New PDF release: Database: Principles Programming and Performance

By Patrick O'Neil

The main finished creation to database programming and function on hand.

a whole and functional advent to database administration structures for a person who's, or needs to develop into, a database administrator, software programmer, or subtle SQL consumer. Patrick O'Neil's pragmatic method of programming and function inside of advertisement settings teaches you to create bug-free functions with the main effective use of assets.

beginning with primary relational options, the writer offers a complete diversity of knowledge approximately database criteria and items, together with Oracle, Ingres, DB2 and Sybase, and gives certain, step- by-step insurance of SQL and database transaction programming.

extra informative than common listings of embedded SQL positive factors, O'Neil's presentation of embedded SQL * together with dynamic SQL) and forms-based software programming is so distinctive that it permits you to jot down your individual courses in C. various routines (with strategies) are supplied to augment the abilities had to produce operational queries and courses. you are going to additionally examine a few obstacles of interactive SQL and the way to surmount those boundaries in courses. Logical database layout can also be defined intensive and utilized to the dialogue of integrity constraints.

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Extra resources for Database: Principles Programming and Performance

Sample text

Hence shape matching and retrieval techniques should handle images containing partially visible, overlapping, or touching objects. • Similar shapes should have similar measures. , perceptually similar shapes should have high similarity measures. 1. MBC Features We first describe a set of six features that could be extracted from the objects M BGs. 2. Polygons we will be using them for both indexing the objects and expediting the comparison between two objects. The six M BC features can be identified for each simple polygon object.

As a consequence, a small change in the coordinates of the objects' boundary points may lead to a very different shape signature, hence, very poor retrieval performance. On the other hand, Fourier transformation captures the general feature of a shape by converting the sensitive direct representation measures into frequency domain. As a result, the data is more robust to small changes and noise. Therefore, Fourier transformation is used as shape representation instead of the direct representation (see [50] for further details).

Although T PF AS method uses simple attributes such as minimum bounding circles and angle sequences, these attributes were shown to be translation, rotation, and scale invariant in [84]. Therefore, the similarity measure is independent of object's position, scale, and rotation (Le. objects does not have to be normalized). 6. MBC-TPVAS method The difference between two objects is the Euclidean distance between their corresponding feature vectors. 8 is that two objects are similar in shape, if and only if the Euclidean distance between their feature vectors is less than a prespecified threshold.

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