Data science methodology for Internet-of-Things

Brohi, Sarfraz; Marjani, Mohsen; Hashem, Ibrahim Abaker Targio; Pillai, Thulasyammal Ramiah; Kaur, Sukhminder; Amalathas, Sagaya Sabestinal

Abstract

The journey of data from the state of being valueless to valuable has been possible due to powerful analytics tools and processing platforms. Organizations have realized the potential of data, and they are looking far ahead from the traditional relational databases to unstructured as well as semi-structured data generated from heterogeneous sources. With the numerous devices and sensors surrounding our ecosystem, IoT has become a reality, and with the use of data science, IoT analytics has become a tremendous opportunity to perceive incredible insights. However, despite the various benefits of IoT analytics, organizations are apprehensive with the dark side of IoT such as security and privacy concerns. In this research, we discuss the opportunities and concerns of IoT analytics. Moreover, we propose a generic data science methodology for IoT data analytics named as Plan, Collect and Analytics for Internet-of-Things (PCA-IoT). The proposed methodology could be applied in IoT scenarios to perform data analytics for effective and efficient decision-making.

• Presentation Conference Type: Conference Paper (published)
• Conference Name: iCETiC 2019: Emerging Technologies in Computing
• Start Date: Aug 19, 2019
• End Date: Aug 20, 2019
• Acceptance Date: Jun 3, 2019
• Publication Date: Jul 14, 2019
• Deposit Date: Sep 9, 2022
• Publisher: Springer Verlag (Germany)
• Volume: 285
• Pages: 178-186
• Series Title: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST,volume 285)
• Book Title: International Conference for Emerging Technologies in Computing
• Chapter Number: 13
• ISBN: 9783030239428
• DOI: https://doi.org/10.1007/978-3-030-23943-5_13
• Public URL: https://uwe-repository.worktribe.com/output/9941048
• Publisher URL: https://link.springer.com/chapter/10.1007/978-3-030-23943-5_13
• Related Public URLs: https://link.springer.com/book/10.1007/978-3-030-23943-5; ; https://eudl.eu/proceedings/ICETIC/2019