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Sliding window top-k dominating query processing over distributed data streams

机译:滑动式窗口top-k主导分布式数据流上的查询处理

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Preference query processing is important for a wide range of applications involving distributed databases, such as network monitoring, web-based systems, and market analysis. In such applications, data objects are generated frequently and massively, which presents an important and challenging problem of continuous query processing over distributed data stream environments. A top-k dominating query, which has been receiving much research attention recently, returns the k data objects that dominate the highest number of data objects in a given dataset, and due to its dominance-based ranking function, we can easily obtain superior data objects. An emerging requirement in distributed stream environments is an efficient technique for continuously monitoring top-k dominating data objects. Despite of this fact, no study has addressed this problem. In this paper, therefore, we address the problem of continuous top-k dominating query processing over distributed data stream environments. We present two algorithms that monitor the exact top-k dominating data and efficiently eliminate unqualified data objects for the result, which reduces both communication and computation costs. In addition to these algorithms, we present an approximate algorithm that further reduces both communication and computation costs. Extensive experiments on both synthetic and real data have demonstrated the efficiency and scalability of our algorithms.
机译:偏好查询处理对于涉及分布式数据库的广泛应用(例如网络监视,基于Web的系统和市场分析)非常重要。在这样的应用中,数据对象被频繁且大量地生成,这提出了在分布式数据流环境上进行连续查询处理的重要且具有挑战性的问题。排在前k位的查询,最近受到了很多研究关注,它返回了在给定数据集中占最大数量的数据对象的k个数据对象,并且由于其基于优势的排名功能,我们可以轻松地获得优质数据对象。分布式流环境中的新兴需求是一种连续监视top-k主导数据对象的有效技术。尽管有这个事实,但是没有研究解决这个问题。因此,在本文中,我们解决了分布式数据流环境中连续top-k支配查询处理的问题。我们提出了两种算法,它们监视确切的前k个主导数据并有效消除结果中不合格的数据对象,从而降低了通信和计算成本。除了这些算法,我们还提出了一种近似算法,可进一步降低通信和计算成本。合成和真实数据的大量实验证明了我们算法的效率和可扩展性。

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