A sample decreasing threshold greedy-based algorithm for big data summarisation

As the scale of datasets used for big data applications expands rapidly, there have been increased efforts to develop faster algorithms. This paper addresses big data summarisation problems using the submodular maximisation approach and proposes an efficient algorithm for maximising general non-nega...

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Veröffentlicht in:	Journal of big data Jg. 8; H. 1; S. 1 - 21
Hauptverfasser:	Li, Teng, Shin, Hyo-Sang, Tsourdos, Antonios
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 09.02.2021 Springer Nature B.V SpringerOpen
Schlagworte:	Algorithms Approximation Big Data Big data summarisation Communications Engineering Complexity Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Database Management Experiments Greedy algorithms Information Storage and Retrieval k-extendible system constraints Mathematical Applications in Computer Science Maximization Networks Optimization Personalised recommendation Random sampling Recommender systems Sampling Submodular maximisation Summarization Thresholds Personalised recommendation extendible system constraints Submodular maximisation Big data summarisation
ISSN:	2196-1115, 2196-1115
Online-Zugang:	Volltext
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