Big data and cloud computing: current state and future opportunities

By D. Agrawal, Sudipto Das, A. El Abbadi. EDBT/ICDT Workshops, 2011

621 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/2d32977c4550c87cf5b9cde263094570d45a78d9

How researchers see this paper

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

In Computer Networks and Communications

In Information Systems

Citations in the corpus

Understandable Big Data: A survey (2015, background): “[5] provide a survey on scalable database management: updating of heavy application, analytics and decision support.”
Data management in cloud environments: NoSQL and NewSQL data stores (2013, background): “Today, businesses are aware that this huge volume of data can be used to generate new opportunities and process improvements through their processing and analysis [5,6].”
Big Data and cloud computing: innovation opportunities and challenges (2017, methodology): “How to optimize different traditional (e.g. MySQL, Oracle) and emerging DBMSs (NoSQL) for distributed storage (Agrawal, Das, and El Abbadi 2011)?”
Customer relationship management and big data enabled: Personalization & customization of services (2019, background): “Therefore reliability is competitive advantage of cloud computing in big data application [25].”
Sensing as a Service and Big Data (2013, background): “Due to lack of cloud feature and high costs in relational database management systems are less favour to be used in big data and cloud environment [33].”
Analytics over large-scale multidimensional data: the big data revolution! (2011, methodology): “To this end, exploiting the cloud computing computational framework seems to be the most promising way to this end [2].”
Cloud-enabled prognosis for manufacturing (2015, background): “…benefits the adoption of prognostic techniques through the enhanced computational capability, which not only improves the execution efficiency [203] of prognosis models but also enables more robust decision-making due to information and knowledge sharing by new techniques such as…”