A general framework for big data knowledge discovery and integration

In this paper, context awareness is a promising technology that provides health care services and a niche area of big data paradigm. The drift in Knowledge Discovery from Data refers to a set of activities designed to refine and extract new knowledge from complex datasets. The proposed model facilitates a parallel mining of frequent item sets for Ambient Assisted Living (AAL) System [a.k.a. Health Care [System] of big data that reside inside a cloud environment. We extend a knowledge discovery framework for processing and classifying the abnormal conditions of patients having fluctuations in Blood Pressure (BP) and Heart Rate(HR) and storing this data sets called Big data into Cloud to access from anywhere when needed. This accessed data is used to compare the new data with it, which helps to know the patients health condition.

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Advanced knowledge discovery techniques from Big Data and Cloud Computing

Enterprise Information Systems ◽

10.1080/17517575.2016.1198965 ◽

2016 ◽

Vol 10 (9) ◽

pp. 945-946 ◽

Cited By ~ 4

Author(s):

Fatos Xhafa

Keyword(s):

Cloud Computing ◽

Big Data ◽

Knowledge Discovery ◽

Advanced Knowledge

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Big Data in Asset Management: Knowledge Discovery in Asset Data by the Means of Data Mining

Proceedings of the 10th World Congress on Engineering Asset Management (WCEAM 2015) - Lecture Notes in Mechanical Engineering ◽

10.1007/978-3-319-27064-7_16 ◽

2016 ◽

pp. 161-171 ◽

Cited By ~ 3

Author(s):

Diego Galar ◽

Mirka Kans ◽

Bernard Schmidt

Keyword(s):

Data Mining ◽

Big Data ◽

Knowledge Discovery ◽

Asset Management ◽

Management Knowledge ◽

Asset Data

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Practice on Big Data Platform for Subject Knowledge Discovery in Stem Cell

China’s e-Science Blue Book 2020 ◽

10.1007/978-981-15-8342-1_22 ◽

2021 ◽

pp. 379-398

Author(s):

Zhiqiang Zhang ◽

Zhengyin Hu ◽

Ning Yang ◽

Yi Wen ◽

Xiaochu Qin ◽

...

Keyword(s):

Big Data ◽

Stem Cell ◽

Knowledge Discovery ◽

Subject Knowledge ◽

Data Platform

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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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Effective Statistical Methods for Big Data Analytics

Handbook of Research on Applied Cybernetics and Systems Science - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-2498-4.ch014 ◽

2017 ◽

pp. 280-299 ◽

Cited By ~ 3

Author(s):

Cheng Meng ◽

Ye Wang ◽

Xinlian Zhang ◽

Abhyuday Mandal ◽

Wenxuan Zhong ◽

...

Keyword(s):

Decision Making ◽

Big Data ◽

Knowledge Discovery ◽

Statistical Methods ◽

Large Scale ◽

Big Data Analytics ◽

Divide And Conquer ◽

Data Driven ◽

The Past ◽

Large Scale Dataset

With advances in technologies in the past decade, the amount of data generated and recorded has grown enormously in virtually all fields of industry and science. This extraordinary amount of data provides unprecedented opportunities for data-driven decision-making and knowledge discovery. However, the task of analyzing such large-scale dataset poses significant challenges and calls for innovative statistical methods specifically designed for faster speed and higher efficiency. In this chapter, we review currently available methods for big data, with a focus on the subsampling methods using statistical leveraging and divide and conquer methods.

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