Download Advanced Query Processing: Volume 1: Issues and Trends by Barbara Catania, Lakhmi C. Jain PDF

By Barbara Catania, Lakhmi C. Jain

This study publication offers key advancements, instructions, and demanding situations pertaining to complex question processing for either conventional and non-traditional facts. a distinct emphasis is dedicated to approximation and adaptivity concerns in addition to to the mixing of heterogeneous facts sources.

The e-book will turn out priceless as a reference ebook for senior undergraduate or graduate classes on complex facts administration concerns, that have a different concentrate on question processing and information integration. it really is aimed for technologists, managers, and builders who need to know extra approximately rising tendencies in complicated question processing.

Show description

Read Online or Download Advanced Query Processing: Volume 1: Issues and Trends PDF

Similar data mining books

Advances in Knowledge Discovery and Data Mining, Part II: 14th Pacific-Asia Conference, PAKDD 2010, Hyderabad, India, June 21-24, 2010, Proceedings

This ebook constitutes the lawsuits of the 14th Pacific-Asia convention, PAKDD 2010, held in Hyderabad, India, in June 2010.

Computational Discovery of Scientific Knowledge: Introduction, Techniques, and Applications in Environmental and Life Sciences

Advances in expertise have enabled the gathering of knowledge from clinical observations, simulations, and experiments at an ever-increasing velocity. For the scientist and engineer to learn from those stronger info amassing features, it truly is turning into transparent that semi-automated facts research concepts has to be utilized to discover the necessary info within the facts.

Metalearning: Applications to Data Mining

Metalearning is the research of principled equipment that make the most metaknowledge to procure effective versions and options through adapting laptop studying and information mining tactics. whereas the diversity of laptop studying and knowledge mining strategies now to be had can, in precept, offer stable version ideas, a technique remains to be had to advisor the quest for the main applicable version in a good approach.

Data-Driven Technology for Engineering Systems Health Management: Design Approach, Feature Construction, Fault Diagnosis, Prognosis, Fusion and Decisions

This booklet introduces condition-based upkeep (CBM)/data-driven prognostics and well-being administration (PHM) intimately, first explaining the PHM layout strategy from a structures engineering viewpoint, then summarizing and elaborating at the data-driven technique for characteristic building, in addition to feature-based fault analysis and diagnosis.

Extra resources for Advanced Query Processing: Volume 1: Issues and Trends

Sample text

The techniques for selecting from skyline sets can be classified in four groups: a) strategies using less strict variations of the Pareto dominance criterion b) approaches aiming at a diverse summarization of the skyline set c) approaches which use additional characteristics of skyline objects or subspace skylines to derive a ranking and selection for the original skyline d) cooperative approaches which elicit additional preference information interactively from the user. However, please note that each of these presented approaches in some way break the absolute fairness of Pareto semantics and replace them by different heuristics for capturing the notion of a skyline object being “more interesting” than others.

It has been suggested that Skyline groups can be used to summarize the full skyline by selecting some common representatives from the skyline groups of the skycube. Fig. 7 Skycube Lattice of a car database on attributes , , and skyline objects , , , , Top-1 Frequent Skyline: Top-3 Frequent Skyline: , , {mileage, price, age} {a, b, c, d, e} {mileage, price} {b, c} {mileage, age} {a, b, c, d} {mileage} {price} {b} {c} {price, age} {a, b} {age} {a} In [30], it is suggested that a metric called skyline frequency can be used to rank and select skyline objects by their interestingness.

Instead, users are only required to specify the attributes they want to minimize (or maximize). The following query illustrates the skyline query counterpart for Q1. SELECT * FROM Hotel SKYLINE OF price MIN, distance MIN; (Q2) Given these attributes, skyline query algorithms retrieve a set of “desirable” objects, for any monotonic function in general. , m is dominated by b. In other words, in any monotonic ranking function, m is less preferred than b. , {b, e, i, l} in the figure. These hotels are guaranteed to contain the top hotel for any monotonic ranking function, without requiring users to specify the exact ranking function.

Download PDF sample

Rated 4.93 of 5 – based on 6 votes