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Transactions on Large-Scale Data- and Knowledge-Centered Systems XIX

Special Issue on Big Data and Open Data

  • Abdelkader Hameurlain
  • Josef Küng
  • Roland Wagner
  • Devis Bianchini
  • Valeria De Antonellis
  • Roberto De Virgilio

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8990)

Also part of the Transactions on Large-Scale Data- and Knowledge-Centered Systems book sub series (TLDKS, volume 8990)

Table of contents

  1. Front Matter
    Pages I-IX
  2. Klitos Christodoulou, Norman W. Paton, Alvaro A. A. Fernandes
    Pages 1-25
  3. Alfio Ferrara, Lorenzo Genta, Stefano Montanelli, Silvana Castano
    Pages 55-86
  4. Stefan Wild, Fabian Wiedemann, Sebastian Heil, Alexey Tschudnowsky, Martin Gaedke
    Pages 87-127
  5. Back Matter
    Pages 129-129

About this book

Introduction

The LNCS journal Transactions on Large-Scale Data- and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. Current decentralized systems still focus on data and knowledge as their main resource. Feasibility of these systems relies basically on P2P (peer-to-peer) techniques and the support of agent systems with scaling and decentralized control. Synergy between grids, P2P systems, and agent technologies is the key to data- and knowledge-centered systems in large-scale environments. This, the 19th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains four high-quality papers investigating the areas of linked data and big data from a data management perspective. Two of the four papers focus on the application of clustering techniques in performing inference and search over (linked) data sources. One paper leverages graph analysis techniques to enable application-level integration of institutional data and a final paper describes an approach for protecting users' profile data from disclosure, tampering, and improper use.

Keywords

Big Data Open Data Semantic Web Social Web applications authentication clustering complex data data integration data management graph query languages integrity linked data linked data sources modeling privacy protection querying reasoning security

Editors and affiliations

  • Abdelkader Hameurlain
    • 1
  • Josef Küng
    • 2
  • Roland Wagner
    • 3
  • Devis Bianchini
    • 4
  • Valeria De Antonellis
    • 5
  • Roberto De Virgilio
    • 6
  1. 1.IRIT, Paul Sabatier UniversityToulouseFrance
  2. 2.FAW, University of LinzLinzAustria
  3. 3.FAW, University of LinzLinzAustria
  4. 4.University of BresciaBresciaItaly
  5. 5.University of BresciaBresciaItaly
  6. 6.University of Rome IIIRomeItaly

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-662-46562-2
  • Copyright Information Springer-Verlag Berlin Heidelberg 2015
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-662-46561-5
  • Online ISBN 978-3-662-46562-2
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site
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