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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. This, the 55th issue of Transactions on Large-Scale Data and Knowledge-Centered Systems, contains five fully revised regular papers covering a wide range of very hot topics in the fields of data driven science life science, workflows, weak signals, online social networks, root cause analysis, detected anomalies, analysis of interplanetary file systems, concept weighting in knowledge graphs, and neural networks.
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 29th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains four revised selected regular papers. Topics covered include optimization and cluster validation processes for entity matching, business intelligence systems, and data profiling in the Semantic Web.
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.
Data management, knowledge discovery, and knowledge processing are core and hot topics in computer science. They are widely accepted as enabling technologies for modern enterprises, enhancing their performance and their decision making processes. Since the 1990s the Internet has been the outstanding driving force for application development in all domains. An increase in the demand for resource sharing (e. g. , computing resources, s- vices, metadata, data sources) across different sites connected through networks has led to an evolvement of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications prov- ing 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 dynamicity of nodes These characteristics recognize: (i) limitations of methods and techniques developed for centralized systems (ii) requirements to extend or design new approaches and methods enhancing efficiency, dynamicity, and scalability (iii) 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 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 eighth issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains eight revised selected regular papers focusing on the following topics: scalable data warehousing via MapReduce, extended OLAP multidimensional models, naive OLAP engines and their optimization, advanced data stream processing and mining, semi-supervised learning of data streams, incremental pattern mining over data streams, association rule mining over data streams, frequent pattern discovery over data streams.
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 seventh issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains five revised selected regular papers on the following topics: data management, data streams, service-oriented computing, abstract algebraic frameworks, RDF and ontologies, and conceptual model frameworks.
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 special issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems highlights some of the major challenges emerging from the biomedical applications that are currently inspiring and promoting database research. These include the management, organization, and integration of massive amounts of heterogeneous data; the semantic gap between high-level research questions and low-level data; and privacy and efficiency. The contributions cover a large variety of biological and medical applications, including genome-wide association studies, epidemic research, and neuroscience.
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 (e.g., computing resources, services, metadata, data sources) 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. This, the 43rd issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains five revised selected regular papers. Topics covered include classification tasks, machine learning algorithms, top-k queries, business process redesign and a knowledge capitalization framework.
This, the 24th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains extended and revised versions of seven papers presented at the 25th International Conference on Database and Expert Systems Applications, DEXA 2014, held in Munich, Germany, in September 2014. Following the conference, and two further rounds of reviewing and selection, six extended papers and one invited keynote paper were chosen for inclusion in this special issue. Topics covered include systems modeling, similarity search, bioinformatics, data pricing, k-nearest neighbor querying, database replication, and data anonymization.
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.