Intelligent method for adaptive in silico knowledge discovery based on big genomic data analytics

Software architecture for adaptive in silico knowledge discovery and decision making based on big genomic data analytics

10.1063/1.5133586 ◽

2019 ◽

Author(s):

Veska Gancheva ◽

Ivailo Georgiev

Keyword(s):

Decision Making ◽

Software Architecture ◽

Knowledge Discovery ◽

In Silico ◽

Data Analytics ◽

Genomic Data

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SOA Based System for Big Genomic Data Analytics and Knowledge Discovery

2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) ◽

10.1109/idaacs.2019.8924370 ◽

2019 ◽

Cited By ~ 1

Author(s):

Veska Gancheva ◽

Plamenka Borovska

Keyword(s):

Knowledge Discovery ◽

Data Analytics ◽

Genomic Data

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Accelerating Genomic Data Analytics with Composable Hardware Acceleration Framework

IEEE Micro ◽

10.1109/mm.2021.3072385 ◽

2021 ◽

pp. 1-1

Author(s):

Tae Jun Ham ◽

David Bruns-Smith ◽

Brendan Sweeney ◽

Yejin Lee ◽

Seong Hoon Seo ◽

...

Keyword(s):

Data Analytics ◽

Genomic Data ◽

Hardware Acceleration

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Problem Formulation in Knowledge Discovery via Data Analytics (KDDA) for Environmental Risk Management

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph13121245 ◽

2016 ◽

Vol 13 (12) ◽

pp. 1245

Author(s):

Yan Li ◽

Manoj Thomas ◽

Kweku-Muata Osei-Bryson ◽

Jason Levy

Keyword(s):

Risk Management ◽

Knowledge Discovery ◽

Environmental Risk ◽

Data Analytics ◽

Problem Formulation ◽

Environmental Risk Management

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Descriptive and Predictive Analytical Methods for Big Data

Web Services ◽

10.4018/978-1-5225-7501-6.ch018 ◽

2019 ◽

pp. 314-331 ◽

Cited By ~ 1

Author(s):

Sema A. Kalaian ◽

Rafa M. Kasim ◽

Nabeel R. Kasim

Keyword(s):

Big Data ◽

Standard Deviation ◽

Linear Regression ◽

Multiple Linear Regression ◽

Knowledge Discovery ◽

Data Visualization ◽

Analytical Methods ◽

Data Analytics ◽

Enterprise Performance ◽

Analytical Tools

Data analytics and modeling are powerful analytical tools for knowledge discovery through examining and capturing the complex and hidden relationships and patterns among the quantitative variables in the existing massive structured Big Data in efforts to predict future enterprise performance. The main purpose of this chapter is to present a conceptual and practical overview of some of the basic and advanced analytical tools for analyzing structured Big Data. The chapter covers descriptive and predictive analytical methods. Descriptive analytical tools such as mean, median, mode, variance, standard deviation, and data visualization methods (e.g., histograms, line charts) are covered. Predictive analytical tools for analyzing Big Data such as correlation, simple- and multiple- linear regression are also covered in the chapter.

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