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 49th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains four fully revised and extended papers selected from the 36th conference on Data Management - Principles, Technologies and Applications, BDA 2020. The topics cover a wide range of timely data management research topics on crowdsourcing, data streams, skyline queries and data protection.
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 49th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains four fully revised and extended papers selected from the 36th conference on Data Management - Principles, Technologies and Applications, BDA 2020. The topics cover a wide range of timely data management research topics on crowdsourcing, data streams, skyline queries and data protection.
Temporal Aggregation of Spanning Event Stream: An Extended Framework to Handle the Many Stream Models.- Reducing the Cost of Aggregation in Crowdsourcing.- A Partitioning Approach for Skyline Queries in Presence of Partial and Dynamic Orders.- Ensuring License Compliance in Linked Data with Query Relaxation.
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