Visible to the public Biblio

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Kim, Taewoo, Thirumaraiselvan, Vidhyasagar, Jia, Jianfeng, Li, Chen.  2017.  Caching Geospatial Objects in Web Browsers. Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. :92:1–92:4.

Map-based services are becoming increasingly important in many applications. These services often need to show geospatial objects (e.g., cities and parks) in Web browsers, and being able to retrieve such objects efficiently is critical to achieving a low response time for user queries. In this demonstration we present a browser-based caching technique to store and load geospatial objects on a map in a Web page. The technique employs a hierarchical structure to store and index polygons, and does intelligent prefetching and cache replacement by utilizing the information about the user's recent browser activities. We demonstrate the usage of the technique in an application called TwitterMap for visualizing more than 1 billion tweets in real time. We show its effectiveness by using different replacement policies. The technique is implemented as a general-purpose Javascript library, making it suitable for other applications as well.

L
Wang, Xi, Yao, Jun, Ji, Hongxia, Zhang, Ze, Li, Chen, Ma, Beizhi.  2018.  A Local Integral Hash Nearest Neighbor Algorithm. 2018 3rd International Conference on Mechanical, Control and Computer Engineering (ICMCCE). :544—548.

Nearest neighbor search algorithm plays a very important role in computer image algorithm. When the search data is large, we need to use fast search algorithm. The current fast retrieval algorithms are tree based algorithms. The efficiency of the tree algorithm decreases sharply with the increase of the data dimension. In this paper, a local integral hash nearest neighbor algorithm of the spatial space is proposed to construct the tree structure by changing the way of the node of the access tree. It is able to express data distribution characteristics. After experimental testing, this paper achieves more efficient performance in high dimensional data.

R
CUI, A-jun, Li, Chen, WANG, Xiao-ming.  2019.  Real-Time Early Warning of Network Security Threats Based on Improved Ant Colony Algorithm. 2019 12th International Conference on Intelligent Computation Technology and Automation (ICICTA). :309—316.
In order to better ensure the operation safety of the network, the real-time early warning of network security threats is studied based on the improved ant colony algorithm. Firstly, the network security threat perception algorithm is optimized based on the principle of neural network, and the network security threat detection process is standardized according to the optimized algorithm. Finally, the real-time early warning of network security threats is realized. Finally, the experiment proves that the network security threat real-time warning based on the improved ant colony algorithm has better security and stability than the traditional warning methods, and fully meets the research requirements.