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An important investigation of moving objects involves searching for objects with specific movement patterns, such as "going up," "going towards southwest," or a combination of these. Movement patterns can be in various scales, and larger-scale patterns usually span over longer time periods with greater disturbances ignored. Movement pattern queries ask for moving objects which show a given movement pattern in a specific scale. This paper studies database techniques to support fast evaluation of movement pattern queries in user-specified scales. The database is assumed to contain position information of moving objects sampled at a certain time interval. A movement pattern is defined as a regular expression of movement letters where each letter describes a set of movement directions. For each series of positions, movement directions of all scales are precomputed and results are mapped into points on a plane. Points on this plane usually cluster well and can be readily bounded by trapezoids. These bounding trapezoids are then stored in a relational database and the query language SQL can be used to help evaluate movement pattern queries. This paper also reports some experiments conducted on a real data set as well as a synthesized data set. Results show that both the precomputation algorithm and the bounding strategy are efficient and scalable.