Abstract
Data generated by sensors, need to be stored in a repository which is of large in size. However, data stored in sensor data repository consists of inconsistent, inaccurate, redundant and noisy data. Deployment of data mining algorithm on such sensor datasets declines the performance of the mining algorithm. Therefore, data preprocessing techniques are proposed to eliminate inconsistent, inaccurate, redundant, noisy data from the datasets and symbolic data analysis approach is proposed to reduce the size of the data repository by creating a symbolic data table. The data stored in a more comprehensible manner through symbolic data table is modeled as object oriented data model for mining knowledge from the sensor data sets.
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Doreswamy, Narasegouda, S. (2014). Symbolic Data Analysis for the Development of Object Oriented Data Model for Sensor Data Repository. In: Satapathy, S., Udgata, S., Biswal, B. (eds) Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2013. Advances in Intelligent Systems and Computing, vol 247. Springer, Cham. https://doi.org/10.1007/978-3-319-02931-3_49
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DOI: https://doi.org/10.1007/978-3-319-02931-3_49
Publisher Name: Springer, Cham
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