Central Library

Normal view MARC view ISBD view

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

Contributor(s): Hameurlain, Abdelkader [editor.] | Tjoa, A Min [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Transactions on Large-Scale Data- and Knowledge-Centered Systems: 12930Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2021Edition: 1st ed. 2021.Description: VII, 117 p. 32 illus., 26 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783662645536.Subject(s): Application software | Software engineering | Data structures (Computer science) | Information theory | Data mining | Computer and Information Systems Applications | Software Engineering | Data Structures and Information Theory | Data Mining and Knowledge DiscoveryAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 005.3 Online resources: Click here to access online
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.
In: Springer Nature eBookSummary: 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.
    average rating: 0.0 (0 votes)
No physical items for this record

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.

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.

There are no comments for this item.

Log in to your account to post a comment.