Implementation of an IoT Sensor Data Collection and Analysis Library
Due to the development of information technology and wireless Internet technology, various data are being generated in various fields. These data are advantageous in that they provide real-time information to the users themselves. However, when the data are accumulated and analyzed, more various information can be extracted. In addition, development and dissemination of boards such as Arduino and Raspberry Pie have made it possible to easily test various sensors, and it is possible to collect sensor data directly by using database application tools such as MySQL. These directly collected data can be used for various research and can be useful as data for data mining. However, there are many difficulties in using the board to collect data, and there are many difficulties in using it when the user is not a computer programmer, or when using it for the first time. Even if data are collected, lack of expert knowledge or experience may cause difficulties in data analysis and visualization. In this paper, we aim to construct a library for sensor data collection and analysis to overcome these problems.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1315527Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1670
 R. M. Jayadeepa, M. S. Niveditha, “Computational approaches to screen candidate ligands with anti-Parkinson’s activity using R programming”, Current Topics in Medicinal Chemistry, Vol. 12, No. 16, pp. 1807-1814, August 2012.
 L. O’Callaghan, A. Meyerson, R. Motwani, N. Mishra, S. Guha, “Streaming-Data Algorithm for High-Quality Clustering”, International Conference on Data Engineering, pp. 685-694, February 2002.
 R. Jin, G. Agrawal, “Efficient decision tree construction on streaming data”, ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 571-576, 2003.
 A. Milenkovic, C. Otto, E. Jovanov, “Wireless sensor networks for personal health monitoring: Issues and an implementation”, Computer Communications, Vol. 29, Issues.13-14, pp. 2521-2533, August 2006.
 H. El-Askary, R. Gautam, R.P. Singh, M. Kafatos, “Dust storms detection over the Indo-Gangetic basin using multi sensor data”, Advances in Stalce Research, Vol. 37, Issue. 4, pp. 728-733, 2006.
 Khowaja, Ali Raza. "Process mining techniques: an application to time management." Ninth International Conference on Graphic and Image Processing. International Society for Optics and Photonics.H. Poor, An Introduction to Signal Detection and Estimation. New York: Springer-Verlag, 1985, ch. 4.
 A. K. Jain, M. N. Murty, P. J. Flynn, “Data clustering: a review”, ACM Computing Surveys, Vol. 31, Issue. 3, pp. 264-323, September 1999.
 Andrew McCallum, Nigam Kamal, and Lyle H. Ungar. "Efficient clustering of high-dimensional data sets with application to reference matching." Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2000.
 Kanungo Tapas, et al. "An efficient k-means clustering algorithm: Analysis and implementation." IEEE transactions on pattern analysis and machine intelligence 24.7 (2002): 881-892.
 Hae-Sang Park, and Chi-Hyuck Jun. "A simple and fast algorithm for K-medoids clustering." Expert systems with applications 36.2 (2009): 3336-3341.
 Sanjay Chakraborty, and Naresh Kumar Nagwani. "Analysis and study of Incremental DBSCAN clustering algorithm." arXiv preprint arXiv:1406.4754 (2014).
 WISDM Public data, Online-Avaible: http://www.cis.fordham.edu/wisdm/.