Summary.Although real-scale Semantic Web applications, such as Knowledge Portals and E-Marketplaces, require the management of voluminous resource metadata, sufficiently expressive declarative languages for metadata created according to the W3C RDF /S standard l are still missing. In answer to this need, we have designed a typed, functional query language, called RQL, whose novelty lies in its ability to smoothly combine schema and data querying. The purpose of this chapter is to present RQ L's formal data model and type system and illustrate its expressiveness by means of exemplary queries. RQL's formal foundations capture the RDF /S modeling primitives and provide a well-founded semantics for a declarative query language involving recursion and functional composition over complex description graphs.
IntroductionIn the next evolutionary step of the Web, termed the Semantic Web [18.5], vast amounts of information resourees (data, doeuments, programs, ete.) will be made available along with various kinds of deseriptive information, i.e.,
The ethical and societal implications of artificial intelligence systems raise concerns. In this paper we outline a novel process based on applied ethics, namely Z-inspection®, to assess if an AI system is trustworthy. We use the definition of trustworthy AI given by the high-level European Commission's expert group on AI. Z-inspection® is a general inspection process that can be applied to a variety of domains where AI systems are used, such as business, healthcare, public sector, among many others. To the best of our knowledge, Z-inspection® is the first process to assess trustworthy AI in practice.
In the first part of this chapter we illustrate how a big data project can be set up and optimized. We explain the general value of big data analytics for the enterprise and how value can be derived by analyzing big data. We go on to introduce the characteristics of big data projects and how such projects can be set up, optimized and managed. Two exemplary real word use cases of big data projects are described at the end of the first part. To be able to choose the optimal big data tools for given requirements, the relevant technologies for handling big data are outlined in the second part of this chapter. This part includes technologies such as NoSQL and NewSQL systems, in-memory databases, analytical platforms and Hadoop based solutions. Finally, the chapter is concluded with an overview over big data benchmarks that allow for performance optimization and evaluation of big data technologies. Especially with the new big data applications, there are requirements that make the platforms more complex and more heterogeneous. The relevant benchmarks designed for big data technologies are categorized in the last part
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