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A survey of real-time approximate nearest neighbor query over streaming data for fog computing

机译:对流数据进行雾计算的实时近似最近邻居查询的调查

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Real-time approximate nearest neighbor (ANN) query over streaming data in fog computing environment is the fundamental problem of real-time analysis of big data. As the fog computing paradigm needs to provide real-time and low latency services, and traditional streaming data ANN query technology cannot be directly applied. Exploring the basic theory, querying framework and technology of real-time ANN query over streaming data for fog computing becomes one of the current research hotspots. This paper summarizes the related ANN query technology based on random hash, learning-to-hash and synopses, analyzes the problems and challenges of real-time ANN query in resource-limited fog computing environment, and finally discusses in detail the basic theory and method of the query, the dimension reduction and encoding method based on learning-to-hash, the generating synopses method for ANN query over streaming data from Internet of Thing, and the future related research directions of ANN query framework and others. Additionally, we propose a Dynamic Adaptive Quantization (DAQ) method for learning-to-hash. Experiments show that DAQ outperformed other quantization methods.
机译:在雾计算环境中对流数据进行实时近似最近邻(ANN)查询是大数据实时分析的基本问题。由于雾计算范例需要提供实时和低延迟的服务,因此传统的流数据ANN查询技术无法直接应用。探索雾流实时数据流实时ANN查询的基本理论,查询框架和技术成为当前研究热点之一。本文总结了基于随机散列,学习到哈希和概要的相关人工神经网络查询技术,分析了资源受限雾计算环境下实时人工神经网络查询存在的问题和挑战,最后详细讨论了其基本理论和方法。查询,基于哈希学习的降维和编码方法,基于Thing Internet的流数据上ANN查询的生成提要方法以及ANN查询框架的未来相关研究方向等。此外,我们提出了一种动态自适应量化(DAQ)方法用于哈希学习。实验表明,DAQ优于其他量化方法。

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