Scalable Continuous Query Processing and Moving Object Indexing in Spatio-temporal Databases

摘要

Spatio-temporal database systems aim to answer continuous spatio-temporal queries issued over moving objects. In many scenarios such as in a wide area, the number of outstanding queries and the number of moving objects are so large that a server fails to process queries promptly. In our work, we aim to develop scalable techniques for spatio-temporal database systems. We focus on two aspects of spatio-temporal database systems: 1) the query processing algorithms for a large set of concurrent queries, and 2) the underlying indexing structures for constantly moving objects. For continuous query processing, we explore the techniques of Incremental Evaluation and Shared Execution, especially to k-nearest-neighbor queries. For moving object indexing, we utilize Update Memos to support frequent updates efficiently in spatial indexes such as R-trees. In this paper, we first identify the challenges towards scalable spatio-temporal databases, then review the current contributions we have achieved so far and discuss future research directions.