A Multi-Resolution Compression Scheme for EfficientWindow Queries over Road Network Databases

Abstract

Vector data and in particular road networks are being queried, hosted, and processed by many application domains such as mobile computing. However, many hosting/processing clients such as PDAs cannot afford this bulky data due to their storage and transmission limitations. In particular, the result of a typical spatial query such as window query is too huge for a transfer-and-store scenario. While several general vector data compression schemes have been studied by different communities, we propose a novel approach in vector data compression which is easily integrated within a geospatial query processing system. It uses line aggregation to reduce the number of relevant tuples and Huffman compression to achieve a multi-resolution compressed representation of a road network database. Our empirical results verify that our approach exhibits both a high compression ratio and fast query processing.

Road network compression techniques in spatiotemporal embedded systems

“…The compression technique in [1,11] has been proposed and integrated with the query processing pipeline of a road network database. Basically, the number of data records is reduced using a line aggregation technique.…”