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

Redaktion: Hameurlain, Abdelkader; 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 53rd issue of Transactions on Large-Scale Data and Knowledge-Centered Systems, contains six fully revised selected regular papers. Topics covered include time series management from edge to cloud, segmentation for time series representation, similarity research, semantic similarity in a taxonomy, linked data semantic distance, linguistics-informed natural language processing, graph neural networks, protected features, imbalanced data, causal consistency in distributed databases, actor models, and elastic horizontal scalability.

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Produktdetails

• Verlag: Springer Berlin Heidelberg
• Seitenzahl: 167
• Erscheinungstermin: 8. Februar 2023
• Englisch
• ISBN-13: 9783662668634
• Artikelnr.: 67425430

Inhaltsangabe

ModelarDB: Integrated Model-Based Management of Time Series from Edge to Cloud.- Variable-Size Segmentation for Time Series Representation.- Semantic Similarity in a Taxonomy by Evaluating the Relatedness of Concept Senses with the Linked Data Semantic Distance.- Constituency-informed and Constituency-constrained Extractive Question Answering with Heterogeneous Graph Transformer.- FAPFID: A Fairness-aware Approach for Protected Features and Imbalanced Data.- D-Thespis: A Distributed Actor-Based Causally Consistent DBMS.