Abstract
As humans and machines generate a tremendous amount of digital data in their daily life, we are in the era of Big Data which poses unique challenges of storing, processing and analyzing this voluminous data. Sensors which continuously generate data are one important source of Big Data and have innumerous applications in real life scenario. As storing the entire data becomes expensive, summarization is the need of the hour. Data Summarization is a compact representation of the entire data which can reduce storage and processing requirements. In this work, we try to effectively summarize Sensor Data using simple but effective techniques such as sampling and clustering and analyze the performance of the summarized data in comparison to the complete dataset. Popular classification techniques like KNN, SVM and Naive Bayes are used to evaluate the efficiency of the summarization techniques by training the classifiers using the summarized data and testing with the test data set. The performance of the summarized dataset and the complete dataset are compared. The experimental results show that summarized data set performs almost equally well as the complete data set.
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P.G., L., Mallappa, S. (2017). Classification of Summarized Sensor Data Using Sampling and Clustering: A Performance Analysis. In: Santosh, K., Hangarge, M., Bevilacqua, V., Negi, A. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2016. Communications in Computer and Information Science, vol 709. Springer, Singapore. https://doi.org/10.1007/978-981-10-4859-3_15
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DOI: https://doi.org/10.1007/978-981-10-4859-3_15
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