Titlebook: Data Management Technologies and Applications; 7th International Co Christoph Quix,Jorge Bernardino Conference proceedings 2019 Springer Na

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1865-0929 eal with the following topics: databases, big data, data mining, data management, data security, and other aspects of information systems and technology involving advanced applications of data.978-3-030-26635-6978-3-030-26636-3Series ISSN 1865-0929 Series E-ISSN 1865-0937

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Limitations of coaching with colleaguesdel reaches similar results while being simpler, clearer, more versatile, extendable and transparent. The presented guide can be used as a manual for anyone building a data visualization recommender system. The resulting model can be applied in the development of new data visualization software or a

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https://doi.org/10.1007/978-3-658-10171-8ion accuracy. The second method combines these regression models with a stochastic model to estimate the number of asset replacements needed for existing and planned systems in the future. Both methods were validated by experiments in the domain of rail automation.

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Robert Wegener,Agnès Fritze,Michael Loebbert when handling sparse training resources. Using the SIMO approach, noticeable performance accuracy and f-measure improvements were achieved over standalone SMAC optimisation. Results showed significant improvement when comparing collective training data with SIMO instance optimisation including indi

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Tanya Faude-Koivisto,Peter Gollwitzeras been experimented with different sizes and hyperparameters over all the multivariate time series. The combinations of trading protocols with variants of the 3 neural network types have been in turn applied to time series, varying the input variables from 4 to 17 and the training period from 8 to

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Constructing a Data Visualization Recommender System,del reaches similar results while being simpler, clearer, more versatile, extendable and transparent. The presented guide can be used as a manual for anyone building a data visualization recommender system. The resulting model can be applied in the development of new data visualization software or a

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A Comprehensive Prediction Approach for Hardware Asset Management,ion accuracy. The second method combines these regression models with a stochastic model to estimate the number of asset replacements needed for existing and planned systems in the future. Both methods were validated by experiments in the domain of rail automation.