{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T18:49:46Z","timestamp":1783968586777,"version":"3.55.0"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030320461","type":"print"},{"value":"9783030320478","type":"electronic"}],"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"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-32047-8_11","type":"book-chapter","created":{"date-parts":[[2019,9,24]],"date-time":"2019-09-24T05:07:22Z","timestamp":1569301642000},"page":"113-127","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["The Role of Local Intrinsic Dimensionality in Benchmarking Nearest\u00a0Neighbor Search"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7212-6476","authenticated-orcid":false,"given":"Martin","family":"Aum\u00fcller","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2783-0218","authenticated-orcid":false,"given":"Matteo","family":"Ceccarello","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,9,23]]},"reference":[{"key":"11_CR1","doi-asserted-by":"crossref","unstructured":"Alman, J., Williams, R.: Probabilistic polynomials and hamming nearest neighbors. In: FOCS 2015, pp. 136\u2013150 (2015)","DOI":"10.1109\/FOCS.2015.18"},{"key":"11_CR2","doi-asserted-by":"crossref","unstructured":"Amsaleg, L., et al.: Estimating local intrinsic dimensionality. In: KDD 2015, pp. 29\u201338. ACM (2015)","DOI":"10.1145\/2783258.2783405"},{"key":"11_CR3","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1137\/1.9781611975673.21","volume-title":"Proceedings of the 2019 SIAM International Conference on Data Mining","author":"Laurent Amsaleg","year":"2019","unstructured":"Amsaleg, L., Chelly, O., Houle, M.E., Kawarabayashi, K.I., Radovanovi\u0107, M., Treeratanajaru, W.: Intrinsic dimensionality estimation within tight localities. In: Proceedings of the 2019 SIAM International Conference on Data Mining, pp. 181\u2013189. SIAM (2019)"},{"key":"11_CR4","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1007\/978-3-319-68474-1_3","volume-title":"SISAP 2017","author":"M Aum\u00fcller","year":"2017","unstructured":"Aum\u00fcller, M., Bernhardsson, E., Faithfull, A.: ANN-benchmarks: a benchmarking tool for approximate nearest neighbor algorithms. In: Beecks, C., Borutta, F., Kr\u00f6ger, P., Seidl, T. (eds.) SISAP 2017. LNCS, vol. 10609, pp. 34\u201349. Springer, Heidelberg (2017). https:\/\/doi.org\/10.1007\/978-3-319-68474-1_3"},{"key":"11_CR5","unstructured":"Aum\u00fcller, M., Ceccarello, M.: Benchmarking nearest neighbor search: influence of local intrinsic dimensionality and result diversity in real-world datasets. In: 1st Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning (EDML 2019) (2019). https:\/\/imada.sdu.dk\/Research\/EDML\/"},{"key":"11_CR6","unstructured":"Bernhardsson, E.: Annoy. https:\/\/github.com\/spotify\/annoy"},{"issue":"7","key":"11_CR7","first-page":"769","volume":"10","author":"G Casanova","year":"2017","unstructured":"Casanova, G., et al.: Dimensional testing for reverse k-nearest neighbor search. PVLDB 10(7), 769\u2013780 (2017)","journal-title":"PVLDB"},{"issue":"3","key":"11_CR8","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). https:\/\/doi.org\/10.1145\/502807.502808","journal-title":"ACM Comput. Surv."},{"key":"11_CR9","first-page":"801","volume":"14","author":"RR Curtin","year":"2013","unstructured":"Curtin, R.R., et al.: MLPACK: a scalable C++ machine learning library. J. Mach. Learn. Res. 14, 801\u2013805 (2013)","journal-title":"J. Mach. Learn. Res."},{"key":"11_CR10","unstructured":"Edel, M., Soni, A., Curtin, R.R.: An automatic benchmarking system. In: NIPS 2014 Workshop on Software Engineering for Machine Learning (2014)"},{"key":"11_CR11","doi-asserted-by":"crossref","unstructured":"Houle, M.E.: Dimensionality, discriminability, density and distance distributions. In: Data Mining Workshops (ICDMW), pp. 468\u2013473. IEEE (2013)","DOI":"10.1109\/ICDMW.2013.139"},{"key":"11_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1007\/978-3-030-02224-2_14","volume-title":"Similarity Search and Applications","author":"ME Houle","year":"2018","unstructured":"Houle, M.E., Schubert, E., Zimek, A.: On the correlation between local intrinsic dimensionality and outlierness. In: Marchand-Maillet, S., Silva, Y.N., Ch\u00e1vez, E. (eds.) SISAP 2018. LNCS, vol. 11223, pp. 177\u2013191. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-02224-2_14"},{"key":"11_CR13","unstructured":"Iwasaki, M., Miyazaki, D.: Optimization of Indexing Based on k-Nearest Neighbor Graph for Proximity Search in High-dimensional Data. ArXiv e-prints, October 2018"},{"issue":"1","key":"11_CR14","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1109\/TPAMI.2010.57","volume":"33","author":"H J\u00e9gou","year":"2011","unstructured":"J\u00e9gou, H., Douze, M., Schmid, C.: Product quantization for nearest neighbor search. IEEE Trans. Pattern Anal. Mach. Intell. 33(1), 117\u2013128 (2011). https:\/\/doi.org\/10.1109\/TPAMI.2010.57","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"11_CR15","unstructured":"Johnson, J., Douze, M., J\u00e9gou, H.: Billion-scale similarity search with GPUs. CoRR abs\/1702.08734 (2017)"},{"issue":"2","key":"11_CR16","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1007\/BF02764938","volume":"54","author":"WB Johnson","year":"1986","unstructured":"Johnson, W.B., Lindenstrauss, J., Schechtman, G.: Extensions of Lipschitz maps into Banach spaces. Israel J. Math. 54(2), 129\u2013138 (1986)","journal-title":"Israel J. Math."},{"key":"11_CR17","volume-title":"Principal Component Analysis","author":"I Jolliffe","year":"2011","unstructured":"Jolliffe, I.: Principal Component Analysis. Springer, Berlin (2011)"},{"issue":"2","key":"11_CR18","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1007\/s10115-016-1004-2","volume":"52","author":"H Kriegel","year":"2017","unstructured":"Kriegel, H., Schubert, E., Zimek, A.: The (black) art of runtime evaluation: are we comparing algorithms or implementations? Knowl. Inf. Syst. 52(2), 341\u2013378 (2017)","journal-title":"Knowl. Inf. Syst."},{"key":"11_CR19","unstructured":"Levina, E., Bickel, P.J.: Maximum likelihood estimation of intrinsic dimension. In: NIPS, pp. 777\u2013784 (2005)"},{"key":"11_CR20","unstructured":"Li, W., Zhang, Y., Sun, Y., Wang, W., Zhang, W., Lin, X.: Approximate nearest neighbor search on high dimensional data - experiments, analyses, and improvement (v1.0). CoRR abs\/1610.02455 (2016)"},{"key":"11_CR21","unstructured":"Malkov, Y.A., Yashunin, D.A.: Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs. ArXiv e-prints, March 2016"},{"key":"11_CR22","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. CoRR abs\/1301.3781 (2013)"},{"key":"11_CR23","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Empirical Methods in Natural Language Processing (EMNLP), pp. 1532\u20131543 (2014)","DOI":"10.3115\/v1\/D14-1162"},{"key":"11_CR24","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.cor.2013.11.015","volume":"45","author":"K Smith-Miles","year":"2014","unstructured":"Smith-Miles, K., Baatar, D., Wreford, B., Lewis, R.: Towards objective measures of algorithm performance across instance space. Comput. Oper. Res. 45, 12\u201324 (2014)","journal-title":"Comput. Oper. Res."},{"key":"11_CR25","doi-asserted-by":"publisher","unstructured":"Spring, R., Shrivastava, A.: Scalable and sustainable deep learning via randomized hashing. In: KDD 2017, pp. 445\u2013454 (2017). https:\/\/doi.org\/10.1145\/3097983.3098035","DOI":"10.1145\/3097983.3098035"},{"key":"11_CR26","unstructured":"Xiao, H., Rasul, K., Vollgraf, R.: Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. CoRR abs\/1708.07747 (2017)"}],"container-title":["Lecture Notes in Computer Science","Similarity Search and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-32047-8_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T10:45:31Z","timestamp":1710326731000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-32047-8_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030320461","9783030320478"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-32047-8_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"23 September 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SISAP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Similarity Search and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Newark, NJ","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","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":"2 October 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"sisap2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.sisap.org\/2019\/","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":"42","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":"12","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":"18","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":"2.88","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":"1-92","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)"}}]}}