Big data: A review

By Ş. Sağiroğlu, Duygu Sinanc. International Conference on Collaboration Technologies and Systems, 2013

1,345 citations recorded by the source corpus.

Subfields: Information Systems, Information Systems and Management, Management Information Systems

View Paper: https://www.semanticscholar.org/paper/50f15da80fbc5098db7e6d7aa815e493ea580c54

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Citations in the corpus

Big Data: Survey, Technologies, Opportunities, and Challenges (2014, background): “Five common issues are volume, variety, velocity, value, and complexity according to [4, 12].”
Agile Software Development: Methodologies and Trends (2020, background): “Variety refers to the many and different sources and types of data both structured and unstructured such as text, images, audio and video [27], [28].”
A Systematic Review on Educational Data Mining (2017, background): “ries such as records of students’ tests or examinations as well as bookkeeping records by Sagiroglu & Sinanc [24].”
Big Data and supply chain management: a review and bibliometric analysis (2016, background): “13 In recent years, scholars (Sagiroglu and Sinanc 2013; Fosso-Wamba et al. 2015; Gandomi and 14 15 Haider 2015; Khorheh et al. 2015; Wang et al. 2016; Mishra et al. 2016a, 2016b) have reviewed the 16 17 literature on Big Data.”
Digital Twin: Values, Challenges and Enablers From a Modeling Perspective (2019, background): “Indeed, there are many survey articles to discuss various aspects of big data including not only state-of-the-art technology and platforms, but also algorithms, applications and challenges [393]–[407].”
A Survey on Big Data Market: Pricing, Trading and Protection (2018, background): “There are numerous benefits of data analytics, including social influence marketing (61%), customer-based marketing (41%), and opportunities of salesmarketing (38%) [48].”
Step away from stepwise (2018, background): “Sagiroglu and Sinanc [25] describe data mining as a quest “to reveal hidden patterns and secret correlations.””
Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities (2020, background): “When it comes to internal sensing, big data analytics can enable firms to identify inefficiencies in processes [84], detect deviations from quality controls, and best practices such as anomaly detection [85], and proactively locate cases of high risk and fault occurrence [77,86].”
Big Data Analytics Capabilities and Innovation: The Mediating Role of Dynamic Capabilities and Moderating Effect of the Environment (2019, positive): “Specifically, by fostering BDAC’s firms can make sense of vast quantities of diverse data which would be impossible to analyze and interpret otherwise (Sagiroglu & Sinanc, 2013).”
A review of machine learning for the optimization of production processes (2019, background): “Most data coming from production systems equipped with sensors can be considered as structured data which is much easier to handle than unstructured data [85].”
Fog computing security: a review of current applications and security solutions (2017, background): “The Fog computing paradigm is largely motivated by a continuous increase in Internet of Things (IoT) devices, where an ever increasing amount of data (with respect to volume, variety, and velocity [1]) is generated from an ever-expanding array of devices.”