Statistical Modeling and Visualizing Open Big Data Using a Terrorism Case Study

Vajjhala, Narasimha Rao; Strang, Kenneth David; Sun, Zhaohao

doi:10.1109/ficloud.2015.15

Cited by 24 publications

(12 citation statements)

References 25 publications

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“…Actually, several researchers had already pointed out that a barrier to performing big data analytics was that most statistical software could not handle the large file sizes. 9 However, researchers have found ways around the big data five Vs—at least the volume, velocity, and variety attributes—by using cloud-based and distributed software such as Hadoop along with sampling techniques to reduce the five Vs. 8,48,49 Nonetheless, this is where another hidden healthcare big data problem lurks. There are several tacit issues that revolve around research design assumptions and statistical sampling assumptions.…”

Section: Literature Review and Discussionmentioning

confidence: 99%

“…8 Value in big data can be viewed as a constraint because it can be challenging to derive a benefit from analytics that is worth the investment time and cost to accommodate the other factors. Big data veracity can refer to ethics, accuracy, validity, or truthfulness 9 as well as social-cultural relevance. 10 In addition to the above characteristics, each discipline and industry has unique big data analytics issues.…”

Section: Introductionmentioning

confidence: 99%

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Hidden big data analytics issues in the healthcare industry

Strang

Sun

2019

Health Informatics J

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The goal of the study was to identify big data analysis issues that can impact empirical research in the healthcare industry. To accomplish that the author analyzed big data related keywords from a literature review of peer reviewed journal articles published since 2011. Topics, methods and techniques were summarized along with strengths and weaknesses. A panel of subject matter experts was interviewed to validate the intermediate results and synthesize the key problems that would likely impact researchers conducting quantitative big data analysis in healthcare studies. The systems thinking action research method was applied to identify and describe the hidden issues. The findings were similar to the extant literature but three hidden fatal issues were detected. Methodical and statistical control solutions were proposed to overcome the three fatal healthcare big data analysis issues.

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Section: Literature Review and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hidden big data analytics issues in the healthcare industry

Strang

Sun

2019

Health Informatics J

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“…The use of parametric statistical techniques requires rigorous designs that ensure the prerequisites of the data are satisfied including distribution, population-sample homogeneity, sample group size, data type, and other inferential thresholds including collinearity and variance tolerance (Strang, 2015d). Learning analytics software generally involves nonparametric distribution-free nonlinear techniques utilised in big data analytics (Chatti et al, 2012, p.10;Strang and Sun, 2015;Sun, Strang and Yearwood, 2014;Xing et al, 2015), which include cluster analysis, neural network analysis with Bayes probability theory, nonlinear math programming, correspondence analysis, and genetic nonlinear programming (Nersesian and Strang, 2013;Strang, 2012;Vajjhala, Strang and Sun, 2015;Xing et al, 2015). The strategy for this study is to accept learning analytics as a 'black box' big data summarisation tool by using its output for input into the unit of analysis during hypothesis testing.…”

Section: Empirical Learning Analytics Researchmentioning

confidence: 99%

Can online student performance be forecasted by learning analytics?

Strang

2016

IJTEL

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The paper focuses on utilising learning analytics to assess student performance in an online business. The value of this paper is in the literature review and the process used for analysis since it provides a way of how research in Moodle learning analytics or other similar systems could be leveraged to track student progress and for retention. The engagement analytics module is a relatively new component of Moodle but it has not yet been investigated with respect to student performance or in online business courses. The purpose of this study was to examine the predictive relationships between student academic performance and Moodle engagement analytics indicators along with student online activity data from the system logs. Unfortunately none of the hypothesised learning analytics factors were positively related to, nor could they predict, student academic performance. However, several interesting deductions from the learning analytics data gave rise to ideas for further research. Sense making of puzzling statistics suggested a mediating pattern of students' poor self-regulation skills because more focus was put on the assignment requirements but less on interacting with the lesson materials needed to complete the assignment and thereby resulting in lower grades.

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“…It is impractical to define a universal threshold for big data volume (i.e., what constitutes a 'big dataset') because the time and type of data can influence its definition [23]. Currently, datasets that reside in the exabyte (EB) or ZB ranges are generally considered as big data [8,24], however challenges still exist for datasets in smaller size ranges. For example, Walmart collects 2.5 PB from over a million customers every hour [25].…”

Section: Big Datamentioning

confidence: 99%

Uncertainty in big data analytics: survey, opportunities, and challenges

2019

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According to the National Security Agency, the Internet processes 1826 petabytes (PB) of data per day [1]. In 2018, the amount of data produced every day was 2.5 quintillion bytes [2]. Previously, the International Data Corporation (IDC) estimated that the amount of generated data will double every 2 years [3], however 90% of all data in the world was generated over the last 2 years, and moreover Google now processes more than 40,000 searches every second or 3.5 billion searches per day [2]. Facebook users upload 300 million photos, 510,000 comments, and 293,000 status updates per day [2, 4]. Needless to say, the amount of data generated on a daily basis is staggering. As a result, techniques are required to analyze and understand this massive amount of data, as it is a great source from which to derive useful information.

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Statistical Modeling and Visualizing Open Big Data Using a Terrorism Case Study

Cited by 24 publications

References 25 publications

Hidden big data analytics issues in the healthcare industry

Hidden big data analytics issues in the healthcare industry

Can online student performance be forecasted by learning analytics?

Uncertainty in big data analytics: survey, opportunities, and challenges

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