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AdaHash: hashing-based scalable, adaptive hierarchical clustering of streaming data on Mapreduce frameworks

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Abstract

Despite the recent growth in large-scale, distributed streaming data processing, there are currently limited options for flexible clustering of streaming data on industrial production frameworks such as Mapreduce and Spark. The clustering methods being used by practitioners on such systems do not respond rapidly to new data, or do not adjust the number of clusters appropriately as more data are processed. This problem is particularly acute for unstructured data like text and other non-enumerated types that are common in log and message streams and not analyzed at scale for precisely this reason. To address such issues, this paper proposes a method for hierarchical clustering using adaptive hash (AdaHash) values that can be re-calculated during a periodic batch process and used for subsequent streaming processing at the speed of data arrival, assuming sufficient distributed compute resources. We demonstrate that this method is as fast as other (optimal) hashing methods while enabling an adaptive hash function on each batch cycle within a lambda architecture computing framework.

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Acknowledgements

The authors would like to thank Ravi Srinivasan for providing valuable feedback on the mathematical framing of the model.

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Authors and Affiliations

  1. JASK Labs, San Francisco, USA

    Dean Teffer

  2. Applied Research Laboratories, The University of Texas at Austin, Austin, USA

    Ravi Srinivasan

  3. Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, USA

    Joydeep Ghosh

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  1. Dean Teffer
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  2. Ravi Srinivasan
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  3. Joydeep Ghosh
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Correspondence to Dean Teffer.

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Teffer, D., Srinivasan, R. & Ghosh, J. AdaHash: hashing-based scalable, adaptive hierarchical clustering of streaming data on Mapreduce frameworks. Int J Data Sci Anal 8, 257–267 (2019). https://doi.org/10.1007/s41060-018-0145-7

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  • DOI: https://doi.org/10.1007/s41060-018-0145-7

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