The massive semantic data sources linked in the Web of Data give new meaning to old features like navigation; introduce new challenges like semantic specification of Web fragments; and make it possible to specify actions relying on semantic data. In this paper we introduce a declarative language to face these challenges. Based on navigational features, it is designed to specify fragments of the Web of Data and actions to be performed based on these data. We implement it in a centralized fashion, and show its power and performance. Finally, we explore the same ideas in a distributed setting, showing their feasibility, potentialities and challenges.
Distributed data management has been explored and implemented by P2P and similar approaches @cite_14 . For RDF, RDFPeers @cite_16 and YARS2 uses P2P to answer RDF queries. Systems for distributed query processing on the Web have also been devised, e.g. DIASPORA @cite_18 . Our distributed version of borrows some ideas from these approaches.
@cite_18 @cite_14 @cite_16
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During the last decades, data management and storage have become increasingly distributed. Advanced query operators, such as skyline queries, are necessary in order to help users to handle the huge amount of available data by identifying a set of interesting data objects. Skyline query processing in highly distributed environments poses inherent challenges and demands and requires non-traditional techniques due to the distribution of content and the lack of global knowledge. This paper surveys this interesting and still evolving research area, so that readers can easily obtain an overview of the state-of-the-art. We outline the objectives and the main principles that any distributed skyline approach has to fulfill, leading to useful guidelines for developing algorithms for distributed skyline processing. We review in detail existing approaches that are applicable for highly distributed environments, clarify the assumptions of each approach, and provide a comparative performance analysis. Moreover, we study the skyline variants each approach supports. Our analysis leads to a taxonomy of existing approaches. Finally, we present interesting research topics on distributed skyline computation that have not yet been explored. The inherent flexibility of the RDF data model has led to its notable adoption in many domains, especially in the area of life-sciences. Some of these domains have an emerging need to access data integrated from various distributed sources of information. It is not always possible to implement this by simply loading all data into one central RDF store. For example, in the context of inter-institutional collaboration for drug development and clinical research participants often want to maintain control over their local databases. Alternatively, distributed query processing techniques can be utilized to evaluate queries by accessing the remote data sources only on demand and in conformance with local authorization models. In this paper we present an efficient approach to distributed query processing for large autonomous RDF databases. The groundwork is laid by a comprehensive RDF-specific schema- and instance-level synopsis. We present an optimizer that is able to utilize this synopsis to generate compact execution plans by precisely determining, at compile-time, those sources that are relevant to a query. Furthermore we present a tightly integrated query engine that is able to further reduce the volume of intermediate results at run-time. An extensive evaluation shows that our approach improves query execution times by up to two and transferred data volumes by up to three orders of magnitude compared to a naive implementation. We study the problem of distributed RDFS reasoning and query answering on top of distributed hash tables. Scalable, distributed RDFS reasoning is an essential functionality for providing the scalability and performance that large-scale Semantic Web applications require. Our goal in this paper is to compare and evaluate two well-known approaches to RDFS reasoning, namely backward and forward chaining, on top of distributed hash tables. We show how to implement both algorithms on top of the distributed hash table Bamboo and prove their correctness. We also study the time-space trade-off exhibited by the algorithms analytically, and experimentally by evaluating our algorithms on PlanetLab. This third edition of a classic textbook can be used to teach at the senior undergraduate and graduate levels. The material concentrates on fundamental theories as well as techniques and algorithms. The advent of the Internet and the World Wide Web, and, more recently, the emergence of cloud computing and streaming data applications, has forced a renewal of interest in distributed and parallel data management, while, at the same time, requiring a rethinking of some of the traditional techniques. This book covers the breadth and depth of this re-emerging field. The coverage consists of two parts. The first part discusses the fundamental principles of distributed data management and includes distribution design, data integration, distributed query processing and optimization, distributed transaction management, and replication. The second part focuses on more advanced topics and includes discussion of parallel database systems, distributed object management, peer-to-peer data management, web data management, data stream systems, and cloud computing. New in this Edition: New chapters, covering database replication, database integration, multidatabase query processing, peer-to-peer data management, and web data management. Coverage of emerging topics such as data streams and cloud computing Extensive revisions and updates based on years of class testing and feedback Ancillary teaching materials are available.