By Sven Groppe

The Semantic internet, that is meant to set up a machine-understandable internet, is presently altering from being an rising pattern to a expertise utilized in complicated real-world functions. a couple of criteria and strategies were built via the realm extensive net Consortium (W3C), e.g., the source Description Framework (RDF), which supplies a common approach for conceptual descriptions for internet assets, and SPARQL, an RDF querying language. contemporary examples of enormous RDF info with billions of proof contain the UniProt entire catalog of protein series, functionality and annotation facts, the RDF information extracted from Wikipedia, and Princeton University’s WordNet. sincerely, querying functionality has develop into a key factor for Semantic net applications.

In his publication, Groppe info a number of facets of high-performance Semantic internet facts administration and question processing. His presentation fills the space among Semantic internet and database books, which both fail take into consideration the functionality problems with large-scale facts administration or fail to take advantage of the detailed homes of Semantic internet info versions and queries. After a common creation to the correct Semantic internet criteria, he provides really good indexing and sorting algorithms, tailored techniques for logical and actual question optimization, optimization chances while utilizing the parallel database applied sciences of today’s multicore processors, and visible and embedded question languages.

Groppe essentially goals researchers, scholars, and builders of large-scale Semantic internet purposes. at the complementary publication website readers will locate extra fabric, reminiscent of a web demonstration of a question engine, and workouts, and their recommendations, that problem their comprehension of the subjects presented.

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Extra info for Data Management and Query Processing in Semantic Web Databases

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2 B+-trees B+-trees are the most widely used database indices (see Fig. 1 for an example of a B+-tree). The B+-tree is a self-balancing block-oriented search tree. We can store keys and their values in a B+-tree and a value can be efficiently retrieved using its key. The nodes in trees without children are called leaves and all other nodes are called interior nodes. 2 B+-trees 37 9 3 keys 1 2 3 4 6 5 12 6 7 8 9 10 11 12 13 14 15 15 16 17 18 values Fig. 1 Example of a B+-tree values), are stored in the leaves and the interior nodes contain only keys, such that interior nodes can hold more keys to decrease the height of the overall search tree.

However, now not the full keys are compared, but just their prefixes with the prefix key (see 1. of Fig. 3). Typically the result of a prefix search is returned as iterator, where the single results are returned one by one after calling a next() method of the iterator. For the next result, the iterator reads the next key-value pair (k, v) of the current leaf and returns v if k conforms to the prefix key (see 2. of Fig. 3). lt su t re firs , 2) r o (1 hf key earc 1. S r prefix fo keys (1,2,2) (1,2,5) (1,2,8) (b) 4.

The language for RIF rules is standardized by the W3C, the world’s leading standardization committee for the Web. RIF rules – in comparison to prolog and datalog rules, are specialized for the usage in the Semantic Web. This opens new possibilities and additional advantages for Semantic Web applications, for example, more interchangeability, more concisely processing by additionally considering the semantics based on ontologies, and a widespread support of further Semantic Web technologies. Logic-based dialects cover languages that apply some kind of logic, such as firstorder logic (often restricted to Horn logic) or non-first-order logics, which underlay the various logic programming languages like logic programming using the wellfounded (Gelder et al.

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