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January 21 February 21 March 21 April 21 May 21 June 21 July 21 August 21 September 21 October 21 November 21 8 in. 22 in. 40 in. 58 in. 72 in. 80 in. 72 in. 58 in. 40 in. 22 in. 8 in. 2 in. 20 cm 55 cm 100 cm 145 cm 195 cm 200 cm 195 cm 145 cm 100 cm 55 cm 20 cm 5 cm from floor from floor from floor from floor from floor from floor from floor from floor from floor from floor from floor from floor



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Views can be a very useful tool for managing very large databases (VLDBs). Typically, data warehouse systems contain huge volumes of uniform data. A textbook example is a retailer that collects information about sales over years. Some analyses would process many years of data, but others would focus on only a few months or the current year. If everything were in a single table, queries and management of data would become increasingly difficult. In such a scenario, the retailer s sales information would be split into several horizontally partitioned tables such as OrderItem2000, OrderItem2001, and OrderItem2002. For analyses (queries) that span all tables, you can create a view that puts them all together:





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way, pixels in a video image typically have similar pixels all around them. To make use of these neighborhood relationships, it is desirable to transform not only in lines and columns, but also in areas. This can be achieved by a planar transform. In practice, separable transforms are used almost exclusively. A separable planar transform is nothing more than the repeated application of a simple transform. It is almost always applied to square picture segments of size N N, and it progresses in two steps, as illustrated in Figure E.2. First, all lines of the picture segments are transformed in succession, then all the rows of the segments calculated in the first step are transformed.

Figure E.2 A simplified search of a best-matched block. (From [2]. Used with permission.)

These measurements are assuming a measurement of 87 in. between the center of the heliodon table and the light source

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In textbooks, the planar transform is frequently called a 2D transform. The transform is, in principle, possible for any segment forms, not just square ones [4]. Consequently, for a segment of size N N, 2N transforms are used. The coefficients now are no longer arranged as vectors (i.e., a list of values), but as a matrix (i.e., a table). The coefficients of the i lines and j columns are called cij (i,j 1 N). Each of these coefficients no longer represents a basic vector, but a basic picture. In this way, each N N picture segment is composed of N N different basic pictures, in which each coefficient gives the weighting of a particular basic picture. Figure E.3 shows the basic pictures of the coefficients c11 and c23 for a planar 4 4 DCT. Because c11 represents the dc part, it is called the dc coefficient; the others are appropriately called the ac coefficients. The planar transform of television pictures in the interlaced format is somewhat problematic. In moving regions of the picture, depending on the speed of motion, the similarities of vertically neighboring pixels of a frame are lost because changes have occurred between samplings of the two halves of the picture. Consequently, interlaced scanning may cause

Create View vOrderItem as select * from OrderItem2000 UNION ALL select * from OrderItem2001 UNION ALL select * from OrderItem2002

Figure E.3 The mechanics of motion-compensated prediction. Shown are the pictures for a planar 4 4 DCT. Element C11 is located at row 1, column 1; element C23 is located at row 2, column 3. Note that picture C11 values are constant, referred to as dc coefficients. The changing values shown in picture C23 are known as ac coefficients. (From [2]. Used with permission.)

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