research-article

Parallelization of Massive Textstream Compression Based on Compressed Sensing

Authors:

Gang TianAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 36, Issue 2

Article No.: 17, Pages 1 - 18

https://doi.org/10.1145/3086702

Published: 21 August 2017 Publication History

Abstract

Compressing textstreams generated by social networks can both reduce storage consumption and improve efficiency such as fast searching. However, the compression process is a challenge due to the large scale of textstreams. In this article, we propose a textstream compression framework based on compressed sensing theory and design a series of matching parallel procedures. The new approach uses a linear projection technique in the textstream compression process, achieving fast compression speed and low compression ratio. Two processes are executed by designing elaborated parallel procedures for efficient compressing and decompressing of large-scale textstreams. The decompression process is implemented for approximate solutions of underdetermined linear systems. Experimental results show that the new method can efficiently achieve the compression and decompression tasks on a large amount of text generated by social networks.

References

[1]

Eugene Agichtein, Carlos Castillo, Debora Donato, Aristides Gionis, and Gilad Mishne. 2008. Finding high-quality content in social media. In Proceedings of the International Conference on Web Search and Data Mining (WSDM’08). ACM Press, New York, NY, 183--193

Digital Library

[2]

Michal Aharon, Michael Elad, and Alfred M. Bruckstein. 2006. K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Sign. Process. 54, 11 (2006), 4311--4322.

Digital Library

[3]

Richard Baraniuk, Mark Davenport, Ronald DeVore, and Michael Wakin. 2008. A simple proof of the restricted isometry property for random matrices. Construct. Approx. 28, 3 (2008), 253--263.

[4]

Ella Bingham and Heikki Mannila. 2001. Random projection in dimensionality reduction: Applications to image and text data. In Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’01). ACM Press, New York, NY, 245--250.

Digital Library

[5]

N. R. Brisaboa and A. Faria. 2010. Dynamic lightweight text compressionACM Trans. Inf. Syst. 28, 3 (2010), 1--32.

Digital Library

[6]

N. R. Brisaboa, A. Faria, G. Navarro, and J. R. Parama. 2008. New adaptive compressors for natural language text. Software Prac. Exper. 38, 13 (2008), 1429--1450.

Digital Library

[7]

Nieves R. Brisaboa, Antonio Faria, Gonzalo Navarro, and Jos R. Param. 2007. Lightweight natural language text compression. Inf. Retriev. 10, 1 (2007), 1--33.

Digital Library

[8]

Emmanuel Cands and Justin Romberg. 2007. Sparsity and incoherence in compressive sampling. Inverse Problems 23, 3 (2007), 969--985.

[9]

Scott Shaobing Chen, D. L. Donoho, and Michael A. Saunders. 1999. Atomic decomposition by basis pursuit. SIAM J. Sci. Comput. 20, 1 (1999), 33--61.

Digital Library

[10]

Chih Yi Chiu, Tsung Han Tsai, Guei Wun Han, Cheng Yu Hsieh, and Sheng Yang Li. 2013. Efficient video stream monitoring for near-duplicate detection and localization in a large-scale repository. ACM Trans. Inf. Syst. 31, 4 (2013), 84--87.

Digital Library

[11]

Edleno Silva de Moura, Gonzalo Navarro, Nivio Ziviani, and Ricardo Baeza-Yates. 2000. Fast and flexible word searching on compressed text. ACM Trans. Inf. Syst. 18, 2 (2000), 113--139.

Digital Library

[12]

Jeffrey Dean and Sanjay Ghemawat. 2008. Mapreduce: Simplified data processing on large clusters. Commun. ACM 51, 1 (2008), 107--113.

Digital Library

[13]

David L. Donoho. 2006. Compressed sensing. IEEE Trans. Inf. Theory 52, 4 (2006), 1289--1306.

Digital Library

[14]

Jiajia Huang, Min Peng, Hua Wang, Jinli Cao, Wang Gao, and Xiuzhen Zhang. 2017. A probabilistic method for emerging topic tracking in microblog stream. World Wide Web: Internet Web Inf. Syst. 20, 2 (2017), 325--350.

Digital Library

[15]

Christian S. Jensen, Dan Lin, Beng Chin Ooi, and Rui Zhang. 2006. Effective density queries on continuously moving objectsIn Proceedings of the 22nd International Conference on Data Engineering (ICDE’06). IEEE, Los Alamitos, CA, 71--81.

Digital Library

[16]

S. P. Kasiviswanathan, G. Cong, P. Melville, and R. D. Lawrence. 2013. Novel document detection for massive data streams using distributed dictionary learning. IBM J. Res. Dev. 57, 3--4 (2013), 1--15.

Digital Library

[17]

Shiva P. Kasiviswanathan, Huahua Wang, Arindam Banerjee, and Prem Melville. 2012. Online l1-dictionary learning with application to novel document detection. In Proceedings of the 25th International Conference on Neural Information Processing Systems (NIPS’12). MIT Press, Cambridge, MA, 2267--2275.

Digital Library

[18]

Daniel Kifer, Shai Ben-David, and Johannes Gehrke. 2004. Detecting change in data streams. In Proceedings of the 30th International Conference on Very Large Data Bases (VLDB’04). Morgan Kaufmann, San Mateo, CA, 180--191.

Digital Library

[19]

Nick Koudas, Beng Chin Ooi, Kian Lee Tan, and Rui Zhang. 2004. Approximate NN queries on streams with guaranteed error/performance bounds. In Proceedings of the 30th International Conference on Very Large Data Bases (VLDB’04). Morgan Kaufmann, San Mateo, CA, 804--815.

Digital Library

[20]

S. G. Mallat and Zhifeng Zhang. 1993. Matching pursuits with time-frequency dictionaries. IEEE Trans. Sign. Process. 41, 12 (1993), 3397--3415.

Digital Library

[21]

Zhongchen Miao, Kai Chen, Yi Fang, Jianhua He, Yi Zhou, Wenjun Zhang, and Hongyuan Zha. 2016. Cost-effective online trending topic detection and popularity prediction in microblogging. ACM Trans. Inf. Syst. 35, 3 (2016), 18:1--18:36.

Digital Library

[22]

Alistair Moffat. 1989. Word-based text compression. Software Prac. Exper. 19, 2 (1989), 185--198.

Digital Library

[23]

Ramesh Nallapati, Bowen Zhou, Cicero Nogueira Dos Santos, Caglar Gulcehre, and Bing Xiang. 2016. Abstractive text summarization using sequence-to-sequence RNNs and beyond. In Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning (CoNLL’16). ACL, Stroudsburg, PA, 280--290.

[24]

Y. C. Pati, R. Rezaiifar, and P. S. Krishnaparsad. 1993. Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition. In Proceedings of the 27th Annual Asilomar Conference on Signals Systems and Computers (ACSSC’93). IEEE, Los Alamitos, CA, 40--44.

[25]

Min Peng, Jiajia Huang, and Hui Fu. 2013. High quality microblog extraction based on multiple features fusion and time-frequency transformation. In Proceedings of the 14th International Conference on Web Information System Engineering (WISE’13). Springer, New York, NY, 188--201.

[26]

Arno Siebes and Jilles Vreeken. 2006. Item sets that compress. In Proceedings of the 6th SIAM International Conference on Data Mining (SDM’06). SIAM, Philadelphia, PA, 393--404.

[27]

Xiaoxun Sun, Hua Wang, Jiuyong Li, and Jian Pei. 2011. Publishing anonymous survey rating data. Data Min. Knowl. Discov. 23, 3 (2011), 379--406.

Digital Library

[28]

Zhongyuan Wang, Fang Wang, Haixun Wang, Zhirui Hu, Jun Yan, Fangtao Li, Ji-Rong Wen, and Zhoujun Li. 2016. Unsupervised head--modifier detection in search queries. ACM Trans. Knowl. Discov. Data 11, 2 (2016), 19:1--19:28.

Digital Library

[29]

J. Wen, J. Y. Nie, and H. Zhang. 2002. Query clustering using user logs. ACM Trans. Inf. Syst. 20, 1 (2002), 59--81.

Digital Library

[30]

Yang Xiang, Ruoming Jin, David Fuhry, and Feodor F. Dragan. 2008. Succinct summarization of transactional databases: An overlapped hyper rectangle scheme. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’08). ACM Press, New York, NY, 758--766.

Digital Library

[31]

Dong Xin, Jiawei Han, Xifeng Yan, and Hong Cheng. 2005. Mining compressed frequent-pattern sets. In Proceedings of the 31st International Conference on Very Large Data Bases (VLDB’05). ACM Press, New York, NY, 709--720.

Digital Library

[32]

Guandong Xu, Yanchun Zhang, and Lin Li. 2010. Web Mining and Social Networking: Techniques and Applications (1st ed.). Springer-Verlag New York, Inc., New York, NY.

Digital Library

[33]

Xintian Yang, Amol Ghoting, Yiye Ruan, and Srinivasan Parthasarathy. 2012. A framework for summarizing and analyzing Twitter feeds. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’12). ACM Press, New York, NY, 370--378.

Digital Library

[34]

Xintian Yang, Yiye Ruan, Srinivasan Parthasarathy, and Amol Ghoting. 2013. Summarization via pattern utility and ranking: A novel framework for social media data analytics. Bull. IEEE Comput. Soc. Techn. Comm. Data Eng. 36, 3 (2013), 67--76.

[35]

Rui Zhang, Nick Koudas, Beng Chin, and Ooi Divesh Srivastava. 2005. Multiple aggregations over data stream. In Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD’05). ACM Press, New York, NY, 299--310.

Digital Library

[36]

Rui Zhang, Nick Koudas, Beng Chin Ooi, Divesh Srivastava, and Pu Zhou. 2010. Streaming multiple aggregations using phantoms. VLDB J. 19, 4 (2010), 57--583.

Digital Library

[37]

Rui Zhang, Dan Lin, Kotagiri Ramamohanarao, and Elisa Bertino. 2008. Continuous intersection joins over moving objects. In Proceedings of the 2008 IEEE 24th International Conference on Data Engineering (ICDE’08). IEEE, Los Alamitos, CA, 863--872.

Digital Library

[38]

Jacob Ziv and Abraham Lempel. 1977. A universal algorithm for sequential data compression. IEEE Trans. Inf. Theory 23, 3 (1977), 337--343.

Digital Library

Cited By

Quadri HGu BWang HIslam S(2024)Advancing MobileNet Security: Weighted Adversarial Learning in Convolutional Neural Networks2024 11th International Conference on Machine Intelligence Theory and Applications (MiTA)10.1109/MiTA60795.2024.10751690(1-8)Online publication date: 14-Jul-2024
https://doi.org/10.1109/MiTA60795.2024.10751690
Xie ZTseng JStarke Svan de Panne MLiu C(2023)Hierarchical Planning and Control for Box Loco-ManipulationProceedings of the ACM on Computer Graphics and Interactive Techniques10.1145/36069316:3(1-18)Online publication date: 24-Aug-2023
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Reda DWon JYe Yvan de Panne MWinkler A(2023)Physics-based Motion Retargeting from Sparse InputsProceedings of the ACM on Computer Graphics and Interactive Techniques10.1145/36069286:3(1-19)Online publication date: 24-Aug-2023
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Index Terms

Parallelization of Massive Textstream Compression Based on Compressed Sensing
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Summarization
2. Theory of computation
  1. Design and analysis of algorithms
    1. Data structures design and analysis
      1. Data compression

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Published In

cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 36, Issue 2

April 2018

371 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3133943

Editor:
Maarten de Rijke
University of Amsterdam, The Netherlands

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Copyright © 2017 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 August 2017

Accepted: 01 April 2017

Revised: 01 January 2017

Received: 01 September 2016

Published in TOIS Volume 36, Issue 2

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Cited By

Quadri HGu BWang HIslam S(2024)Advancing MobileNet Security: Weighted Adversarial Learning in Convolutional Neural Networks2024 11th International Conference on Machine Intelligence Theory and Applications (MiTA)10.1109/MiTA60795.2024.10751690(1-8)Online publication date: 14-Jul-2024
https://doi.org/10.1109/MiTA60795.2024.10751690
Xie ZTseng JStarke Svan de Panne MLiu C(2023)Hierarchical Planning and Control for Box Loco-ManipulationProceedings of the ACM on Computer Graphics and Interactive Techniques10.1145/36069316:3(1-18)Online publication date: 24-Aug-2023
https://dl.acm.org/doi/10.1145/3606931
Reda DWon JYe Yvan de Panne MWinkler A(2023)Physics-based Motion Retargeting from Sparse InputsProceedings of the ACM on Computer Graphics and Interactive Techniques10.1145/36069286:3(1-19)Online publication date: 24-Aug-2023
https://dl.acm.org/doi/10.1145/3606928
Younes MKijak EKulpa RMalinowski SMulton F(2023)MAAIPProceedings of the ACM on Computer Graphics and Interactive Techniques10.1145/36069266:3(1-20)Online publication date: 24-Aug-2023
https://dl.acm.org/doi/10.1145/3606926
Starke PStarke SKomura TSteinicke F(2023)Motion In-Betweening with Phase ManifoldsProceedings of the ACM on Computer Graphics and Interactive Techniques10.1145/36069216:3(1-17)Online publication date: 24-Aug-2023
https://dl.acm.org/doi/10.1145/3606921
Xie ZStarke SLing Hvan de Panne M(2022)Learning Soccer Juggling Skills with Layer-wise Mixture-of-ExpertsACM SIGGRAPH 2022 Conference Proceedings10.1145/3528233.3530735(1-9)Online publication date: 27-Jul-2022
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Park JMin SChang PLee JPark MLee J(2022)Generative GaitNetACM SIGGRAPH 2022 Conference Proceedings10.1145/3528233.3530717(1-9)Online publication date: 27-Jul-2022
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Starke SMason IKomura T(2022)DeepPhaseACM Transactions on Graphics10.1145/3528223.353017841:4(1-13)Online publication date: 22-Jul-2022
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Li PAberman KZhang ZHanocka RSorkine-Hornung O(2022)GANimatorACM Transactions on Graphics10.1145/3528223.353015741:4(1-12)Online publication date: 22-Jul-2022
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Kim NLing HXie Zvan de Panne M(2021)Flexible Motion Optimization with Modulated Assistive ForcesProceedings of the ACM on Computer Graphics and Interactive Techniques10.1145/34801444:3(1-25)Online publication date: 27-Sep-2021
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