SIGKDD 2021: paper by Reza Akbarinia et al. accepted (research track).

The paper proposes PBA (Parallel Boundary Aggregator), a novel algorithm that computes incremental aggregations in parallel over massive data streams. The work has been done with Univ. Clermont-Auvergne (postdoc Chao Zhang and Professor Farouk Toumani).

Chao Zhang, Reza Akbarinia, Farouk Toumani. Efficient Incremental Computation of Aggregations over Sliding Windows. Int ACM Conference on Knowledge Discovery and Data Mining (SIGKDD), 2021.

Nowadays, we are witnessing the production of large volumes of continuous or real-time data in many application domains like traffic monitoring, medical monitoring, social networks, weather forecasting, network monitoring, etc. For example, every day around one trillion messages are processed through Uber data analytics infrastructure, and more than 500 million tweets are posted on Twitter. Efficient streaming algorithms are needed for analyzing data streams in such applications. In particular, aggregations having the inherent property of summarizing information from data, constitute a fundamental operator to compute real-time statistics in this context. In the streaming setting, aggregations are typically computed over finite subsets of a stream, called windows. In particular, sliding-window aggregation (SWAG) continuously computes a summary of the most recent data items in a given range r (aka window size) and using a given slide s.

One of the challenges faced by the SWAG algorithms is to incrementally compute aggregations over moving data, i.e., without recomputing the aggregation from scratch after inserting new data items or evicting old data items to/from the window. High throughput and low latency are essential requirements as stream processing systems are typically designed for real-time applications.

In this paper, we propose PBA (Parallel Boundary Aggregator), a novel algorithm that computes incremental aggregations in parallel. PBA groups continuous slices into chunks, and maintains two buffers for each chunk containing, respectively, the cumulative slice aggregations (denoted as csa) and the left cumulative slice aggregations (denoted as lcs) of the chunk’s slices. Using PBA, SWAGs can be computed in constant time for both amortized and worst-case time. We also propose an approach to optimize the chunk size, which guarantees the minimum latency for PBA. We conducted extensive empirical experiments using both synthetic and real-world datasets. Our experiments show that PBA behaves very well for average and large sliding windows (e.g., with sizes higher than 1024 values) compared to the state-of-the-art algorithms. For small-size windows, the results show the superiority of the non-parallel version of PBA (denoted as SBA) that outperforms other algorithms in terms of throughput.

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