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Title: Weather Data Analysis and Sensor Fault Detection Using An Extended IoT Framework with Semantics, Big Data, and Machine Learning
Authors: Önal, Aras Can
Sezer, Ömer Berat
Özbayoğlu, Ahmet Murat
Doğdu, Erdoğan
Keywords: Internet of things
machine learning
big data analytics
weather data analysis
anomaly detection
fault detection
Issue Date: 2017
Publisher: IEEE
Source: Onal, A. C., Sezer, O. B., Ozbayoglu, M., & Dogdu, E. (2017, December). Weather data analysis and sensor fault detection using an extended iot framework with semantics, big data, and machine learning. In 2017 IEEE International Conference on Big Data (Big Data) (pp. 2037-2046). IEEE.
Abstract: In recent years, big data and Internet of Things (IoT) implementations started getting more attention. Researchers focused on developing big data analytics solutions using machine learning models. Machine learning is a rising trend in this field due to its ability to extract hidden features and patterns even in highly complex datasets. In this study, we used our Big Data IoT Framework in a weather data analysis use case. We implemented weather clustering and sensor anomaly detection using a publicly available dataset. We provided the implementation details of each framework layer (acquisition, ETL, data processing, learning and decision) for this particular use case. Our chosen learning model within the library is Scikit-Learn based k-means clustering. The data analysis results indicate that it is possible to extract meaningful information from a relatively complex dataset using our framework.
Description: IEEE International Conference on Big Data (IEEE Big Data) (2017 : Boston, MA)
ISBN: 978-1-5386-2715-0
ISSN: 2639-1589
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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