Abstract
The vast amount of data that organizations should gather, store and process, entails a set of new requirements towards the analytical solutions used by organizations. These requirements have become drivers for the development of the in-memory computing paradigm, which enables the creation of applications running advanced queries and performing complex transactions on very large sets of data in a much faster and scalable way than the traditional solutions. The main aim of our work is to examine the analytical possibilities of the in-memory computing solution, on the example of SAP HANA, and their possible applications. In order to do that we apply SAP HANA and its components to the challenge of forecasting of the energy demand in the energy sector. In order to examine the analytical possibilities of SAP HANA, a number of experiments were conducted. Their results are described in this paper.
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Rudny, T., Kaczmarek, M., Abramowicz, W. (2014). Analytical Possibilities of SAP HANA – On the Example of Energy Consumption Forecasting. In: Swiątek, J., Grzech, A., Swiątek, P., Tomczak, J. (eds) Advances in Systems Science. Advances in Intelligent Systems and Computing, vol 240. Springer, Cham. https://doi.org/10.1007/978-3-319-01857-7_14
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DOI: https://doi.org/10.1007/978-3-319-01857-7_14
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-01856-0
Online ISBN: 978-3-319-01857-7
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