Hameurlain / Küng / Wagner Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXIII

1. Auflage 2017
ISBN: 978-3-662-55696-2
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 185 Seiten, eBook

Reihe: Transactions on Large-Scale Data- and Knowledge-Centered Systems

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 33rd issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains five revised selected regular papers. Topics covered include distributed massive data streams, storage systems, scientific workflow scheduling, cost optimization of data flows, and fusion strategies.

Zielgruppe

Research

Autoren/Hrsg.

Hameurlain, Abdelkader

Küng, Josef

Wagner, Roland

Akbarinia, Reza

Pacitti, Esther

Inhaltsverzeichnis

Lightweight Metric Computation for Distributed Massive Data Streams.- Performance Analysis of Object Store Systems in a Fog and Edge Computing Infrastructure.- Scientific Workflow Scheduling with Provenance Data in a Multisite Cloud.- Cost Optimization of Data Flows Based on Task Re-ordering.- Fusion Strategies for Large-Scale Multi-Modal Image Retrieval.