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Transactions on Large-Scale Data- and Knowledge-Centered Systems LII [electronic resource] / edited by Abdelkader Hameurlain, A Min Tjoa.

Contributor(s): Material type: TextTextSeries: Transactions on Large-Scale Data- and Knowledge-Centered Systems ; 13470Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2022Edition: 1st ed. 2022Description: IX, 149 p. 59 illus., 50 illus. in color. online resourceISBN:
  • 9783662661468
Subject(s): DDC classification:
  • 005.3 23
Online resources:
Contents:
Mutida: A Rights Management Protocol for Distributed Storage Systems Without Fully Trusted Nodes -- OpenCEMS: An Open Solution for Easy Data Management in Connected Environments -- Knowledge Graph Augmentation for Increased Question Answering Accuracy -- Online Optimized Product Quantization for ANN Queries over Dynamic Database using SVD-Updating -- Empirical Study of the Model Generalization for Argument Mining in Cross-domain and Cross-topic Settings -- A Pattern Mining Framework for Improving Billboard Advertising Revenue.
Summary: 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 52nd issue of Transactions on Large-scale Data and Knowledge-centered Systems, contains six fully revised selected regular papers. Topics covered include management of decryption keys, delegations as a rights management system, data analytics in connected environments, knowledge graph augmentation, online optimized product quantization for All-Nearest-Neighbours queries, generalization of argument models and transactional modelling using coverage pattern mining.
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Holdings
Item type Current library Call number Materials specified Status Date due Barcode Item holds
E-Books E-Books National Library of India Online Resource 005.3 (Browse shelf(Opens below)) Available EBK000033457ENG
Total holds: 0

Mutida: A Rights Management Protocol for Distributed Storage Systems Without Fully Trusted Nodes -- OpenCEMS: An Open Solution for Easy Data Management in Connected Environments -- Knowledge Graph Augmentation for Increased Question Answering Accuracy -- Online Optimized Product Quantization for ANN Queries over Dynamic Database using SVD-Updating -- Empirical Study of the Model Generalization for Argument Mining in Cross-domain and Cross-topic Settings -- A Pattern Mining Framework for Improving Billboard Advertising Revenue.

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 52nd issue of Transactions on Large-scale Data and Knowledge-centered Systems, contains six fully revised selected regular papers. Topics covered include management of decryption keys, delegations as a rights management system, data analytics in connected environments, knowledge graph augmentation, online optimized product quantization for All-Nearest-Neighbours queries, generalization of argument models and transactional modelling using coverage pattern mining.

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