{"type":"electronic","value":"9783030276157"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-27615-7_8","type":"book-chapter","created":{"date-parts":[[2019,8,18]],"date-time":"2019-08-18T19:02:41Z","timestamp":1566154961000},"page":"106-121","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["HGraph: A Connected-Partition Approach to Proximity Graphs for Similarity Search"],"prefix":"10.1007","author":[{"given":"Larissa Capobianco","family":"Shimomura","sequence":"first","affiliation":[]},{"given":"Daniel S.","family":"Kaster","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,8,3]]},"reference":[{"issue":"C","key":"8_CR1","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1016\/j.is.2015.01.010","volume":"52","author":"G Amato","year":"2015","unstructured":"Amato, G., Esuli, A., Falchi, F.: A comparison of pivot selection techniques for permutation-based indexing. Inf. Syst. 52(C), 176\u2013188 (2015)","journal-title":"Inf. Syst."},{"key":"8_CR2","unstructured":"Barioni, M.C.N., Kaster, D.D.S., Razente, H.L., Traina, A.J., J\u00fanior, C.T.: Advanced Database Query Systems. IGI Global (2011)"},{"key":"8_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1007\/978-3-642-41062-8_28","volume-title":"Similarity Search and Applications","author":"L Boytsov","year":"2013","unstructured":"Boytsov, L., Naidan, B.: Engineering efficient and effective non-metric space library. In: Brisaboa, N., Pedreira, O., Zezula, P. (eds.) SISAP 2013. LNCS, vol. 8199, pp. 280\u2013293. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-41062-8_28"},{"key":"8_CR4","unstructured":"Bustos, B., Navarro, G., Chavez, E.: Pivot selection techniques for proximity searching in metric spaces. In: SCCC, pp. 33\u201340, November 2001"},{"issue":"3","key":"8_CR5","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1145\/502807.502808","volume":"33","author":"E Ch\u00e1vez","year":"2001","unstructured":"Ch\u00e1vez, E., Navarro, G., Baeza-Yates, R., Marroqu\u00edn, J.L.: Searching in metric spaces. ACM Comput. Surv. 33(3), 273\u2013321 (2001)","journal-title":"ACM Comput. Surv."},{"key":"8_CR6","first-page":"1989","volume":"10","author":"J Chen","year":"2009","unstructured":"Chen, J., Fangand, H.R., Saad, Y.: Fast approximate KNN graph construction for high dimensional data via recursive Lanczos bisection. J. Mach. Learn. Res. 10, 1989\u20132012 (2009)","journal-title":"J. Mach. Learn. Res."},{"key":"8_CR7","unstructured":"Hajebi, K., Abbasi-Yadkori, Y., Shahbazi, H., Zhang, H.: Fast approximate nearest-neighbor search with k-nearest neighbor graph. In: IJCAI, pp. 1312\u20131317 (2011)"},{"issue":"11","key":"8_CR8","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"key":"8_CR9","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.is.2013.10.006","volume":"45","author":"Y Malkov","year":"2014","unstructured":"Malkov, Y., et al.: Approximate nearest neighbor algorithm based on navigable small world graphs. Inf. Syst. 45, 61\u201368 (2014)","journal-title":"Inf. Syst."},{"key":"8_CR10","unstructured":"Ocsa, A., Bedregal, C., Cuadros-Vargas, E.: A new approach for similarity queries using neighborhood graphs. In: Brazilian Symposium on Databases, pp. 131\u2013142 (2007)"},{"issue":"6","key":"8_CR11","first-page":"905","volume":"10","author":"M Ortega","year":"1998","unstructured":"Ortega, M., Rui, Y., Chakrabarti, K., Porkaew, K., Mehrotra, S., Huang, T.S.: Supporting ranked boolean similarity queries in MARS. TKDE 10(6), 905\u2013925 (1998)","journal-title":"TKDE"},{"key":"8_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1007\/11575832_14","volume-title":"String Processing and Information Retrieval","author":"R Paredes","year":"2005","unstructured":"Paredes, R., Ch\u00e1vez, E.: Using the k-nearest neighbor graph for proximity searching in metric spaces. In: Consens, M., Navarro, G. (eds.) SPIRE 2005. LNCS, vol. 3772, pp. 127\u2013138. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/11575832_14"},{"issue":"2, Part 2","key":"8_CR13","doi-asserted-by":"publisher","first-page":"3336","DOI":"10.1016\/j.eswa.2008.01.039","volume":"36","author":"HS Park","year":"2009","unstructured":"Park, H.S., Jun, C.H.: A simple and fast algorithm for k-medoids clustering. Expert Syst. Appl. 36(2, Part 2), 3336\u20133341 (2009)","journal-title":"Expert Syst. Appl."},{"key":"8_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1007\/978-3-030-02224-2_2","volume-title":"Similarity Search and Applications","author":"LC Shimomura","year":"2018","unstructured":"Shimomura, L.C., Vieira, M.R., Kaster, D.S.: Performance analysis of graph-based methods for exact and approximate similarity search in metric spaces. In: Marchand-Maillet, S., Silva, Y.N., Ch\u00e1vez, E. (eds.) SISAP 2018. LNCS, vol. 11223, pp. 18\u201332. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-02224-2_2"},{"issue":"4","key":"8_CR15","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1007\/s00778-005-0178-0","volume":"16","author":"C Traina Jr","year":"2007","unstructured":"Traina Jr., C., Filho, R.F., Traina, A.J., Vieira, M.R., Faloutsos, C.: The Omni-family of all-purpose access methods: a simple and effective way to make similarity search more efficient. VLDB J. 16(4), 483\u2013505 (2007)","journal-title":"VLDB J."},{"issue":"4","key":"8_CR16","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1016\/0020-0190(91)90074-R","volume":"40","author":"JK Uhlmann","year":"1991","unstructured":"Uhlmann, J.K.: Satisfying general proximity\/similarity queries with metric trees. Inf. Process. Lett. 40(4), 175\u2013179 (1991)","journal-title":"Inf. Process. Lett."}],"container-title":["Lecture Notes in Computer Science","Database and Expert Systems Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-27615-7_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T09:13:28Z","timestamp":1710234808000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-27615-7_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030276140","9783030276157"],"references-count":16,"URL":"http:\/\/dx.doi.org\/10.1007\/978-3-030-27615-7_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"3 August 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DEXA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database and Expert Systems Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Linz","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Austria","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 August 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 August 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dexa2019a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.dexa.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mixed (Single-blind and Double-blind)","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"ConfDriver","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"157","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":"32","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":"34","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":"20% - 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":"4-6","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":"3-4","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)"}}]}}
