To main content

Towards scalability guidelines for semantic data container management

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.
Read publication

Category

Academic chapter/article/Conference paper

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

View this publication at Cristin