… , which will help shape up the future of large-scale analytics, leading the way to the design
of … amounts of heterogeneous data, generated by massively distributed data sources. Each …

The increasing adoption of Big Data analytics has led to a high demand for efficient technologies
in order to manage and process large datasets. Popular MapReduce frameworks such …

… -of-art big data analytics (BDA) approaches for large-scale wireless networks. In particular, …
stages: Data Acquisition, Data Preprocessing, Data Storage, and Data Analytics. We then …

… – a distributed, large scale approach for semantic discovery, data analytics and reasoning
of large-scale real-time Internet of Things and relevant social data streams for knowledge …

… MapReduce and Spark are two very popular open source cluster computing frameworks for
large scale data analytics. These frameworks hide the complexity of task parallelism and fault…

… 2.2 Storage and data management challenges … if we are scale large-scale data
analysis. New data organizations are needed that better fit the intrinsic data cube model of n-dimensional …

… used large-scale MapReduce platform, … data analytics platform, and we needed a simpler
and more flexible solution that could be rapidly customized and evolved for the changing data …

… technologies for supporting advanced analytics over large-scale data repositories.
Furthermore, Starfish [11] is a recent self-tuning system for supporting big data analytics that is still …

… of data analytics in order processing. Furthermore, due to the synergetic nature and
uncertainty as well as scalability of data analytics … to handle data analytics projects. As a result, …

We provide an overview of the recent trends toward digitalization and large-scale data analytics
in healthcare. It is expected that these trends are instrumental in the dramatic changes in …