Abstract
In this work, the structure for the prototype construction of an application that can be framed within ubiquitous sensing is proposed. The objective of application is to allow that a user knows through his mobile device which other users of his environment are doing the same activity as him. Therefore, the knowledge is obtained from data acquired by pervasive sensors. The FIWARE infrastructure is used to allow to homogenize the data flows.
An important element of the application is the Intelligent Data Analysis module where, within the Apache Storm technology, a Data Mining technique will be used. This module identifies the activity carried out by mobile device user based on the values obtained by the different sensors of the device.
The Data Mining technique used in this module is an extension of the Nearest Neighbors technique. This extension allows the imperfect data processing, and therefore, the effort that must be made in the data preprocessing to obtain the minable view of data is reduced. It also allows us to parallelize part of the process by using the Apache Storm technology.
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Supported by the project TIN2017-86885-R (AEI/FEDER, UE) granted by the Ministry of Economy, Industry and Competitiveness of Spain (including ERDF support).
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Cadenas, J.M., Garrido, M.C., Villa, C. (2018). Towards an App Based on FIWARE Architecture and Data Mining with Imperfect Data. In: Medina, J., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations. IPMU 2018. Communications in Computer and Information Science, vol 854. Springer, Cham. https://doi.org/10.1007/978-3-319-91476-3_7
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