Thèse de doctorat : Université de Fribourg, 2020 ; no. 2192.
We live in an Information Age, facing a rapid increase in the amount of information that is exchanged. This permanently growing amount of data makes the ability to store, analyze, and act upon information a primary concern (in addition to the obvious privacy, legal and ethical issues that are related), raising the question: “How can one consume Big Data and transformit into actionable...
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In: 2018 IEEE International Conference on Big Data (Big Data), 2018, p. 2253–2262
Large knowledge bases typically contain data adhering to various schemas with incomplete and/or noisy type information. This seriously complicates further integration and post-processing efforts, as type information is crucial in correctly handling the data. In this paper, we introduce a novel statistical type inference method, called StaTIX, to effectively infer instance types in Linked Data...
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In: 2018 IEEE International Conference on Data Mining Workshops (ICDMW), 2018, p. 1481–1486
There is a great diversity of clustering and community detection algorithms, which are key components of many data analysis and exploration systems. To the best of our knowledge, however, there does not exist yet any uniform benchmarking framework, which is publicly available and suitable for the parallel benchmarking of diverse clustering algorithms on a wide range of synthetic and...
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