Transactions on large-scale data- and knowledge-centered systems L / Abdelkader Hameurlain, A Min Tjoa (eds.).
2021
QA76.9.A25
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Title
Transactions on large-scale data- and knowledge-centered systems L / Abdelkader Hameurlain, A Min Tjoa (eds.).
ISBN
9783662645536 (electronic bk.)
366264553X (electronic bk.)
9783662645529 (print)
3662645521
366264553X (electronic bk.)
9783662645529 (print)
3662645521
Published
Berlin, Germany : Springer, 2021.
Language
English
Description
1 online resource (vii, 117 pages) : illustrations (some color)
Item Number
10.1007/978-3-662-64553-6 doi
Call Number
QA76.9.A25
Dewey Decimal Classification
005.8
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 50th issue of Transactions on Large-Scale Data and Knowledge-Centered Systems, contains five fully revised selected regular papers. Topics covered include data anonymization, quasi-identifier discovery methods, symbolic time series representation, detection of anomalies in time series, data quality management in biobanks, and the use of multi-agent technology in the design of intelligent systems for maritime transport.
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Source of Description
Online resource; title from PDF title page (SpringerLink, viewed December 9, 2021).
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Series
Lecture notes in computer science ; 12930.
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Table of Contents
A Parallel Quasi-identifier Discovery Scheme for Dependable Data Anonymisation
Towards Symbolic Time Series Representation Improved by Kernel Density Estimators
Anomaly Detection in Time Series
Designing Intelligent Marine Framework Based on Complex Adaptive System Principle
Data Item Quality for Biobanks.
Towards Symbolic Time Series Representation Improved by Kernel Density Estimators
Anomaly Detection in Time Series
Designing Intelligent Marine Framework Based on Complex Adaptive System Principle
Data Item Quality for Biobanks.