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

  • Abdelkader Hameurlain
  • Josef Küng
  • Roland Wagner
  • Sherif Sakr
  • Imran Razzak
  • Alshammari Riyad

Part of the Lecture Notes in Computer Science book series (LNCS, volume 10680)

Also part of the Transactions on Large-Scale Data- and Knowledge-Centered Systems book sub series (TLDKS, volume 10680)

Table of contents

  1. Front Matter
    Pages I-IX
  2. Jaak Tepandi, Mihkel Lauk, Janar Linros, Priit Raspel, Gunnar Piho, Ingrid Pappel et al.
    Pages 1-26
  3. Zakaria Maamar, Vanilson Burégio, Mohamed Sellami, Nelson Souto Rosa, Zhengshuai Peng, Zachariah Subin et al.
    Pages 27-49
  4. Feras Aljumah, Makan Pourzandi, Mourad Debbabi
    Pages 50-73
  5. Fawaz Alharbi, Anthony Atkins, Clare Stanier
    Pages 96-131
  6. Back Matter
    Pages 133-133

About this book


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 volume, the 35th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains five fully-revised selected regular papers focusing on data quality, social-data artifacts, data privacy, predictive models, and e-health. Specifically, the five papers present and discuss a data-quality framework for the Estonian public sector; a data-driven approach to bridging the gap between the business and social worlds; privacy-preserving querying on privately encrypted data in the cloud; algorithms for the prediction of norovirus concentration in drinking water; and cloud computing in healthcare organizations in Saudi Arabia.


information systems privacy preserving differential privacies cryptography privacy business process business process management computer systems cloud computing fuzzy sets data mining anfis fuzzy inference quality models information management data management data security data privacy

Editors and affiliations

  • Abdelkader Hameurlain
    • 1
  • Josef Küng
    • 2
  • Roland Wagner
    • 3
  • Sherif Sakr
    • 4
  • Imran Razzak
    • 5
  • Alshammari Riyad
    • 6
  1. 1.IRIT, Paul Sabatier UniversityToulouseFrance
  2. 2.FAW, University of LinzLinzAustria
  3. 3.FAW, University of LinzLinzAustria
  4. 4.King Saud bin Abdulaziz UniversityRiyadhSaudi Arabia
  5. 5.King Saud bin Abdulaziz UniversityRiyadhSaudi Arabia
  6. 6.King Saud bin Abdulaziz UniversityRiyadhSaudi Arabia

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag GmbH Germany 2017
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science Computer Science (R0)
  • Print ISBN 978-3-662-56120-1
  • Online ISBN 978-3-662-56121-8
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site