Moving sensors refers to an emerging class of data intensive applications that impacts 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 folds. 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 retrieval of data in support of query processing. We quantify the tradeoff associated with these alternatives using empirical data from RoboCup soccer matches.
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