By Barbara Catania, Lakhmi C. Jain
This learn publication 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 combination of heterogeneous facts sources.
The e-book will turn out important as a reference ebook for senior undergraduate or graduate classes on complicated information administration matters, that have a unique specialise in question processing and information integration. it's aimed for technologists, managers, and builders who need to know extra approximately rising traits in complex question processing.
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Extra resources for Advanced Query Processing: Volume 1: Issues and Trends
Example text
Better in all dimensions) dominates . 5). ε ε Attribute1 better ADR is designed as a subsequent step after the actual skyline computation, thus no performance advantages can be gained as still the full skyline needs to be computed. Furthermore it has been shown that although finding the smallest possible ADR (assuming the skyline is given) is in linear time complexity in the number of skyline elements for two attribute dimensions, the problem is unfortunately NP hard for more than two dimensions.
A skyline query on four attributes , , , is sampled by a union of the randomly selected subspace skylines with 2 dimensions , , , , and , . , considering a skyline of a car database, the sample could contain some fuel efficient cars, some cheap cars, some fast cars, and some luxurious cars. Furthermore, the sample contains only real skyline objects and can be computed significantly faster than the full skyline. 5 Weighting Characteristics of Skyline Points Approaches presented in this section also aim at generating a sample of the skyline.
While this allowed for very efficient query evaluation, the preferences’ expressiveness was rather limited [20]. But this drawback was quickly remedied by [21] and [22], which both helped to popularize the use of partial order preferences. , when considering two cars, one with (75 HP, 5 Liter / 100km) and one with (120 HP, 9 Liter / 100km), these two cars would be incomparable when using the default preferences “more HP is better” and “lower fuel consumption is better” as none of the two objects is clearly better than the other.