Web and Semantic Web Query Languages

A number of techniques have been developed to facilitate powerful data retrieval on the Web and Semantic Web. Three categories of Web query languages can be distinguished, according to the format of the data they can retrieve: XML, RDF and Topic Maps. This article introduces the spectrum of languages falling into these categories and summarises their salient aspects. The languages are introduced using common sample data and query types. Key aspects of the query languages considered are stressed in a conclusion

Reasoning & Querying – State of the Art

Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF

Big Data Management Challenges, Approaches, Tools and their limitations

International audienceBig Data is the buzzword everyone talks about. Independently of the application domain, today there is a consensus about the V's characterizing Big Data: Volume, Variety, and Velocity. By focusing on Data Management issues and past experiences in the area of databases systems, this chapter examines the main challenges involved in the three V's of Big Data. Then it reviews the main characteristics of existing solutions for addressing each of the V's (e.g., NoSQL, parallel RDBMS, stream data management systems and complex event processing systems). Finally, it provides a classification of different functions offered by NewSQL systems and discusses their benefits and limitations for processing Big Data

TopX : efficient and versatile top-k query processing for text, structured, and semistructured data

TopX is a top-k retrieval engine for text and XML data. Unlike Boolean engines, it stops query processing as soon as it can safely determine the k top-ranked result objects according to a monotonous score aggregation function with respect to a multidimensional query. The main contributions of the thesis unfold into four main points, confirmed by previous publications at international conferences or workshops: • Top-k query processing with probabilistic guarantees. • Index-access optimized top-k query processing. • Dynamic and self-tuning, incremental query expansion for top-k query processing. • Efficient support for ranked XML retrieval and full-text search. Our experiments demonstrate the viability and improved efficiency of our approach compared to existing related work for a broad variety of retrieval scenarios.TopX ist eine Top-k Suchmaschine für Text und XML Daten. Im Gegensatz zu Boole\u27; schen Suchmaschinen terminiert TopX die Anfragebearbeitung, sobald die k besten Ergebnisobjekte im Hinblick auf eine mehrdimensionale Anfrage gefunden wurden. Die Hauptbeiträge dieser Arbeit teilen sich in vier Schwerpunkte basierend auf vorherigen Veröffentlichungen bei internationalen Konferenzen oder Workshops: • Top-k Anfragebearbeitung mit probabilistischen Garantien. • Zugriffsoptimierte Top-k Anfragebearbeitung. • Dynamische und selbstoptimierende, inkrementelle Anfrageexpansion für Top-k Anfragebearbeitung. • Effiziente Unterstützung für XML-Anfragen und Volltextsuche. Unsere Experimente bestätigen die Vielseitigkeit und gesteigerte Effizienz unserer Verfahren gegenüber existierenden, führenden Ansätzen für eine weite Bandbreite von Anwendungen in der Informationssuche