Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/96209
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Computing | - |
dc.creator | Tang, B | en_US |
dc.creator | Yiu, ML | en_US |
dc.creator | Hua, KA | en_US |
dc.date.accessioned | 2022-11-14T04:06:54Z | - |
dc.date.available | 2022-11-14T04:06:54Z | - |
dc.identifier.issn | 1041-4347 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/96209 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.rights | The following publication B. Tang, M. L. Yiu and K. A. Hua, "Exploit Every Bit: Effective Caching for High-Dimensional Nearest Neighbor Search," in IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 5, pp. 1175-1188, 1 May 2016 is available at https://doi.org/10.1109/TKDE.2016.2515603. | en_US |
dc.subject | Caching | en_US |
dc.subject | High dimensional data | en_US |
dc.subject | Histogram | en_US |
dc.subject | Similarity search | en_US |
dc.title | Exploit every bit : effective caching for high-dimensional nearest neighbor search | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 1175 | en_US |
dc.identifier.epage | 1188 | en_US |
dc.identifier.volume | 28 | en_US |
dc.identifier.issue | 5 | en_US |
dc.identifier.doi | 10.1109/TKDE.2016.2515603 | en_US |
dcterms.abstract | High-dimensional k nearest neighbor (kNN) search has a wide range of applications in multimedia information retrieval. Existing disk-based k NN search methods incur significant I/O costs in the candidate refinement phase. In this paper, we propose to cache compact approximate representations of data points in main memory in order to reduce the candidate refinement time during k NN search. This problem raises two challenging issues: (i) which is the most effective encoding scheme for data points to support k NN search? and (ii) what is the optimal number of bits for encoding a data point? For (i), we formulate and solve a novel histogram optimization problem that decides the most effective encoding scheme. For (ii), we develop a cost model for automatically tuning the optimal number of bits for encoding points. In addition, our approach is generic and applicable to exact/approximate k NN search methods. Extensive experimental results on real datasets demonstrate that our proposal can accelerate the candidate refinement time of k NN search by at least an order of magnitude. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE transactions on knowledge and data engineering, May 2016, v. 28, no. 5, 7374714, p. 1175-1188 | en_US |
dcterms.isPartOf | IEEE transactions on knowledge and data engineering | en_US |
dcterms.issued | 2016-05 | - |
dc.identifier.scopus | 2-s2.0-84963792027 | - |
dc.identifier.eissn | 1558-2191 | en_US |
dc.identifier.artn | 7374714 | en_US |
dc.description.validate | 202211 bcww | - |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | RGC-B3-0791 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Exploit_Every_Bit.pdf | Pre-Published version | 1.21 MB | Adobe PDF | View/Open |
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