Continuous top-k query for graph streams

Shirui Pan, Xingquan Zhu
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引用次数: 11

Abstract

In this paper, we propose to query correlated graphs in a data stream scenario, where an algorithm is required to retrieve the top k graphs which are mostly correlated to a query graph q. Due to the dynamic changing nature of the stream data and the inherent complexity of the graph query process, treating graph streams as static datasets is computationally infeasible or ineffective. In the paper, we propose a novel algorithm, Hoe-PGPL, to identify top-k correlated graphs from data stream, by using a sliding window which covers a number of consecutive batches of stream data records. Our theme is to employ Hoeffding bound to discover some potential candidates and use two level candidate checking (one corresponding to the whole sliding window level and one corresponding to the local data batch level) to accurately estimate the correlation of the emerging candidate patterns, without rechecking the historical stream data. Experimental results demonstrate that the proposed algorithm not only achieves good performance in terms of query precision and recall, but also is several times, or even an order of magnitude, more efficient than the straightforward algorithm with respect to the time and the memory consumption. Our method represents the first research endeavor for data stream based top-k correlated graph query.
图流的连续top-k查询
在本文中,我们提出在数据流场景中查询相关图,其中需要一种算法来检索最前面的k个图,这些图主要与查询图q相关。由于流数据的动态变化性质和图查询过程的固有复杂性,将图流视为静态数据集在计算上是不可行的或无效的。在本文中,我们提出了一种新的算法,Hoe-PGPL,通过使用覆盖多个连续批次流数据记录的滑动窗口,从数据流中识别top-k相关图。我们的主题是使用Hoeffding bound来发现一些潜在的候选模式,并使用两级候选模式检查(一个对应于整个滑动窗口级别,一个对应于局部数据批处理级别)来准确估计新出现的候选模式的相关性,而无需重新检查历史流数据。实验结果表明,该算法不仅在查询精度和查全率方面取得了良好的性能,而且在时间和内存消耗方面比直接算法的效率提高了几倍甚至一个数量级。我们的方法代表了基于top-k相关图查询的数据流的第一个研究尝试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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