| Περίληψη: | 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 20th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, presents a representative and useful selection of articles covering a wide range of important topics in the domain of advanced techniques for big data management. Big data has become a popular term, used to describe the exponential growth and availability of data. The recent radical expansion and integration of computation, networking, digital devices, and data storage has provided a robust platform for the explosion in big data, as well as being the means by which big data are generated, processed, shared, and analyzed. In general, data are only useful if meaning and value can be extracted from them. Big data discovery enables data scientists and other analysts to uncover patterns and correlations through analysis of large volumes of data of diverse types. Insights gleaned from big data discovery can provide businesses with significant competitive advantages, leading to more successful marketing campaigns, decreased customer churn, and reduced loss from fraud. In practice, the growing demand for large-scale data processing and data analysis applications has spurred the development of novel solutions from both industry and academia.
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