846 resultados para Internet of Things,Internet of Things collaborativo,Open data,Data Mining,Clustering,Classificazione,Dati sensoristici
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Fastener holes in aeronautical structures are typical sources of fatigue cracks due to their induced local stress concentration. A very efficient solution to this problem is to establish compressive residual stresses around the fastener holes that retard the fatigue crack nucleation and its subsequent local propagation. Previous work done on the subject of the application of LSP treatment on thin, open-hole specimens [1] has proven that the LSP effect on fatigue life of treated specimens can be detrimental, if the process is not properly optimized. In fact, it was shown that the capability of the LSP to introduce compressive residual stresses around fastener holes in thin-walled structures representative of typical aircraft constructions was not superior to the performance of conventional techniques, such as cold-working.
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Within the European Union, member states are setting up official data catalogues as entry points to access PSI (Public Sector Information). In this context, it is important to describe the metadata of these data portals, i.e., of data catalogs, and allow for interoperability among them. To tackle these issues, the Government Linked Data Working Group developed DCAT (Data Catalog Vocabulary), an RDF vocabulary for describing the metadata of data catalogs. This topic report analyzes the current use of the DCAT vocabulary in several European data catalogs and proposes some recommendations to deal with an inconsistent use of the metadata across countries. The enrichment of such metadata vocabularies with multilingual descriptions, as well as an account for cultural divergences, is seen as a necessary step to guarantee interoperability and ensure wider adoption.
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Objective: To compare tension-free open mesh hernioplasty under local anaesthetic with transabdominal preperitoneal laparoscopic hernia repair under general anaesthetic.
Open business intelligence: on the importance of data quality awareness in user-friendly data mining
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Citizens demand more and more data for making decisions in their daily life. Therefore, mechanisms that allow citizens to understand and analyze linked open data (LOD) in a user-friendly manner are highly required. To this aim, the concept of Open Business Intelligence (OpenBI) is introduced in this position paper. OpenBI facilitates non-expert users to (i) analyze and visualize LOD, thus generating actionable information by means of reporting, OLAP analysis, dashboards or data mining; and to (ii) share the new acquired information as LOD to be reused by anyone. One of the most challenging issues of OpenBI is related to data mining, since non-experts (as citizens) need guidance during preprocessing and application of mining algorithms due to the complexity of the mining process and the low quality of the data sources. This is even worst when dealing with LOD, not only because of the different kind of links among data, but also because of its high dimensionality. As a consequence, in this position paper we advocate that data mining for OpenBI requires data quality-aware mechanisms for guiding non-expert users in obtaining and sharing the most reliable knowledge from the available LOD.
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Prepared by Cynthia Norris Graae, Program analyst, Office of Federal Civil Rights Evaluation.
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Mode of access: Internet.
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National Highway Traffic Safety Administration, Washington, D.C.
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Mode of access: Internet.
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Series "A" pt. 1: 2d ed.
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National Highway Safety Bureau, Washington, D.C.
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National Highway Traffic Safety Administration, Washington, D.C.
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Federal Highway Administration, Washington, D.C.
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Federal Highway Administration, Washington, D.C.