Indexing historical spatio-temporal data in the cloud

Abstract

With the development of various Cloud platforms, providing spatio-temporal database services is an essential requirement for many applications, e.g., location-based services in Cloud. However, many previous works on processing queries in distributed environment fail to apply to spatio-temporal queries which is a significant role in spatio-temporal database. In this paper, we propose an efficient and scalable index for answering spatio-temporal queries in the Cloud. The index is a peer-to-peer-based overlay network, which is composed of two ring-structured overlays: spatial ring globally indexing spatial dimension and temporal ring for the temporal one. And it is featured by cost-aware function, i.e., a query is always able to be accomplished at a low cost, utilized by histograms keeping the distributions of Cloud nodes and data, maintained by each Cloud node. Both range query and kNN query can benefit from the mechanism, additionally, an elaborate algorithm for kNN processing is proposed, with which Cloud nodes can deliberatively send messages only to the result-related destinations. Furthermore, optimizations are also proposed for achieving low cost index maintenance and scalable kNN query processing. Experiments on both synthetic and real dataset show that our index is capable to support efficient and scalable range and kNN query, even for a skewed distribution.