Big Data: New Opportunities and New Challenges [Guest editors' introduction]

By Katina Michael, K. Miller. Computer, 2013

298 citations recorded by the source corpus.

Subfields: Transportation

View Paper: https://www.semanticscholar.org/paper/00e12432f71351ae2e25872e32d018a82660c051

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Transportation

1. Md. Zakirul Alam Bhuiyan
2. Mehdi Gheisari
3. Guojun Wang
4. Martin E. P. Seligman

Citations in the corpus

• Big Data: Survey, Technologies, Opportunities, and Challenges (2014, unknown): “Therefore, high processing speed is necessary [77].”
• Using wellbeing for public policy: Theory, measurement, and recommendations (2016, background): “In the private sector, corporations are increasingly using big data to inform decision making in the domains of supply chains, workforce management, and consumer marketing (Michael & Miller, 2013).”
• Where are human subjects in Big Data research? The emerging ethics divide (2016, background): “The range of harms made possible by data analytics extremely hard to fore-see and delimit (Andrejevic, 2014; Michael and Miller, 2013; Nissenbaum 2009, 2011; Polonetsky et al., 2015).”
• Big Data and cloud computing: innovation opportunities and challenges (2017, background): “The unprecedented networking among smart devices and computing platforms contributes to Big Data but poses privacy concerns where an individual ’ s location, behavior and transactions are digitally recorded (Cukier 2010; Tene 2011; Michael and Miller 2013; Cheatham 2015).”
• A Survey on Deep Learning in Big Data (2017, methodology): “Data analysis methods should provide availability to support large amounts of data along with a high-speed stream of data [9].”
• Transforming business using digital innovations: the application of AI, blockchain, cloud and data analytics (2020, background): “Although data analytics are transforming business operations, firms need to address challenges in both managerial and technological contexts to extract value from large data sets (Michael and Miller 2013).”