Continuous kNN-Skyline Queries over Moving Objects with Uncertainty

• Main Authors: Zong-HanHe, 何宗翰
• Other Authors: Chiang Lee
• Format: Others
• Language: en_US
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/37085470739703321905

碩士 === 國立成功大學 === 資訊工程學系碩博士班 === 100 === Continuous k nearest neighbor-skyline query (CkNN-SQ) is an important type of spatio-temporal queries. Given a query time interval [ts, te] and a moving query object q, a CkNN-SQ is to retrieve the k-nearest neighbor skyline points (kNN-SP) of q at each time instant within [ts, te]. Different from the previous works, our work devotes to overcoming the past assumption that each object is static with certain dimensional values and located in road networks. In this paper, we focus on processing the CkNN-SQ over moving objects with uncertain dimensional values in Euclidean space and the velocity of each object (including the query object) varies within a known range. Such a query is called the continuous possible-kNN-skyline query (CPkNN-SQ). We first discuss the difficulties raised by the uncertainty of object and then propose the CPkNN-SQ algorithm operated with a data-partitioning index, called the uncertain TPR-tree (UTPR-tree), to efficiently answer the CPkNN-SQ. Comprehensive experiments are performed to demonstrate the effectiveness and the efficiency of the proposed approach.