ctor map compression: a clustering approach Shashi Shekhar, Yan Huang, Judy Djugash, and Changqing Zhou   View Paper (PDF)   Return to Location-based services and mobile computing: algorithms Abstract Vector maps (e.g. road maps) are widely used in a variety of applications such as Geographic Information Systems(GIS), Intelligent Transportation Systems(ITS) and mobile computing. However, the relatively large size of vector maps has in some cases negatively impacted their usage and application in these systems because of the small storage available with mobile wireless devices or the limited bandwidth of the data transportation. In these cases, data compression techniques need to be applied on these vector maps to handle larger datasets and faster data transportation. Among all the data compression techniques, dictionary-based compression is a good candidate since encoding and decoding do not need a significantly large amount of computing resources. This paper explores the problem of dictionary design for dictionary based vector map compression. We propose a novel clustering-based dictionary design which adapts the dictionary to a given dataset, yielding better approximation. Experimental evaluation shows that when the dictionary size is fixed, the proposed clustering-based technique achieves lower error compared with conventional dictionary compression approaches. DiSC'03 © 2003 Association for Computing Machinery