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

作者：Jiang, Xiaohui^*; Hu, Peng; Li, Yanchao; Yuan, Chi; Masood, Isma; Jelodar, Hamed; Rabbani, Mandi; Wang, Yongli

来源：Journal of Parallel and Distributed Computing, 2018, 116: 50-62.

DOI：10.1016/j.jpdc.2018.01.005

• Fog computing
• Streaming data
• Approximate nearest neighbor query
• Real-time analysis
• Hashing learning
• Quantization method

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.

• 出版日期2018-6
• 单位南京理工大学