Data Mining Efficiency and Scalability for Smarter Internet of Things

The impact of engineering students' performance in the first three years on their graduation result using educational data mining

“…The discovery of hidden information is achieved by running data mining algorithms that combine statistics with computer science to mine valuable information from a seemingly meaningless data jumble. Data mining can be applied in various fields such as engineering (Adekitan et al., 2019; Saini and Aggarwal, 2018), business management (Zuo et al., 2016), marketing and product design (Jin et al., 2019), computer science (Mahendra et al., 2019), education (Ibrahim et al., 2019; Porouhan, 2018), genetics (Noreña et al., 2018), biological studies (Gu et al., 2018), facility maintenance management (Miguel-Cruz et al., 2019), health and drug development studies (Keserci et al., 2017), chemistry and toxicity analysis (Saini and Srivastava, 2019), meteorology (Kovalchuk et al., 2019), transportation safety (Divya et al., 2019) and traffic management (Amiruzzaman, 2019), fraud detection (Vardhani et al., 2019), and so forth. In the educational sector, volumes of data are daily generated from various teaching and learning activities within an institution.…”

Bringing Back the Person into Behavioural Personality Science Using Big Data

“…Doing thorough replications and discussing potential limitations and alternative explications should go without saying. Such replications are also possible with regard to n–of–1 studies (Kratochwill et al, 2013; Manolov, Gast, Perdices, & Evans, 2014).…”

“…In addition, Ihsan and Furnham (2018) expect the Internet of Things to lead to increased amounts of data gathered from individuals or households (Atzori, Iera, & Morabito, 2014). As many as 50 billion items are expected to be connected to the Internet of Things by 2020, making it a challenging task to process, mine and analyse this large amount of data (Mahendra, Kishore, & Prathima, 2019).…”

“…In addition, Ihsan and Furnham (2018) expect the Internet of Things to lead to increased amounts of data gathered from individuals or households (Atzori, Iera, & Morabito, 2014). As many as 50 billion items are expected to be connected to the Internet of Things by 2020, making it a challenging task to process, mine and analyse this large amount of data (Mahendra, Kishore, & Prathima, 2019). Importantly, such high volume and highly various data collected longitudinally and across different situations may be used for hypothesis testing with enough statistical power at the level of the individual person and also to move beyond the single individual by the possibility to determine similar patterns for groups of persons (Hofmans, De Clercq, Kuppens, Verbeke, & Widiger, 2019 for this reasoning regarding ambulatory assessment).…”