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25-07-2019 | Issue 1/2020

Cognitive Computation 1/2020

EPF: A General Framework for Supporting Continuous Top-k Queries Over Streaming Data

Journal:
Cognitive Computation > Issue 1/2020
Authors:
Hong Jiang, Rui Zhu, Bin Wang
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Abstract

Continuous top-k query over sliding window is a fundamental problem in the domain of streaming data management, which monitors the query window and retrieves k objects with the highest scores when the window slides. The key of supporting this query is maintaining a subset of objects in the window, and try to retrieve answers from them when the window slides. The state-of-the-art approach called SAP utilizes the partition technique to support top-k searches. Its key idea is using, as few as possible, high-quality candidates to support the query via finding a proper partition. However, it has to waste relatively high computation cost in evaluating whether the partition is proper and re-scanning the widow. In this paper, we propose an ELM-based framework named EPF, which improves SAP via learning the nature of streaming data. If we learn that the distribution of streaming data is predictable, we could construct a suitable prediction model for a more efficient partition of the window. Furthermore, we propose a novel algorithm to reduce the re-scanning cost. We conduct a thorough experimental study of this technique on real and synthetic datasets and show the significant performance improvement when applying the technique in existing algorithms.

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