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Adaptivity in continuous massively parallel distance-based outlier detection

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Abstract

We deal with the problem of dynamically allocating the workload to multiple workers in massively parallel continuous distance-based outlier detection, where the workload is conceptually split in contiguous overlapping regions. The main challenges stem from the fact that modern streaming processing frameworks, such as Apache Flink and Spark Streaming, do not support feedback loops, the process is stateful while the adaptations do not result in key redistribution but in modifying the region boundaries associated with each key. These challenges correspond to overlooked issues, which call for novel solutions that we provide in our work. More specifically, firstly, we propose an architecture for allowing such adaptations in Flink. Secondly, we propose specific techniques for adaptive region definition that are applicable to any distance metric. Finally, we conduct thorough experimental evaluation and our results show that our proposal is both efficient and effective even in small finite streams. In addition, our proposal is shown to be insensitive to the exact continuous outlier detection algorithm and outlier query parameters.

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Availability of data and material (data transparency)

all datasets used are publicly available from third-part repositories.

Notes

  1. An early short version of this work has appeared in [29], which introduced the technique that is termed as naive in this work and was tailored for the Euclidean space and evaluated using only single-dimensional numerical datasets. We significantly extend and improve upon this early work through proposing and experimenting with more eager and sophisticated techniques, while supporting arbitrary metric distances and evaluating using both numeric and text datasets with a high number of dimensions.

  2. The implementation of the whole framework along with the techniques in this work is publicly available from https://github.com/tatoliop/PROUD-PaRallel-OUtlier-Detection-for-streams/tree/adaptive_partitioning.

  3. https://ci.apache.org/projects/flink/flink-docs-stable/dev/stream/side_output.html.

  4. We have experimented with additional parameters (slide sizes of 1% up to 50% of the window size) and the results are similar to the ones to be presented; due to space constraints such experiments are omitted.

  5. To further investigate any possible correlation we have run some tests using the MMPC algorithm [6] after transforming the runtime values to a binary target variable (improvement or not-improvement); this has also not yielded any concrete results.

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Acknowledgements

This research work has been supported by the European Commission under the Horizon 2020 Programme, through funding of the LifeChamps project (Grant 875329).

Funding

European Commission under the Horizon 2020 Programme, LifeChamps project (Grant 875329).

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Authors and Affiliations

  1. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Theodoros Toliopoulos & Anastasios Gounaris

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  1. Theodoros Toliopoulos
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Correspondence to Theodoros Toliopoulos.

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all code is available from https://github.com/tatoliop/PROUD-PaRallel-OUtlier-Detection-for-streams/tree/adaptive_partitioning.

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Toliopoulos, T., Gounaris, A. Adaptivity in continuous massively parallel distance-based outlier detection. Computing 104, 2659–2684 (2022). https://doi.org/10.1007/s00607-022-01101-5

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