Towards Scalability Guidelines for Semantic Data Container Management

Gunnar Brataas, Bernd Neumayr, Christoph G. Schuetz, Audun Vennesland
<span title="">2018</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="" style="color: black;">Companion of the 2018 ACM/SPEC International Conference on Performance Engineering - ICPE &#39;18</a> </i> &nbsp;
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
more &raquo; ... clude this work-in-progress paper by describing how more measurements could be performed so that the missing guidelines could be provided.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.1145/3185768.3186302</a> <a target="_blank" rel="external noopener" href="">dblp:conf/wosp/BrataasNSV18</a> <a target="_blank" rel="external noopener" href="">fatcat:3lmnywewkffylez5dzigkmxtlu</a> </span>
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