Big Data and cloud computing: innovation opportunities and challenges

@article{Yang2017BigDA,
  title={Big Data and cloud computing: innovation opportunities and challenges},
  author={Chaowei Phil Yang and Qunying Huang and Zhenlong Li and Kai Liu and Fei Hu},
  journal={International Journal of Digital Earth},
  year={2017},
  volume={10},
  pages={13 - 53}
}
ABSTRACT Big Data has emerged in the past few years as a new paradigm providing abundant data and opportunities to improve and/or enable research and decision-support applications with unprecedented value for digital earth applications including business, sciences and engineering. At the same time, Big Data presents challenges for digital earth to store, transport, process, mine and serve the data. Cloud computing provides fundamental support to address the challenges with shared computing… 
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References

SHOWING 1-10 OF 360 REFERENCES
Big Data Processing in Cloud Computing Environments
TLDR
This paper presents the key issues of big data processing, including cloud computing platform, cloud architecture, cloud database and data storage scheme, and introduces Map Reduce optimization strategies and applications reported in the literature.
The rise of "big data" on cloud computing: Review and open research issues
New Benchmarking Methodology and Programming Model for Big Data Processing
TLDR
This paper presents one possible solution for the coming exascale big data processing: a data flow computing concept, which integrates the performance issues of speed, area, and power needed to execute the task.
Big data and cloud computing: current state and future opportunities
TLDR
This tutorial presents an organized picture of the challenges faced by application developers and DBMS designers in developing and deploying internet scale applications, and crystallizes the design choices made by some successful systems large scale database management systems, analyze the application demands and access patterns, and enumerate the desiderata for a cloud-bound DBMS.
Contemporary Computing Technologies for Processing Big Spatiotemporal Data
TLDR
Modern computing technologies required for processing big data are introduced, including sensor web, Earth observations, and model simulations for collecting and generating big data, and how these cutting-edge computing technologies are utilized to handle big spatiotemporal data are discussed.
Spatial cloud computing: how can the geospatial sciences use and help shape cloud computing?
TLDR
The utilization of cloud computing to support the intensities of geospatial sciences is discussed by reporting from investigations on how cloud computing could enable the geosphere sciences and how spatiotemporal principles, the kernel of the geosp spatial sciences, could be utilized to ensure the benefits of cloud Computing.
Big Data Analytics for Earth Sciences: the EarthServer approach
TLDR
The EarthServer Big Earth Data Analytics engine offers a solution for coverage-type datasets, built around a high performance array database technology, and the adoption and enhancement of standards for service interaction (OGC WCS and WCPS).
Parallel Programming Paradigms and Frameworks in Big Data Era
  • C. Dobre, F. Xhafa
  • Computer Science
    International Journal of Parallel Programming
  • 2013
TLDR
This paper discusses and analyzes opportunities and challenges for efficient parallel data processing, and reviews various parallel and distributed programming paradigms, analyzing how they fit into the Big Data era, and present modern emerging paradigm and frameworks.
Big Data computing and clouds: Trends and future directions
Moving Big Data to The Cloud: An Online Cost-Minimizing Approach
TLDR
This work studies timely, cost-minimizing upload of massive, dynamically-generated, geo-dispersed data into the cloud, for processing using a MapReduce-like framework, and proposes two online algorithms: an online lazy migration (OLM) algorithm and a randomized fixed horizon control (RFHC) algorithm.
...
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5
...