Data science: developing theoretical contributions in information systems via text analytics

Rizk, Aya; Elragal, Ahmed

doi:10.1186/s40537-019-0280-6

Cited by 37 publications

(21 citation statements)

References 58 publications

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“…Current studies stated that big data analytics could transform the practices of doing business and theory of management [19][20][21]. It is considered as "Break through technological development" [22] "the digital revolution" [23] and "the fourth paradigm of science" [24]. Gartner [25] stated that organizations had invested USD 2.1 trillion in 2013 to leverage big data analytics, and these investments are expected to grow around USD 3.8 trillion in 2014.…”

Section: Introductionmentioning

confidence: 99%

How Big Data Analytics Boosts Organizational Performance: The Mediating Role of the Sustainable Product Development

Ali

Poulová

Yasmin

et al. 2020

Journal of Open Innovation: Technology, Market, and Complexity

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Increasing haze pollution and its adverse effects on human health is pressuring academics and practitioners to search for different solutions for environmental sustainability around the world. Similar to other countries, Pakistan is also affected by air pollution, and smog has become a fifth season. In Pakistan, one of the main reasons of smog and air pollution is hazardous emissions from vehicles. As a result, the booming automobile industry of Pakistan is now affected by two major challenges: sustainable product development and organizational performance. To meet these challenges, the study has developed a conceptual model to find the effect of big data analytics on organizational performance by adopting a sustainable development program. For the elimination of standard method biases, the study has used a time lag approach to collect the data in three waves and receive 372 usable responses. The empirical results of PLS-SEM suggest that big data analytics have a positive effect on a sustainable product development and sustainable product development has a positive and significant impact on organizational performance. Moreover, mediation of a sustainable program development is also confirmed between big data analytics and organizational performance. The managerial and theoretical implications of these results are discussed.

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Section: Introductionmentioning

confidence: 99%

How Big Data Analytics Boosts Organizational Performance: The Mediating Role of the Sustainable Product Development

Ali

Poulová

Yasmin

et al. 2020

Journal of Open Innovation: Technology, Market, and Complexity

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“…The authors in [ 87 ] provide a thorough understanding of computational optimal transport with application to data science. In [ 97 ], the authors present data science as theoretical contributions in information systems via text analytics.…”

Section: Background and Related Workmentioning

confidence: 99%

Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective

Sarker

2021

SN COMPUT. SCI.

261

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The digital world has a wealth of data, such as internet of things (IoT) data, business data, health data, mobile data, urban data, security data, and many more, in the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR). Extracting knowledge or useful insights from these data can be used for smart decision-making in various applications domains. In the area of data science, advanced analytics methods including machine learning modeling can provide actionable insights or deeper knowledge about data, which makes the computing process automatic and smart. In this paper, we present a comprehensive view on "Data Science" including various types of advanced analytics methods that can be applied to enhance the intelligence and capabilities of an application through smart decision-making in different scenarios. We also discuss and summarize ten potential real-world application domains including business, healthcare, cybersecurity, urban and rural data science, and so on by taking into account data-driven smart computing and decision making. Based on this, we finally highlight the challenges and potential research directions within the scope of our study. Overall, this paper aims to serve as a reference point on data science and advanced analytics to the researchers and decision-makers as well as application developers, particularly from the data-driven solution point of view for real-world problems.

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“…Data science is largely driven by the availability of data [48]. Datasets typically represent a collection of information records that consist of several attributes or features and related facts, in which cybersecurity data science is based on.…”

Section: Understanding Cybersecurity Datamentioning

confidence: 99%

Cybersecurity data science: an overview from machine learning perspective

et al. 2020

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In a computing context, cybersecurity is undergoing massive shifts in technology and its operations in recent days, and data science is driving the change. Extracting security incident patterns or insights from cybersecurity data and building corresponding data-driven model, is the key to make a security system automated and intelligent. To understand and analyze the actual phenomena with data, various scientific methods, machine learning techniques, processes, and systems are used, which is commonly known as data science. In this paper, we focus and briefly discuss on cybersecurity data science, where the data is being gathered from relevant cybersecurity sources, and the analytics complement the latest data-driven patterns for providing more effective security solutions. The concept of cybersecurity data science allows making the computing process more actionable and intelligent as compared to traditional ones in the domain of cybersecurity. We then discuss and summarize a number of associated research issues and future directions. Furthermore, we provide a machine learning based multi-layered framework for the purpose of cybersecurity modeling. Overall, our goal is not only to discuss cybersecurity data science and relevant methods but also to focus the applicability towards data-driven intelligent decision making for protecting the systems from cyber-attacks.

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Data science: developing theoretical contributions in information systems via text analytics

Cited by 37 publications

References 58 publications

How Big Data Analytics Boosts Organizational Performance: The Mediating Role of the Sustainable Product Development

How Big Data Analytics Boosts Organizational Performance: The Mediating Role of the Sustainable Product Development

Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective

Cybersecurity data science: an overview from machine learning perspective

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