Titlebook: Enterprise Information Systems; 20th International C Slimane Hammoudi,Micha? ?mia?ek,Joaquim Filipe Conference proceedings 2019 Springer Na

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https://doi.org/10.1007/978-3-319-65885-8, then (c) running load tests and recording performance data and finally (d) analyzing the obtained logs in order to detect problems under load. Our approach is illustrated by means of a case study from the healthcare domain.

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The War in Ukraine and International Law different experimentations on its different parts. The incorporation of scientometric indicators into the different parts of our approach has significantly improved retrieval performance which is rated for 41.66% in terms of F-measure. An important implication of this finding is the existence of co

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The problem is growing, not going away for inference of learning centered emotions. In these experiments we achieve an accuracy rate and Cohen Kappa near to 65% and 0.55, respectively, in the task of inferring five classes of learning centered emotion. Even though these results are better than some related work, we believe they can be i

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https://doi.org/10.1057/9781403978264aggregate. In addition, we automate the process of reformulating the classical manipulation operators (insert, delete and update queries) in order to update the dictionary according to the different structural changes made in the collection. These two processes produce queries which are compatible w

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https://doi.org/10.1007/978-1-349-10840-4ata attribute labels. The framework, whose design is based on intensive review of real-world data attribute labels, queries arbitrary pluggable knowledge bases and weights/aggregates their results. We evaluate our approach with different existing knowledge bases and compare it with existing state-of

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