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SILVERBACK+: scalable association mining via fast list intersection for columnar social data

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Abstract

We present Silverback+, a scalable probabilistic framework for accurate association rule and frequent item-set mining of large-scale social behavioral data. Silverback+ tackles the problem of efficient storage utilization and management via: (1) probabilistic columnar infrastructure and (2) using Bloom filters and sampling techniques. In addition, probabilistic pruning techniques based on Apriori method are developed, for accelerating the mining of frequent item-sets. The proposed target-driven techniques yield a significant reduction of the size of the frequent item-set candidates, as well as the required number of repetitive membership checks through a novel list intersection algorithm. Extensive experimental evaluations demonstrate the benefits of this context-aware consideration and incorporation of the infrastructure limitations when utilizing the corresponding research techniques. When compared to the traditional Hadoop-based approach for improving scalability by straightforwardly adding more hosts, Silverback+ exhibits a much better runtime performance, with negligible loss of accuracy.

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Notes

  1. http://www.mongodb.org.

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Acknowledgments

This work is supported in part by the following Grants: NSF awards CCF-1029166, IIS-1343639, CCF-1409601, CNS-0910952 and III 1213038; DOE awards DE-SC0007456, DE-SC0014330; ONR Grant N00014-14-1-0215.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, USA

    Yusheng Xie, Diana Palsetia, Goce Trajcevski, Ankit Agrawal & Alok Choudhary

  2. NEC Laboratories America, Princeton, NJ, USA

    Zhengzhang Chen

  3. Baidu Research, Sunnyvale, CA, USA

    Yusheng Xie

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  1. Yusheng Xie
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  2. Zhengzhang Chen
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  4. Goce Trajcevski
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  5. Ankit Agrawal
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Correspondence to Yusheng Xie.

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Xie, Y., Chen, Z., Palsetia, D. et al. SILVERBACK+: scalable association mining via fast list intersection for columnar social data. Knowl Inf Syst 50, 969–997 (2017). https://doi.org/10.1007/s10115-016-0962-8

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