Buch, Englisch, Band 14790, 153 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 260 g
Special Issue on Data Management - Principles, Technologies, and Applications
Buch, Englisch, Band 14790, 153 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 260 g
Reihe: Lecture Notes in Computer Science
ISBN: 978-3-662-69602-6
Verlag: Springer
The LNCS journal Transactions on Large-scale Data and Knowledge-centered Systemsfocuses 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 maindriving force behind application development in all domains. An increase in the demand forresource sharing across different sites connected through networks has led to an evolution ofdata- and knowledge-management systems from centralized systems to decentralized systemsenabling large-scale distributed applications providing high scalability.
This, the 56th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems,contains five fully revised and extended papers selected from the 39th conference on DataManagement - Principles, Technologies and Applications, BDA 2023. The topics cover awide range of timely data management research topics on adaptive learning, personal datamanagement systems, topic discovery in large corpora, spatio-temporal query processing, anddata generation.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Warehouse
- Mathematik | Informatik EDV | Informatik Angewandte Informatik
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Information Retrieval
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
Weitere Infos & Material
Multi-Objective Test Recommendation for Adaptive Learning.- Handling Dropouts in Federating Learning with Personal Data Management Systems.- ANTM: Aligned Neural Topic Models for Exploring Evolving Topics.- A Data-Driven Model Selection Approach to Spatio-Temporal Prediction.- Optimistic Data Generation for JSON Schema.
