Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/105560
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Computing | - |
dc.creator | Xie, K | - |
dc.creator | Wang, X | - |
dc.creator | Wang, X | - |
dc.creator | Chen, Y | - |
dc.creator | Xie, G | - |
dc.creator | Ouyang, Y | - |
dc.creator | Wen, J | - |
dc.creator | Cao, J | - |
dc.creator | Zhang, D | - |
dc.date.accessioned | 2024-04-15T07:35:02Z | - |
dc.date.available | 2024-04-15T07:35:02Z | - |
dc.identifier.issn | 1063-6692 | - |
dc.identifier.uri | http://hdl.handle.net/10397/105560 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | ©2019 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 K. Xie et al., "Accurate Recovery of Missing Network Measurement Data With Localized Tensor Completion," in IEEE/ACM Transactions on Networking, vol. 27, no. 6, pp. 2222-2235, Dec. 2019 is available at https://doi.org/10.1109/TNET.2019.2940147. | en_US |
dc.subject | Network measurement | en_US |
dc.subject | Tensor completion | en_US |
dc.title | Accurate recovery of missing network measurement data with localized tensor completion | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 2222 | - |
dc.identifier.epage | 2235 | - |
dc.identifier.volume | 27 | - |
dc.identifier.issue | 6 | - |
dc.identifier.doi | 10.1109/TNET.2019.2940147 | - |
dcterms.abstract | The inference of the network traffic data from partial measurements data becomes increasingly critical for various network engineering tasks. By exploiting the multi-dimensional data structure, tensor completion is a promising technique for more accurate missing data inference. However, existing tensor completion algorithms generally have the strong assumption that the tensor data have a global low-rank structure, and try to find a single and global model to fit the data of the whole tensor. In a practical network system, a subset of data may have stronger correlation. In this work, we propose a novel localized tensor completion model (LTC) to increase the data recovery accuracy by taking advantage of the stronger local correlation of data to form and recover sub-tensors each with a lower rank. Despite that it is promising to use local tensors, the finding of correlated entries faces two challenges, the data with adjacent indexes are not ones with higher correlation and it is difficult to find the similarity of data with missing tensor entries. To conquer the challenges, we propose several novel techniques: efficiently calculating the candidate anchor points based on locality-sensitive hash (LSH), building sub-tensors around properly selected anchor points, encoding factor matrices to facilitate the finding of similarity with missing entries, and similarity-aware local tensor completion and data fusion. We have done extensive experiments using real traffic traces. Our results demonstrate that LTC is very effective in increasing the tensor recovery accuracy without depending on specific tensor completion algorithms. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE/ACM transactions on networking, Dec. 2019, v. 27, no. 6, p. 2222-2235 | - |
dcterms.isPartOf | IEEE/ACM transactions on networking | - |
dcterms.issued | 2019-12 | - |
dc.identifier.scopus | 2-s2.0-85077325134 | - |
dc.identifier.eissn | 1558-2566 | - |
dc.description.validate | 202402 bcch | - |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | COMP-0470 | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Natural Science Foundation of China; Hunan Provincial Natural Science Foundation of China; U.S. NSF; open project funding of State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, the Peng Cheng Laboratory Project of Guangdong Province | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 20511780 | en_US |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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Cao_Accurate_Recovery_Missing.pdf | Pre-Published version | 4.3 MB | Adobe PDF | View/Open |
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