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BIGtensor: Mining Billion-Scale Tensor Made Easy

Authors:

Namyong Park,

Byungsoo Jeon,

Jungwoo Lee,

U KangAuthors Info & Claims

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

Pages 2457 - 2460

https://doi.org/10.1145/2983323.2983332

Published: 24 October 2016 Publication History

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Abstract

Many real-world data are naturally represented as tensors, or multi-dimensional arrays. Tensor decomposition is an important tool to analyze tensors for various applications such as latent concept discovery, trend analysis, clustering, and anomaly detection. However, existing tools for tensor analysis do not scale well for billion-scale tensors or offer limited functionalities. In this paper, we propose BIGtensor, a large-scale tensor mining library that tackles both of the above problems. Carefully designed for scalability, BIGtensor decomposes at least 100× larger tensors than the current state of the art. Furthermore, BIGtensor provides a variety of distributed tensor operations and tensor generation methods. We demonstrate how BIGtensor can help users discover hidden concepts and analyze trends from large-scale tensors that are hard to be processed by existing tensor tools.

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Li ZHu YLi MYang WLi K(2024)cuFastTucker: A Novel Sparse FastTucker Decomposition For HHLST on Multi-GPUsACM Transactions on Parallel Computing10.1145/366145011:2(1-31)Online publication date: 8-Jun-2024
https://dl.acm.org/doi/10.1145/3661450
Ahn DSaini UPapalexakis EPayani ASerra ESpezzano F(2024)Neural Additive Tensor Decomposition for Sparse TensorsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679833(14-23)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679833
Bharadwaj VMalik OMurray RBuluç ADemmel JAgrawal KPetrank E(2024)Distributed-Memory Randomized Algorithms for Sparse Tensor CP DecompositionProceedings of the 36th ACM Symposium on Parallelism in Algorithms and Architectures10.1145/3626183.3659980(155-168)Online publication date: 17-Jun-2024
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Index Terms

BIGtensor: Mining Billion-Scale Tensor Made Easy
1. Information systems
  1. Information systems applications
    1. Data mining

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Information & Contributors

Information

Published In

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

October 2016

2566 pages

ISBN:9781450340731

DOI:10.1145/2983323

General Chairs:
Snehasis Mukhopadhyay
Indiana University Purdue University Indianapolis, USA
,
ChengXiang Zhai
University of Illinois at Urbana-Champaign, USA
,
Program Chairs:
Elisa Bertino
Purdue University
,
Fabio Crestani
University of Lugano
,
Javed Mostafa
University of North Carolina
,
Jie Tang
Tsinghua University
,
Luo Si
Alibaba Group Inc & Purdue University
,
Xiaofang Zhou
University of Queensland
,
Yi Chang
Yahoo Research
,
Yunyao Li
IBM Research - Almaden
,
Parikshit Sondhi
WalmartLabs

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 October 2016

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Funding Sources

MSIP/IITP of Korea
National Research Foundation of Korea

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CIKM'16

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CIKM'16: ACM Conference on Information and Knowledge Management

October 24 - 28, 2016

Indiana, Indianapolis, USA

Acceptance Rates

CIKM '16 Paper Acceptance Rate 160 of 701 submissions, 23%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

Upcoming Conference

CIKM '25

Sponsor:
sigir
sigir

The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

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Cited By

View all

Li ZHu YLi MYang WLi K(2024)cuFastTucker: A Novel Sparse FastTucker Decomposition For HHLST on Multi-GPUsACM Transactions on Parallel Computing10.1145/366145011:2(1-31)Online publication date: 8-Jun-2024
https://dl.acm.org/doi/10.1145/3661450
Ahn DSaini UPapalexakis EPayani ASerra ESpezzano F(2024)Neural Additive Tensor Decomposition for Sparse TensorsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679833(14-23)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679833
Bharadwaj VMalik OMurray RBuluç ADemmel JAgrawal KPetrank E(2024)Distributed-Memory Randomized Algorithms for Sparse Tensor CP DecompositionProceedings of the 36th ACM Symposium on Parallelism in Algorithms and Architectures10.1145/3626183.3659980(155-168)Online publication date: 17-Jun-2024
https://dl.acm.org/doi/10.1145/3626183.3659980
Yan HGe YWang HZhang DYang YFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Logistics Audience Expansion via Temporal Knowledge GraphProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614695(4879-4886)Online publication date: 21-Oct-2023
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Gillet ALeclercq ÉCullot N(2023)Multi-level optimization of the canonical polyadic tensor decomposition at large-scale: Application to the stratification of social networks through deflationInformation Systems10.1016/j.is.2022.102142112(102142)Online publication date: Feb-2023
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Wang BDeng LQu ZLi SZhang ZXie Y(2022)Efficient Processing of Sparse Tensor Decomposition via Unified Abstraction and PE-Interactive ArchitectureIEEE Transactions on Computers10.1109/TC.2020.304661771:2(266-281)Online publication date: 1-Feb-2022
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Singh NZhang ZWu XZhang NZhang SSolomonik E(2022)Distributed-memory tensor completion for generalized loss functions in python using new sparse tensor kernelsJournal of Parallel and Distributed Computing10.1016/j.jpdc.2022.07.005169(269-285)Online publication date: Nov-2022
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Yang KGao YShen YZheng BChen L(2021)DisMASTD: An Efficient Distributed Multi-Aspect Streaming Tensor Decomposition2021 IEEE 37th International Conference on Data Engineering (ICDE)10.1109/ICDE51399.2021.00098(1080-1091)Online publication date: Apr-2021
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Gillet ALeclercq ÉCullot N(2021)MuLOT: Multi-level Optimization of the Canonical Polyadic Tensor Decomposition at Large-ScaleAdvances in Databases and Information Systems10.1007/978-3-030-82472-3_15(198-212)Online publication date: 24-Aug-2021
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Yang HYong H(2020)Multi-Aspect Incremental Tensor Decomposition Based on Distributed In-Memory Big Data SystemsJournal of Data and Information Science10.2478/jdis-2020-00105:2(13-32)Online publication date: 20-May-2020
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