Evaluating continuous image-nearest neighbor query on moving objects with uncertainty

Continuous image-nearest neighbor image query is one of the most fundamental queries in the field of spatio-temporal databases. Given a time interval image, a image query is to retrieve the image-nearest neighbors image of a moving user at each time instant within image. Existing methods for processing a image query, however, assume that each object moves with a fixed direction and/or a fixed speed. In this paper, we relieve this assumption by allowing both the moving speed and the moving direction of each object to vary. This uncertainty on speed and direction of a moving object would increase the complexity of processing a image query. We thoroughly analyze the involved issues incurred by this uncertainty and propose a continuous possible KNN (CPKNN) algorithm to effectively find the objects that could be the image. These objects are termed the possible KNNs (image) in this paper. A probability-based model is designed accordingly to quantify the possibility of each image being the image. In addition, we design a PKNN updating mechanism to rapidly evaluate the new query result when object updates occur. Comprehensive experiments are conducted to demonstrate the effectiveness and the efficiency of the proposed approach.