Storage Retrieval

Download Data Management and Query Processing in Semantic Web by Sven Groppe PDF

By Sven Groppe

The Semantic internet, that's meant to set up a machine-understandable net, is at the moment altering from being an rising pattern to a expertise utilized in advanced real-world functions. a few criteria and strategies were constructed through the area broad net Consortium (W3C), e.g., the source Description Framework (RDF), which supplies a basic procedure for conceptual descriptions for internet assets, and SPARQL, an RDF querying language. contemporary examples of enormous RDF info with billions of proof comprise the UniProt finished catalog of protein series, functionality and annotation info, the RDF information extracted from Wikipedia, and Princeton University’s WordNet. basically, querying functionality has develop into a key factor for Semantic internet applications.

In his e-book, Groppe info a variety of points of high-performance Semantic internet facts administration and question processing. His presentation fills the distance among Semantic internet and database books, which both fail take into consideration the functionality problems with large-scale info administration or fail to use the exact homes of Semantic internet facts versions and queries. After a common advent to the suitable Semantic internet criteria, he provides really good indexing and sorting algorithms, tailored techniques for logical and actual question optimization, optimization probabilities while utilizing the parallel database applied sciences of today’s multicore processors, and visible and embedded question languages.

Groppe basically ambitions researchers, scholars, and builders of large-scale Semantic net purposes. at the complementary e-book website readers will locate extra fabric, reminiscent of a web demonstration of a question engine, and workouts, and their suggestions, that problem their comprehension of the subjects presented.

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Each solution is contained in one tag. A variable binding of a variable n is contained in a tag. The bound values can be encoded in an tag in the case of an URI, in a tag in the case of a literal, or in a tag in the case of a blank node. The tag can have no attribute for simple literals, the attribute datatype¼“D” for a typed literal with data type D, or the attribute xml:lang¼“L” for a language-tagged literal with language L. 0"? org / 2005 / sparql-results#"> < / head> http:// book / instances / book1< / uri>< / binding> Fritz< / literal>< / binding> < / result> http:// book / instances / book1< / uri>< / binding> Egon< / literal>< / binding> < / result> < / results> < / sparql> Fig.

For example, the effective Boolean formula of a plain literal or a typed literal with a data type of xsd:string is false if the result has zero length; otherwise the effective Boolean value is true. The effective Boolean value of a numeric type or a typed literal with a data type derived from a numeric type is false if the operand value is NaN (alias for not a number) or is numerically equal to zero; otherwise the effective Boolean value is true. author¼“Fritz”) between lines (4) and (5) in Fig.

An incremental update to databases will be impractical for insertion of large datasets; instead, indices need to be constructed from scratch. This also applies to many other situations, for example, whenever databases need to be set up from previously made dumps, because of, for example, reconfigurations of the underlying hardware. Therefore, developing efficient techniques to speed up the index construction for large datasets is obviously an important and urgent task for modern databases. The most widely used index type in databases is the B+-tree, which is a variant of the B-tree storing only keys in the interior nodes and all records, that is, the keys and their values, in leaves.

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