Let sleeping files lie: pattern matching in Z-compressed files
Journal of Computer and System Sciences
Compression of Low Entropy Strings with Lempel--Ziv Algorithms
SIAM Journal on Computing
Computational Geometry: Theory and Applications
An analysis of the Burrows—Wheeler transform
Journal of the ACM (JACM)
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Navigating nets: simple algorithms for proximity search
SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
The level ancestor problem simplified
Theoretical Computer Science - Latin American theorotical informatics
Journal of the ACM (JACM)
Compressing and searching XML data via two zips
Proceedings of the 15th international conference on World Wide Web
ACM Computing Surveys (CSUR)
Note: A simple storage scheme for strings achieving entropy bounds
Theoretical Computer Science
The MERL motion detector dataset
Proceedings of the 2007 workshop on Massive datasets
Compressed text indexes: From theory to practice
Journal of Experimental Algorithmics (JEA)
Generalized kraft inequality and arithmetic coding
IBM Journal of Research and Development
Compressing Kinetic Data from Sensor Networks
Algorithmic Aspects of Wireless Sensor Networks
Compression, indexing, and retrieval for massive string data
CPM'10 Proceedings of the 21st annual conference on Combinatorial pattern matching
Statistical encoding of succinct data structures
CPM'06 Proceedings of the 17th Annual conference on Combinatorial Pattern Matching
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As sensor networks increase in size and number, efficient techniques are required to process the very large data sets that they generate. Frequently, sensor networks monitor objects in motion within their vicinity; the data associated with the movement of these objects are known as kinetic data. In an earlier paper we introduced an algorithm which, given a set of sensor observations, losslessly compresses this data to a size that is within a constant factor of the asymptotically optimal joint entropy bound. In this paper we present an efficient algorithm for answering spatio-temporal range queries. Our algorithm operates on a compressed representation of the data, without the need to decompress it. We analyze the efficiency of our algorithm in terms of a natural measure of information content, the joint entropy of the sensor outputs. We show that with space roughly equal to entropy, queries can be answered in time that is roughly logarithmic in entropy. In addition, we show experimentally that on real-world data our range searching structures use less space and have faster query times than the naive versions. These results represent the first solutions to range searching problems over compressed kinetic sensor data.