Article

Mining large graphs and streams using matrix and tensor tools

Authors:

Christos Faloutsos,

Tamara G. Kolda,

Jimeng SunAuthors Info & Claims

SIGMOD '07: Proceedings of the 2007 ACM SIGMOD international conference on Management of data

Page 1174

https://doi.org/10.1145/1247480.1247647

Published: 11 June 2007 Publication History

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Abstract

Coevolving streams of numerical measurements, as well astime evolving graphs, can well be represented as tensors. Here we review the fundamental matrix and tensors tools forthe analysis and mining of large scale streams and graphs.

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Xie KLiu CWang XLi XXie GWen JLi K(2025)Neural Network Compression Based on Tensor Ring DecompositionIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2024.338339236:3(5388-5402)Online publication date: Mar-2025
https://doi.org/10.1109/TNNLS.2024.3383392
Wang CXie KTian JWen JLi XXie GLi K(2024)HPETC: History Priority Enhanced Tensor Completion for Network Distance MeasurementIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2023.327430535:6(1012-1028)Online publication date: Jun-2024
https://doi.org/10.1109/TPDS.2023.3274305
Plantevit MRobardet CScuturici V(2016)Graph dependency construction based on interval-event dependencies detection in data streamsIntelligent Data Analysis10.3233/IDA-16080320:2(223-256)Online publication date: 1-Mar-2016
https://doi.org/10.3233/IDA-160803
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Mining large graphs and streams using matrix and tensor tools
1. Information systems
  1. Data management systems

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Published In

SIGMOD '07: Proceedings of the 2007 ACM SIGMOD international conference on Management of data

June 2007

1210 pages

ISBN:9781595936868

DOI:10.1145/1247480

General Chairs:
Lizhu Zhou
Tsinghua University, China
,
Tok Wang Ling
National University of Singapore, Singapore
,
Program Chair:
Beng Chin Ooi
National University of Singapore, Singapore

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 11 June 2007

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SIGMOD/PODS07

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SIGMOD/PODS07: International Conference on Management of Data

June 11 - 14, 2007

Beijing, China

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Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Xie KLiu CWang XLi XXie GWen JLi K(2025)Neural Network Compression Based on Tensor Ring DecompositionIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2024.338339236:3(5388-5402)Online publication date: Mar-2025
https://doi.org/10.1109/TNNLS.2024.3383392
Wang CXie KTian JWen JLi XXie GLi K(2024)HPETC: History Priority Enhanced Tensor Completion for Network Distance MeasurementIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2023.327430535:6(1012-1028)Online publication date: Jun-2024
https://doi.org/10.1109/TPDS.2023.3274305
Plantevit MRobardet CScuturici V(2016)Graph dependency construction based on interval-event dependencies detection in data streamsIntelligent Data Analysis10.3233/IDA-16080320:2(223-256)Online publication date: 1-Mar-2016
https://doi.org/10.3233/IDA-160803
Jin LFeng ZFeng LMukhopadhyay SZhai CBertino ECrestani FMostafa JTang JSi LZhou XChang YLi YSondhi P(2016)A Context-aware Collaborative Filtering Approach for Urban Black Holes DetectionProceedings of the 25th ACM International on Conference on Information and Knowledge Management10.1145/2983323.2983655(2137-2142)Online publication date: 24-Oct-2016
https://dl.acm.org/doi/10.1145/2983323.2983655
Ball BFlood MJagadish HLangsam JRaschid LWiriyathammabhum PKrishnamurthy RRaschid LVaithyanathan S(2014)A Flexible and Extensible Contract Aggregation Framework (CAF) for Financial Data Stream AnalyticsProceedings of the International Workshop on Data Science for Macro-Modeling10.1145/2630729.2630737(1-6)Online publication date: 22-Jun-2014
https://dl.acm.org/doi/10.1145/2630729.2630737
Zheng YCapra LWolfson OYang H(2014)Urban ComputingACM Transactions on Intelligent Systems and Technology10.1145/26295925:3(1-55)Online publication date: 18-Sep-2014
https://dl.acm.org/doi/10.1145/2629592
Adarkwa OAttoh-Okine N(2013)Pavement crack classification based on tensor factorizationConstruction and Building Materials10.1016/j.conbuildmat.2013.07.09148(853-857)Online publication date: Nov-2013
https://doi.org/10.1016/j.conbuildmat.2013.07.091
Lu H(2013)Learning Modewise Independent Components from Tensor Data Using Multilinear Mixing ModelAdvanced Information Systems Engineering10.1007/978-3-642-40991-2_19(288-303)Online publication date: 2013
https://doi.org/10.1007/978-3-642-40991-2_19
Qiu RWicks MQiu RWicks M(2013)Database Friendly Data ProcessingCognitive Networked Sensing and Big Data10.1007/978-1-4614-4544-9_12(559-566)Online publication date: 27-Jun-2013
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Li WLiu CZhang TLi HWaterman MZhou X(2011)Integrative Analysis of Many Weighted Co-Expression Networks Using Tensor ComputationPLoS Computational Biology10.1371/journal.pcbi.10011067:6(e1001106)Online publication date: 16-Jun-2011
https://doi.org/10.1371/journal.pcbi.1001106
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Abstract

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Research issues in mining multiple data streams

Approximate mining of maximal frequent itemsets in data streams with different window models

A novel hash-based approach for mining frequent itemsets over data streams requiring less memory space

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