Neural networks for pattern recognition
Neural networks for pattern recognition
RoboCup: The Robot World Cup Initiative
AGENTS '97 Proceedings of the first international conference on Autonomous agents
DOMINO: databases fOr MovINg Objects tracking
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
A data model and data structures for moving objects databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A foundation for representing and querying moving objects
ACM Transactions on Database Systems (TODS)
Alternative techniques for the efficient acquisition of haptic data
Proceedings of the 2001 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Modeling and Querying Moving Objects
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Managing Time in GIS: An Event-Oriented Approach
Proceedings of the International Workshop on Temporal Databases: Recent Advances in Temporal Databases
Modeling, Tracking and Interactive Animation of Faces and Heads Using Input from Video
CA '96 Proceedings of the Computer Animation
Logical Data Modeling of SpatioTemporal Applications: Definitions and a Model
IDEAS '98 Proceedings of the 1998 International Symposium on Database Engineering & Applications
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Moving sensors refers to an emerging class of data intensive applications that inpacts disciplines such as communication, health-care, scientific applications, etc. These applications consist of a fixed number of sensors that move and produce streams of data as a function of time. They may require the system to match these streams against stored streams to retrieve relevant data (patterns). With communication, for example, a speaking impaired individual might utilize a haptic glove that translates hand signs into written (spoken) words. The glove consists of sensors for different finger joints. These sensors report their location and values as a function of time, producing streams of data. These streams are matched against a repository of spatio-temporal streams to retrieve the corresponding English character or word.The contributions of this study are two fold. First, it introduces a framework to store and retrieve "moving sensors" data. The framework advocates physical data independence and software-reuse. Second, we investigate alternative representations for storage and retrieve of data in support of query processing. We quantify the tradeoff associated with these alternatives using empirical data RoboCup soccer matches.