Motivation


Data management, knowledge discovery as well as knowledge processing are core and hot topics in computer science. They are widely accepted as enabling technologies for modern enterprises enhancing their performance, especially in the decision making process. Since the 1990s the Internet has been the outstanding driving force for application development in all domains.

An increase in the demand of resource sharing (e.g. computing resources, services, metadata, data sources) across different sites connected through networks leads to an evolvement 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 characterized by:

  • heterogeneity of nodes, data and knowledge

  • autonomy of data and knowledge sources and services

  • large scale data volumes, high numbers of data sources, users, computing resources

  • dynamics of nodes

Considering these characteristics recognize:

  • limitations of methods and techniques developed for centralized systems

  • requirements to extend or design new approaches and methods enhancing efficiency, dynamics and scalability

  • development of large scale, experimental platforms and relevant benchmarks to evaluate and validate scaling

Feasibility of these systems relies basically on P2P (peer-to-peer) techniques and agent systems supporting 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.

The objective of the international journal on Large Scale Data and Knowledge Centered Systems is to provide an opportunity to disseminate original research contributions and a high quality communication platform for researchers and practitioners. The journal contains sound peer reviewed papers (research, state of the art and technical) of high quality.