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DUET: A Generic Framework for Finding Special Quadratic Elements in Data Streams

Authors:

Guihai ChenAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 2989 - 2997

https://doi.org/10.1145/3485447.3512019

Published: 25 April 2022 Publication History

Abstract

Finding special items, like heavy hitters, top-k, and persistent items, has always been a hot issue in data stream processing for web analysis. While data streams nowadays are usually high-dimensional, most prior works focus on special items according to a certain primary dimension and yield little insight into the correlations between dimensions. Therefore, we propose to find special quadratic elements to reveal close correlations. Based on the items mentioned above, we extend our problem to three applications related to heavy hitters, top-k, and persistent items, and design a generic framework DUET to process them. Besides, we analyze the error bound of our algorithm and conduct extensive experiments on four data sets. Our experimental results show that DUET can achieve 3.5 times higher throughput and three orders of magnitude lower average relative error compared with cutting-edge algorithms.

References

[1]

[n. d.]. Advanced Persistent Threat. http://www.usenix.org/event/lisa09/tech/slides/daly. pdf.

[2]

[n. d.]. BobHash. http://burtleburtle.net/bob/hash/evahash.html.

[3]

[n. d.]. The CAIDA Anonymized Internet Traces. https://www.caida.org/data/passive/passive_2016_dataset.xml.

[4]

[n. d.]. Social Network: Reddit Hyperlink Network. http://snap.stanford.edu/data/soc-RedditHyperlinks.html.

[5]

[n. d.]. Source Codes of DUET. https://github.com/callitwhatyouwannt13/WWW_DUET.

[6]

[n. d.]. The Source Codes of HeavyKeeper. https://github.com/papergitkeeper/heavy-keeper-project.

[7]

[n. d.]. Stack Overflow Temporal Network. http://snap.stanford.edu/data/sx-stackoverflow.html.

[8]

Juan Jose Garcia Adeva and Juan Manuel Pikatza Atxa. 2007. Intrusion detection in web applications using text mining. Engineering Applications of Artificial Intelligence 20, 4(2007), 555–566.

Digital Library

[9]

Mohammad Alizadeh, Tom Edsall, Sarang Dharmapurikar, Ramanan Vaidyanathan, Kevin Chu, Andy Fingerhut, Vinh The Lam, Francis Matus, Rong Pan, Navindra Yadav, 2014. CONGA: Distributed congestion-aware load balancing for datacenters. In Proceedings of ACM Conference on SIGCOMM. ACM, 503–514.

Digital Library

[10]

Ran Ben Basat, Gil Einziger, Roy Friedman, and Yaron Kassner. 2017. Optimal elephant flow detection. In Proceedings of International Conference on Computer Communications. IEEE, 1–9.

[11]

Ran Ben Basat, Gil Einziger, Michael Mitzenmacher, and Shay Vargaftik. 2020. Faster and More Accurate Measurement through Additive-Error Counters. In Proceedings of International Conference on Computer Communications. IEEE, 1251–1260.

Digital Library

[12]

Burton H Bloom. 1970. Space/time trade-offs in hash coding with allowable errors. Commun. ACM 13, 7 (1970), 422–426.

Digital Library

[13]

Moses Charikar, Kevin Chen, and Martin Farach-Colton. 2002. Finding frequent items in data streams. In Proceedings of International Colloquium on Automata, Languages, and Programming. Springer, 693–703.

[14]

Graham Cormode and Shan Muthukrishnan. 2005. An improved data stream summary: the count-min sketch and its applications. Journal of Algorithms 55, 1 (2005), 58–75.

Digital Library

[15]

Haipeng Dai, Muhammad Shahzad, Alex X Liu, and Yuankun Zhong. 2016. Finding persistent items in data streams. Proceedings of the VLDB Endowment 10, 4 (2016), 289–300.

Digital Library

[16]

Italo Epicoco, Massimo Cafaro, and Marco Pulimeno. 2018. Fast and accurate mining of correlated heavy hitters. Data Mining and Knowledge Discovery 32, 1 (2018), 162–186.

Digital Library

[17]

Cristian Estan and George Varghese. 2002. New directions in traffic measurement and accounting. In Proceedings of ACM Conference on SIGCOMM. ACM, 323–336.

Digital Library

[18]

Qun Huang, Patrick PC Lee, and Yungang Bao. 2018. SketchLearn: relieving user burdens in approximate measurement with automated statistical inference. In Proceedings of ACM Conference on SIGCOMM. ACM, 576–590.

Digital Library

[19]

Mohammad T Irfan and Tucker Gordon. 2018. The power of context in networks: Ideal point models with social interactions. In Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems. 910–918.

[20]

Bibudh Lahiri and Srikanta Tirthapura. 2009. Finding correlated heavy-hitters over data streams. In Proceedings of International Performance Computing and Communications Conference. IEEE, 307–314.

[21]

Bibudh Lahiri, Srikanta Tirthapura, and Jaideep Chandrashekar. 2014. Space-efficient tracking of persistent items in a massive data stream. Statistical Analysis and Data Mining: The ASA Data Science Journal 7, 1(2014), 70–92.

Digital Library

[22]

Jizhou Li, Zikun Li, Yifei Xu, Shiqi Jiang, Tong Yang, Bin Cui, Yafei Dai, and Gong Zhang. 2020. WavingSketch: An Unbiased and Generic Sketch for Finding Top-k Items in Data Streams. In Proceedings of International Conference on Knowledge Discovery & Data Mining. ACM, 1574–1584.

Digital Library

[23]

Zaoxing Liu, Ran Ben-Basat, Gil Einziger, Yaron Kassner, Vladimir Braverman, Roy Friedman, and Vyas Sekar. 2019. Nitrosketch: Robust and general sketch-based monitoring in software switches. In Proceedings of ACM Conference on SIGCOMM. ACM, 334–350.

Digital Library

[24]

Zaoxing Liu, Antonis Manousis, Gregory Vorsanger, Vyas Sekar, and Vladimir Braverman. 2016. One sketch to rule them all: Rethinking network flow monitoring with univmon. In Proceedings of ACM Conference on SIGCOMM. ACM, 101–114.

Digital Library

[25]

Ahmed Metwally, Divyakant Agrawal, and Amr El Abbadi. 2005. Efficient computation of frequent and top-k elements in data streams. In Proceedings of International Conference on Database Theory. Springer, 398–412.

[26]

Katsiaryna Mirylenka, Themis Palpanas, Graham Cormode, and Divesh Srivastava. 2013. Finding interesting correlations with conditional heavy hitters. In Proceedings of International Conference on Data Engineering. IEEE, 1069–1080.

Digital Library

[27]

Jayadev Misra and David Gries. 1982. Finding repeated elements. Science of Computer Programming 2, 2 (1982), 143–152.

[28]

Marco Pulimeno, Italo Epicoco, Massimo Cafaro, Catiuscia Melle, and Giovanni Aloisio. 2018. Parallel Mining of Correlated Heavy Hitters on Distributed and Shared-Memory Architectures. In Proceedings of International Conference on Big Data. IEEE, 5111–5118.

[29]

Amin Shokrollahi. 2006. Raptor codes. IEEE Transactions on Information Theory 52, 6 (2006), 2551–2567.

Digital Library

[30]

Stuart Staniford, James A Hoagland, and Joseph M McAlerney. 2002. Practical automated detection of stealthy portscans. Journal of Computer Security 10, 1-2 (2002), 105–136.

Digital Library

[31]

Karthik Subbian, Charu Aggarwal, and Kshiteesh Hegde. 2016. Recommendations for streaming data. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. 2185–2190.

Digital Library

[32]

Lu Tang, Qun Huang, and Patrick PC Lee. 2019. MV-Sketch: A fast and compact invertible sketch for heavy flow detection in network data streams. In Proceedings of International Conference on Computer Communications. IEEE, 2026–2034.

Digital Library

[33]

Tobias Weller. 2018. Compromised account detection based on clickstream data. In Companion Proceedings of the The Web Conference 2018. 819–823.

Digital Library

[34]

Tong Yang, Junzhi Gong, Haowei Zhang, Lei Zou, Lei Shi, and Xiaoming Li. 2018. HeavyGuardian: Separate and guard hot items in data streams. In Proceedings of International Conference on Knowledge Discovery & Data Mining. ACM, 2584–2593.

Digital Library

[35]

Tong Yang, Jie Jiang, Peng Liu, Qun Huang, Junzhi Gong, Yang Zhou, Rui Miao, Xiaoming Li, and Steve Uhlig. 2018. Elastic sketch: Adaptive and fast network-wide measurements. In Proceedings of the ACM Special Interest Group on Data Communication. ACM, 561–575.

Digital Library

[36]

Yinda Zhang, Jinyang Li, Yutian Lei, Tong Yang, Zhetao Li, Gong Zhang, and Bin Cui. 2020. On-off sketch: a fast and accurate sketch on persistence. Proceedings of the VLDB Endowment 14, 2 (2020), 128–140.

Digital Library

Cited By

Wang HDai HChen SChen G(2024)Rethinking Hash Tables: Challenges and Opportunities with Compute Express Link (CXL)Proceedings of the ACM Turing Award Celebration Conference - China 202410.1145/3674399.3674418(23-27)Online publication date: 5-Jul-2024
https://dl.acm.org/doi/10.1145/3674399.3674418
Gao GMa THuang HSun YWang HDu YChen S(2024)Scout Sketch+: Finding Both Promising and Damping Items Simultaneously in Data StreamsIEEE/ACM Transactions on Networking10.1109/TNET.2024.346919632:6(5491-5506)Online publication date: Dec-2024
https://doi.org/10.1109/TNET.2024.3469196
Fan ZWang XLi XGuo JLiu WLi HLong SZhong ZYang TChen XCui B(2024)SteadySketch: A High-Performance Algorithm for Finding Steady Flows in Data StreamsIEEE/ACM Transactions on Networking10.1109/TNET.2024.344448832:6(5004-5019)Online publication date: Dec-2024
https://doi.org/10.1109/TNET.2024.3444488
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Index Terms

DUET: A Generic Framework for Finding Special Quadratic Elements in Data Streams
1. Information systems
  1. Information systems applications
    1. Data mining
2. Theory of computation
  1. Design and analysis of algorithms
  2. Theory and algorithms for application domains

Index terms have been assigned to the content through auto-classification.

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Information

Published In

cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

Copyright © 2022 ACM.

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Publication History

Published: 25 April 2022

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National Natural Science Foundation of China

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WWW '22

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SIGWEB

WWW '22: The ACM Web Conference 2022

April 25 - 29, 2022

Virtual Event, Lyon, France

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Wang HDai HChen SChen G(2024)Rethinking Hash Tables: Challenges and Opportunities with Compute Express Link (CXL)Proceedings of the ACM Turing Award Celebration Conference - China 202410.1145/3674399.3674418(23-27)Online publication date: 5-Jul-2024
https://dl.acm.org/doi/10.1145/3674399.3674418
Gao GMa THuang HSun YWang HDu YChen S(2024)Scout Sketch+: Finding Both Promising and Damping Items Simultaneously in Data StreamsIEEE/ACM Transactions on Networking10.1109/TNET.2024.346919632:6(5491-5506)Online publication date: Dec-2024
https://doi.org/10.1109/TNET.2024.3469196
Fan ZWang XLi XGuo JLiu WLi HLong SZhong ZYang TChen XCui B(2024)SteadySketch: A High-Performance Algorithm for Finding Steady Flows in Data StreamsIEEE/ACM Transactions on Networking10.1109/TNET.2024.344448832:6(5004-5019)Online publication date: Dec-2024
https://doi.org/10.1109/TNET.2024.3444488
Wang HDai HChen SLi MGu RChai HZheng JChen ZLi SDeng XChen G(2024)Bamboo Filters: Make Resizing Smooth and AdaptiveIEEE/ACM Transactions on Networking10.1109/TNET.2024.340399732:5(3776-3791)Online publication date: Oct-2024
https://doi.org/10.1109/TNET.2024.3403997
Liu JDai HXia RLi MBasat RLi RGu RZheng JChen G(2024)A Generic Framework for Finding Special Quadratic Elements in Data StreamsIEEE/ACM Transactions on Networking10.1109/TNET.2024.339202932:4(3269-3284)Online publication date: Aug-2024
https://doi.org/10.1109/TNET.2024.3392029
Ma TGao GHuang HSun YDu Y(2024)Scout Sketch: Finding Promising Items in Data StreamsIEEE INFOCOM 2024 - IEEE Conference on Computer Communications10.1109/INFOCOM52122.2024.10621279(1561-1570)Online publication date: 20-May-2024
https://doi.org/10.1109/INFOCOM52122.2024.10621279
Liu JBasat RDe Wardt LDai HChen G(2024)DISCO: A Dynamically Configurable Sketch Framework in Skewed Data Streams2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00365(4801-4814)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00365
Li XFan ZLi HZhong ZGuo JLong SYang TCui B(2023)SteadySketch: Finding Steady Flows in Data Streams2023 IEEE/ACM 31st International Symposium on Quality of Service (IWQoS)10.1109/IWQoS57198.2023.10188743(01-09)Online publication date: 19-Jun-2023
https://doi.org/10.1109/IWQoS57198.2023.10188743
Dai HWang HChen ZZheng JLi MGu RTian CDou W(2023)Variable-length Encoding Framework: A Generic Framework for Enhancing the Accuracy of Approximate Membership Queries2023 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM58522.2023.00015(61-70)Online publication date: 1-Dec-2023
https://doi.org/10.1109/ICDM58522.2023.00015

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