Algorithms for clustering data
Algorithms for clustering data
Detecting graph-based spatial outliers: algorithms and applications (a summary of results)
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Data Compression: The Complete Reference
Data Compression: The Complete Reference
Dictionary Design Algorithms for Vector Map Compression
DCC '02 Proceedings of the Data Compression Conference
GIS: A Computing Perspective, 2nd Edition
GIS: A Computing Perspective, 2nd Edition
Distortion-constrained compression of vector maps
Proceedings of the 2007 ACM symposium on Applied computing
Variable-resolution Compression of Vector Data
Geoinformatica
A hybrid aggregation and compression technique for road network databases
Knowledge and Information Systems
An Effective GML Documents Compressor
IEICE - Transactions on Information and Systems
Optimal encoding of vector data with polygonal approximation and vertex quantization
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
A spatial index using MBR compression and hashing technique for mobile map service
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
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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.