Transactions on Large-Scale Data- and Knowledge-Centered Systems LI (eBook, PDF)

Special Issue on Data Management - Principles, Technologies and Applications
Redaktion: Hameurlain, Abdelkader; Miklos, Zoltan; Pacitti, Esther; Tjoa, A Min

• Format: PDF

Produktbeschreibung

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 51st issue of Transactions on Large-Scale Data and Knowledge-Centered Systems, contains five fully revised selected regular papers. Topics covered include data anonyomaly detection, schema generation, optimizing data coverage, and digital preservationwith synthetic DNA.

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Produktdetails

• Verlag: Springer Berlin Heidelberg
• Seitenzahl: 137
• Erscheinungstermin: 7. Oktober 2022
• Englisch
• ISBN-13: 9783662661116
• Artikelnr.: 66009241

Inhaltsangabe

Threats Modeling And Anomaly Detection In The Behaviour Of A System - A Review Of Some Approaches.- Incremental Schema Generation for Large and Evolving RDF Sources.- Optimizing Data Coverage and Significance in Multiple Hypothesis Testing on User Groups.- Efficiently identifying disguised missing values in heterogeneous, text-rich data.- Digital Preservation with Synthetic DNA.

Rezensionen

The volume under review contains five papers from the 37th Conference on Data Management Principle, Technologies and Applications (BDA 2021), covering a range of research topics in data management, threat modeling, resource description framework (RDF) schema generation, data coverage optimization, data quality, storage, and data science. ... This collection is certainly useful for researchers in big data processing and cybersecurity. (Anoop Malaviya, Computing Reviews, November 28, 2025)