作为一种监控与跟踪车流和人类活动等的潜在技术,RFID(radio frequency identification)已经在数据库领域得到了很大关注.RFID监控对象上的k-近邻查询是一种最重要的时空查询,能够用来支持有价值的高层信息分析.但是,不同于没有限制的空间和基于限制的空间,RFID监控场景通常被设置在一种半限制的空间内,需要新的存储和距离计算策略.此外,监控对象位置的不确定性对查询语义和处理方法提出了挑战.提出了半限制空间的概念,并且分析了基于RFID的半限制空间的模型.基于半限制空间,在给定一个动态查询点的基础上,提出了3种模型和算法以有效地估计可能性k-近邻的查询结果,并采用一些特殊的索引技术加快查询的速度.实验对提出算法的效率和准确性进行了评估,表明了相关方法的有效性.%As a promising technology for monitoring and tracing the vehicle flows and human activities, radio frequency identification (RFID) has received much attention in database community, k-nearest neighbor (k-NN) query over RFID monitored objects is one of the most important spatio-temporal queries used to support valuable information analysis. Different from the constraint-free space and constraint-based space, however, RFID monitoring scenario is usually merged into a semi-constraint space, which desires new data storage and distance evaluation strategies. Furthermore, the uncertainty of the monitored object locations challenges the query semantics and processing methods. In this paper, the concept of semi-constraint space is proposed, and the RFID-based semi-constraint space model is analyzed. Based on the semi-constraint space, three models and algorithms are proposed to estimate the probable k-NN results given a dynamic query point. Some special indexing techniques are adopted to speed the query. The experiment evaluates the efficiency and accuracy of the proposed algorithms and proves the effectiveness of relative methods.
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