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Towards Scalability Guidelines for Semantic Data Container Management

Towards Scalability Guidelines for Semantic Data Container Management

Category
Part of a book/report
Abstract
Semantic container management is a promising approach to organize data. However, the scalability of this approach is challenging. By scalability in this paper, we mean the expressivity and size of the semantic data containers we can handle, given a suitable quality threshold. In this paper, we derive scalability characteristics of the semantic container approach in a structured way. We also describe actual experiments where we vary the number of available CPU cores and quality thresholds. We conclude this work-in-progress paper by describing how more measurements could be performed so that the missing guidelines could be provided.
Client
  • EC/H2020 / 699298
Language
English
Author(s)
Affiliation
  • SINTEF Digital / Software Engineering, Safety and Security
  • Johannes Kepler University Linz
Year
2018
Publisher
Association for Computing Machinery (ACM)
Book
ICPE '18, Companion of the 2018 ACM/SPEC International Conference on Performance Engineering, Berlin, Germany — April 09 - 13, 2018
ISBN
978-1-4503-5629-9
Page(s)
17 - 20