Big Data computing and clouds: Trends and future directions

By Marcos Dias de Assunção, Rodrigo Neves Calheiros, S. Bianchi, M. Netto, Rajkumar Buyya. J. Parallel Distributed Comput., 2013

898 citations recorded by the source corpus.

This paper discusses approaches and environments for carrying out analytics on Clouds for Big Data applications. It revolves around four important areas of analytics and Big Data, namely (i) data management and supporting architectures; (ii) model development and scoring; (iii) visualisation and user interaction; and (iv) business models. Through a detailed survey, we identify possible gaps in technology and provide recommendations for the research community on future directions on Cloud-supported Big Data computing and analytics solutions. Survey of solutions for carrying out analytics and Big Data on Clouds.Identification of gaps in technology for Cloud-based analytics.Recommendations of research directions for Cloud-based analytics and Big Data.

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

View Paper: https://www.semanticscholar.org/paper/31cacba3495e1aa21d218a57d1259df091430cd6

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Citations in the corpus

Next generation cloud computing: New trends and research directions (2017, positive): “Firstly, data processing and resource management on distributed cloud nodes29 [75].”
Big data analytics in supply chain management: A state-of-the-art literature review (2017, background): “BD has been characterized by 5Vs: volume, variety, velocity, veracity, and value (Wamba et al., 2015; Assunção et al., 2015; Emani et al., 2015).”
Big Data sources and methods for social and economic analyses (2017, methodology): “…data reuse and bringing access to previous results, have been included in a variety of places, ranging from being included in the data module (Assunção et al., 2015) or the preprocessing module (Wang et al., 2016a; Zhang et al., 2017), to being a macro-functionality present in each module of…”
Big Data and cloud computing: innovation opportunities and challenges (2017, background): “Several investigations have identified new initiatives for Big Data and cloud computing (Bughin, Chui, and Manyika 2010; Karimi 2014; Assunção et al. 2015; Hashem et al. 2015) from different facets of management/architecture, model development, visualization, interaction and business.”
Critical analysis of Big Data challenges and analytical methods (2017, methodology): “Often, large volumes of historical data is used in descriptive analytics to identify patterns and createmanagement reports that is concerned with modelling past behaviour (Assunção et al., 2015). Watson (2014) asserts that descriptive analytics, such as reporting, dashboards, scorecards, and data vi”
Big Data Analytics in Intelligent Transportation Systems: A Survey (2019, background): “ical data, then processed within a short time [159].”
Fog Computing: Survey of Trends, Architectures, Requirements, and Research Directions (2018, background): “As a result, organizations are generating and storing huge amounts of data on a regular basis [1].”
Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice (2017, background): “The presence of data visualisation capabilities can effectively support the three analytics capabilities (descriptive, predictive, and prescriptive) (Assunção et al., 2015).”