By Barbara Catania, Lakhmi C. Jain
This learn ebook offers key advancements, instructions, and demanding situations relating complex question processing for either conventional and non-traditional information. a unique emphasis is dedicated to approximation and adaptivity concerns in addition to to the mixing of heterogeneous info sources.
The ebook will end up worthy as a reference booklet for senior undergraduate or graduate classes on complex facts administration matters, that have a unique specialise in question processing and knowledge integration. it really is aimed for technologists, managers, and builders who need to know extra approximately rising traits in complex question processing.
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Additional info for Advanced Query Processing: Volume 1: Issues and Trends
9 for a used car database. The provided precedence is “low mileage is more important than low price, which is more important than young age”, and 3 skyline objects are to be returned. In the first step, the algorithm starts at the root node of the full subspace which contains more than 3 objects. Thus, it navigates to the next lowest subspace with highest precedence , and adds both objects to the intermediate result. It continues to the next sibling subspace , which contains four objects (and thus too many to fit the result of required size ).
For actually computing a skyline set, there are multiple algorithms available which can be classified into Block Nested Loop algorithms, Divide-and-Conquer algorithms, and Multi-Scan Algorithms. Basically, each object has to be compared to each other one and tested for dominance. However, advanced algorithms try to avoid testing every object pair by employing optimizations and advanced techniques to eliminate as many objects as possible early in the computation process. Efficient skyline algorithms thus require only a fraction of the number of object dominance tests than less sophisticated algorithms.
The interestingness of a skyline object is propagated to all skyline object that dominate it in any subspace. This means, the interestingness of an object in the full space skyline increases the more other skyline objects it dominates in any of the subspaces. , were also dominating many original skyline objects in some subspaces). To compute this recursive concept of interestingness, SKYRANK relies on the notion of a skyline graph. The skyline graph contains all skyline objects of the full data space as nodes.