
Transactions on Large-Scale Data- and Knowledge-Centered Systems LII
Springer (Publisher)
Published on 29. September 2022
Book
Paperback/Softback
IX, 149 pages
978-3-662-66145-1 (ISBN)
Description
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 6 fully revised selected regular papers.
More details
Series
Edition
1st ed. 2022
Language
English
Place of publication
Berlin
Germany
Publishing group
Springer Berlin
Target group
Professional and scholarly
Illustrations
9 s/w Abbildungen, 50 farbige Abbildungen
IX, 149 p. 59 illus., 50 illus. in color.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 9 mm
Weight
254 gr
ISBN-13
978-3-662-66145-1 (9783662661451)
DOI
10.1007/978-3-662-66146-8
Schweitzer Classification
Other editions
Additional editions

Abdelkader Hameurlain | A. Min Tjoa
Transactions on Large-Scale Data- and Knowledge-Centered Systems LII
E-Book
09/2022
Springer
€53.49
Available for download
Content
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.