skip to main content
10.1145/3394486.3403260acmconferencesArticle/Chapter ViewAbstractPublication PageskddConference Proceedingsconference-collections
research-article

Non-Linear Mining of Social Activities in Tensor Streams

Published: 20 August 2020 Publication History

Abstract

Given a large time-evolving event series such as Google web-search logs, which are collected according to various aspects, i.e., timestamps, locations and keywords, how accurately can we forecast their future activities? How can we reveal significant patterns that allow us to long-term forecast from such complex tensor streams? In this paper, we propose a streaming method, namely, CubeCast, that is designed to capture basic trends and seasonality in tensor streams and extract temporal and multi-dimensional relationships between such dynamics. Our proposed method has the following properties: (a) it is effective: it finds both trends and seasonality and summarizes their dynamics into simultaneous non-linear latent space. (b) it is automatic: it automatically recognizes and models such structural patterns without any parameter tuning or prior information. (c) it is scalable: it incrementally and adaptively detects shifting points of patterns for a semi-infinite collection of tensor streams. Extensive experiments that we conducted on real datasets demonstrate that our algorithm can effectively and efficiently find meaningful patterns for generating future values, and outperforms the state-of-the-art algorithms for time series forecasting in terms of forecasting accuracy and computational time.

References

[1]
2018. Recurrent Neural Networks for Multivariate Time Series with Missing Values. Scientific Reports 8, 1 (2018), 6085.
[2]
Roel Bertens, Jilles Vreeken, and Arno Siebes. 2016. Keeping it short and simple: Summarising complex event sequences with multivariate patterns. In KDD. 735--744.
[3]
Yongjie Cai, Hanghang Tong, Wei Fan, Ping Ji, and Qing He. 2015. Facets: Fast Comprehensive Mining of Coevolving High-order Time Series. In KDD. 79--88.
[4]
Deepayan Chakrabarti, Spiros Papadimitriou, Dharmendra S. Modha, and Christos Faloutsos. 2004. Fully automatic cross-associations. In KDD. 79--88.
[5]
James Durbin and Siem Jan Koopman. 2012. Time Series Analysis by State Space Methods 2 ed.). Oxford University Press.
[6]
Jayavardhana Gubbi, Rajkumar Buyya, Slaven Marusic, and Marimuthu Palaniswami. 2013. Internet of Things (IoT): A Vision, Architectural Elements, and Future Directions. Future Gener. Comput. Syst., Vol. 29, 7 (2013), 1645--1660.
[7]
David Hallac, Sagar Vare, Stephen Boyd, and Jure Leskovec. 2017. Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data. In KDD. 215--223.
[8]
Geoffrey Hinton, Li Deng, Dong Yu, George E Dahl, Abdel rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara N Sainath, et al. 2012. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Processing Magazine, Vol. 29, 6 (2012), 82--97.
[9]
Bryan Hooi, Shenghua Liu, Asim Smailagic, and Christos Faloutsos. 2017. BeatLex: Summarizing and Forecasting Time Series with Patterns. In ECML PKDD. Springer, 3--19.
[10]
Emre Kiciman and Matthew Richardson. 2015. Towards Decision Support and Goal Achievement: Identifying Action-Outcome Relationships From Social Media. In KDD. 547--556.
[11]
Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. CoRR, Vol. abs/1412.6980 (2015).
[12]
Tamara G. Kolda and Brett W. Bader. 2009. Tensor Decompositions and Applications. SIAM Rev., Vol. 51, 3 (September 2009), 455--500.
[13]
Jae-Gil Lee, Jiawei Han, and Kyu-Young Whang. 2007. Trajectory clustering: a partition-and-group framework. In SIGMOD. 593--604.
[14]
Lei Li, James McCann, Nancy S. Pollard, and Christos Faloutsos. 2009. DynaMMo: mining and summarization of coevolving sequences with missing values. In KDD. 507--516.
[15]
Yasuko Matsubara and Yasushi Sakurai. 2016. Regime Shifts in Streams: Real-time Forecasting of Co-evolving Time Sequences. In KDD. 1045--1054.
[16]
Yasuko Matsubara and Yasushi Sakurai. 2019. Dynamic Modeling and Forecasting of Time-Evolving Data Streams. In KDD. 458--468.
[17]
Yasuko Matsubara, Yasushi Sakurai, and Christos Faloutsos. 2014. AutoPlait: Automatic Mining of Co-evolving Time Sequences. In SIGMOD.
[18]
Yasuko Matsubara, Yasushi Sakurai, and Christos Faloutsos. 2016. Non-Linear Mining of Competing Local Activities. In WWW.
[19]
Yasuko Matsubara, Yasushi Sakurai, Christos Faloutsos, Tomoharu Iwata, and Masatoshi Yoshikawa. 2012. Fast mining and forecasting of complex time-stamped events. In KDD. 271--279.
[20]
Gianmarco De Francisci Morales, Albert Bifet, Latifur Khan, Joao Gama, and Wei Fan. 2016. IoT Big Data Stream Mining. In KDD, Tutorial. 2119--2120.
[21]
Jorge J. Moré. 1978. The Levenberg-Marquardt algorithm: Implementation and theory. In Numerical Analysis. 105--116.
[22]
Yao Qin, Dongjin Song, Haifeng Cheng, Wei Cheng, Guofei Jiang, and Garrison W. Cottrell. 2017. A Dual-stage Attention-based Recurrent Neural Network for Time Series Prediction. In IJCAI. AAAI Press, 2627--2633.
[23]
Jorma Rissanen. 1978. Modeling by shortest data description. Automatia, Vol. 14 (1978), 465--471.
[24]
Mark Rogers, Lei Li, and Stuart J Russell. 2013. Multilinear Dynamical Systems for Tensor Time Series. In NIPS. 2634--2642.
[25]
Yasushi Sakurai, Yasuko Matsubara, and Christos Faloutsos. 2015. Mining and Forecasting of Big Time-series Data. In SIGMOD, Tutorial. 919--922.
[26]
Yasushi Sakurai, Yasuko Matsubara, and Christos Faloutsos. 2016. Mining Big Time-series Data on the Web. In WWW, Tutorial. 1029--1032.
[27]
Hyun Ah Song, Bryan Hooi, Marko Jereminov, Amritanshu Pandey, Lawrence T. Pileggi, and Christos Faloutsos. 2017a. PowerCast: Mining and Forecasting Power Grid Sequences. In ECML/PKDD.
[28]
Qingquan Song, Xiao Huang, Hancheng Ge, James Caverlee, and Xia Hu. 2017b. Multi-Aspect Streaming Tensor Completion. In KDD. 435--443.
[29]
Jimeng Sun, Dacheng Tao, and Christos Faloutsos. 2006. Beyond Streams and Graphs: Dynamic Tensor Analysis. In KDD. 374--383.
[30]
Tsubasa Takahashi, Bryan Hooi, and Christos Faloutsos. 2017. AutoCyclone: Automatic Mining of Cyclic Online Activities with Robust Tensor Factorization. In WWW. 213--221.
[31]
Nikolaj Tatti and Jilles Vreeken. 2012. The long and the short of it: summarising event sequences with serial episodes. In KDD. 462--470.
[32]
Peng Wang, Haixun Wang, and Wei Wang. 2011. Finding semantics in time series. In SIGMOD. 385--396.
[33]
Junchen Ye, Leilei Sun, Bowen Du, Yanjie Fu, Xinran Tong, and Hui Xiong. 2019. Co-Prediction of Multiple Transportation Demands Based on Deep Spatio-Temporal Neural Network. In SIGKDD. 305--313.
[34]
Shuo Zhou, Nguyen Xuan Vinh, James Bailey, Yunzhe Jia, and Ian Davidson. 2016. Accelerating Online CP Decompositions for Higher Order Tensors. In KDD. 1375--1384.

Cited By

View all
  • (2024)Gaussian Graphical Model-Based Clustering of Time Series DataProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635728(1148-1149)Online publication date: 4-Mar-2024
  • (2023)Modeling Dynamic Interactions over Tensor StreamsProceedings of the ACM Web Conference 202310.1145/3543507.3583458(1793-1803)Online publication date: 30-Apr-2023
  • (2023)Fast and Multi-aspect Mining of Complex Time-stamped Event StreamsProceedings of the ACM Web Conference 202310.1145/3543507.3583370(1638-1649)Online publication date: 30-Apr-2023

Index Terms

  1. Non-Linear Mining of Social Activities in Tensor Streams

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
    August 2020
    3664 pages
    ISBN:9781450379984
    DOI:10.1145/3394486
    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]

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 20 August 2020

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. automatic mining
    2. tensor analysis
    3. time series

    Qualifiers

    • Research-article

    Conference

    KDD '20
    Sponsor:

    Acceptance Rates

    Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

    Upcoming Conference

    KDD '25

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)26
    • Downloads (Last 6 weeks)7
    Reflects downloads up to 14 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)Gaussian Graphical Model-Based Clustering of Time Series DataProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635728(1148-1149)Online publication date: 4-Mar-2024
    • (2023)Modeling Dynamic Interactions over Tensor StreamsProceedings of the ACM Web Conference 202310.1145/3543507.3583458(1793-1803)Online publication date: 30-Apr-2023
    • (2023)Fast and Multi-aspect Mining of Complex Time-stamped Event StreamsProceedings of the ACM Web Conference 202310.1145/3543507.3583370(1638-1649)Online publication date: 30-Apr-2023

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media