Tutorial: Big Data Analytics: Concepts, Technologies, and Applications

By Hugh J. Watson. Communications of the Association for Information Systems, 2014

295 citations recorded by the source corpus.

Subfields: Information Systems and Management, Management Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/2b87d63638c9a2282c5ca7de074a50658cdce098

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In Information Systems and Management

In Management Information Systems

In Management Science and Operations Research

Citations in the corpus

An integrated big data analytics-enabled transformation model: Application to health care (2018, background): “We identi ﬁ ed three architectural components of big data analytics from its tools and functionalities, namely data aggregation, data analysis, and data interpretation, by reviewing the relevant academic literature (e.g., [4,22]) and technology tutorials (e.g., [23,24]).”
A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: A framework, challenges and future research directions (2019, background): “Definitions, technologies, modeling approaches and research challenges of big data from both industry and academic fields were discussed in the literature (Costa and Santos, 2017; Gandomi and Haider, 2015; Watson, 2014).”
How Big Data Analytics Enables Service Innovation: Materiality, Affordance, and the Individualization of Service (2018, methodology): “API Provides access to data sources like sensor data, clickstream data, and social media data [3] Data lake Stores data in its native format until it is needed; used in combination with, for example, a Hadoop framework, this technology allows firms to analyze large and/or unstructured data much fast”
Critical analysis of Big Data challenges and analytical methods (2017, background): “Watson (2014) asserts that descriptive analytics, such as reporting, dashboards, scorecards, and data visualization, have been widely used for some time, and are the core applications of traditional business intelligence.”