Prediction and indexing of moving objects with unknown motion patterns

Existing methods for prediction in spatio-temporal databases assume that objects move according to linear functions. This severely limits their applicability, since in practice movement is more complex, and individual objects may follow drastically different motion patterns. In order to overcome these problems, we first introduce a general framework for monitoring and indexing moving objects, where (i) each object computes individually the function that accurately captures its movement and (ii) a server indexes the object locations at a coarse level and processes queries using a filter-refinement mechanism. Our second contribution is a novel recursive motion function that supports a broad class of non-linear motion patterns. The function does not presume any a-priori movement but can postulate the particular motion of each object by examining its locations at recent timestamps. Finally, we propose an efficient indexing scheme that facilitates the processing of predictive queries without false misses