Big Data Analytics

2017 ◽  
Vol 11 (2) ◽  
pp. 41-56 ◽  
Author(s):  
Yingxu Wang ◽  
Jun Peng
Keyword(s):  
Big Data ◽  
Cognitive Processes ◽  
Data Analytics ◽  
Data Science ◽  
Computational Models ◽  
Big Data Analytics ◽  
Data Systems ◽  
Data Engineering ◽  
Big Data Systems ◽  
Cognitive Foundations

Big data are pervasively generated by human cognitive processes, formal inferences, and system quantifications. This paper presents the cognitive foundations of big data systems towards big data science. The key perceptual model of big data systems is the recursively typed hyperstructure (RTHS). The RTHS model reveals the inherited complexities and unprecedented difficulty in big data engineering. This finding leads to a set of mathematical and computational models for efficiently processing big data systems. The cognitive relationship between data, information, knowledge, and intelligence is formally described.

Big Data Analytics in Supply Chain Management

2021 ◽  
pp. 2443-2457
Author(s):  
Nenad Stefanovic
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Current Approach ◽  
Data Systems ◽  
Chain Management ◽  
Big Data Systems

The current approach to supply chain intelligence has some fundamental challenges when confronted with the scale and characteristics of big data. In this chapter, applications, challenges and new trends in supply chain big data analytics are discussed and background research of big data initiatives related to supply chain management is provided. The methodology and the unified model for supply chain big data analytics which comprises the whole business intelligence (data science) lifecycle is described. It enables creation of the next-generation cloud-based big data systems that can create strategic value and improve performance of supply chains. Finally, example of supply chain big data solution that illustrates applicability and effectiveness of the model is presented.

Big Data Analytics in Supply Chain Management

2022 ◽  
pp. 1801-1816
Author(s):  
Nenad Stefanovic
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Current Approach ◽  
Data Systems ◽  
Chain Management ◽  
Big Data Systems

The current approach to supply chain intelligence has some fundamental challenges when confronted with the scale and characteristics of big data. In this chapter, applications, challenges and new trends in supply chain big data analytics are discussed and background research of big data initiatives related to supply chain management is provided. The methodology and the unified model for supply chain big data analytics which comprises the whole business intelligence (data science) lifecycle is described. It enables creation of the next-generation cloud-based big data systems that can create strategic value and improve performance of supply chains. Finally, example of supply chain big data solution that illustrates applicability and effectiveness of the model is presented.

scholarly journals A System for Evaluating Big Data Analytics Performance

2019 ◽  
Vol 9 (1) ◽  
pp. 1679-1683
Keyword(s):  
Big Data ◽  
Subject Matter ◽  
Data Analytics ◽  
Development Process ◽  
Big Data Analytics ◽  
Prototype System ◽  
Data Systems ◽  
Subject Matter Experts ◽  
Big Data Systems ◽  
Performance Gains

Despite of advances in systems and practices, performancemeasurement remains instrumental in helping organization discoverexistingproblems and propose viable solutions. On big data Analytics (BDA), more efforts are being focused, butperformance side is still a room for improvement. This paper discusses the development and implementation of performance measurement prototype system for big data systems. With this system, organizations can continually assess the performance gains and setbacks of their big data systems. The systems were developed based on measures and metrics retrieved from the extant literature. Then it was evaluated through review of subject-matter experts and usability survey. The development process of the prototype and the results of the evaluation are presented in this paper.

scholarly journals Infrastructure for Time Critical Applications of Big Data Systems Using Light Weight YARN Architecture

2021 ◽  
Author(s):  
Saravanan A.M. ◽  
K. Loheswaran ◽  
G. Naga Rama Devi ◽  
Karuppathal R ◽  
C Balakrishnan ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Systems ◽  
Computational Framework ◽  
Internet Resources ◽  
Big Data Systems ◽  
Hadoop Cluster ◽  
Almost All ◽  
Time Critical

Abstract Increasing of humanity and development of Internet resources, storage size is growing with each day, whereby digital records are accessible in clouds of an exploratory format. The immediate future of Big Data is coming shortly for almost all other sectors. Big data can aid in the metamorphosis of significant company operations by offering a recommended and reliable overview of available data. Big data has also figured prominently in the detection of violence. Present framework for designing Big data implementations is capable of processing vast quantities of data through Big data analytics using collections of computing devices together to execute complex processing. Furthermore, existing technologies have not been built to fulfil the specifications of time-critical application areas and are far more oriented on real applications than on time-critical ones. This paper proposes the lightweight architecture called Yet Another Resource Negotiator (YARN), which focuses on the concept of a time-critical big-data system from the perspective of specifications and analyses the essential principles of several common big-data implementations. YARN as the normal computational framework to help MapReduce and another application instances within that Hadoop cluster. YARN requires multiple programs to execute concurrently on a constitutive common server and assent programs to delegate services depending on need. The final evaluation is accompanied by problems stemming from infrastructure and services that serve applications, recommend frameworkand provide preliminary efficiency behaviours that often contribute system impacts to implementation reliability.

Human-Computer Interaction With Big Data Analytics

2022 ◽  
pp. 1578-1596
Author(s):  
Gunasekaran Manogaran ◽  
Chandu Thota ◽  
Daphne Lopez
Keyword(s):  
Big Data ◽  
Human Computer Interaction ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Vital Role ◽  
Data Systems ◽  
Real Time Analysis ◽  
Big Data Systems ◽  
Almost All ◽  
Computer Interaction

Big Data has been playing a vital role in almost all environments such as healthcare, education, business organizations and scientific research. Big data analytics requires advanced tools and techniques to store, process and analyze the huge volume of data. Big data consists of huge unstructured data that require advance real-time analysis. Thus, nowadays many of the researchers are interested in developing advance technologies and algorithms to solve the issues when dealing with big data. Big Data has gained much attention from many private organizations, public sector and research institutes. This chapter provides an overview of the state-of-the-art algorithms for processing big data, as well as the characteristics, applications, opportunities and challenges of big data systems. This chapter also presents the challenges and issues in human computer interaction with big data analytics.

On the Cognitive and Theoretical Foundations of Big Data Science and Engineering

2017 ◽  
Vol 13 (02) ◽  
pp. 101-117 ◽  
Author(s):  
Yingxu Wang
Keyword(s):  
Big Data ◽  
Data Science ◽  
Recursive Formula ◽  
Significant Finding ◽  
Data Systems ◽  
Basic Study ◽  
Wide Range ◽  
Big Data Systems ◽  
Cognitive Foundations ◽  
Theoretical Foundations

Big data play an indispensable role not only in the cognitive mechanisms of human sensation, quantification, qualification, estimation, memory, and reasoning, but also in a wide range of engineering applications. A basic study on the theoretical foundations of big data science is presented with a coherent set of general principles and analytic methodologies for big data systems. Cognitive foundations of big data are explored in order to formally explain the origination and nature of big data. A set of mathematical models of big data are created that rigorously elicit the general essences and patterns of big data across pervasive domains in sciences, engineering, and societies. A significant finding towards big data science is that big data systems in nature are a recursive [Formula: see text]-dimensional-typed hyperstructure (RNTHS) rather than pure numbers. The fundamental topological property of big data reveals a set of denotational mathematical solutions for dealing with inherited complexities and unprecedented challenges in big data engineering.

Human-Computer Interaction With Big Data Analytics

2018 ◽  
pp. 1-22 ◽  
Author(s):  
Gunasekaran Manogaran ◽  
Chandu Thota ◽  
Daphne Lopez
Keyword(s):  
Big Data ◽  
Human Computer Interaction ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Vital Role ◽  
Data Systems ◽  
Real Time Analysis ◽  
Big Data Systems ◽  
Almost All ◽  
Computer Interaction

Big Data has been playing a vital role in almost all environments such as healthcare, education, business organizations and scientific research. Big data analytics requires advanced tools and techniques to store, process and analyze the huge volume of data. Big data consists of huge unstructured data that require advance real-time analysis. Thus, nowadays many of the researchers are interested in developing advance technologies and algorithms to solve the issues when dealing with big data. Big Data has gained much attention from many private organizations, public sector and research institutes. This chapter provides an overview of the state-of-the-art algorithms for processing big data, as well as the characteristics, applications, opportunities and challenges of big data systems. This chapter also presents the challenges and issues in human computer interaction with big data analytics.

Big Data Analytics in Mobile and Cloud Computing Environments

2017 ◽  
pp. 1478-1496 ◽  
Author(s):  
Muhammad Habib ur Rehman ◽  
Atta ur Rehman Khan ◽  
Aisha Batool
Keyword(s):  
Big Data ◽  
Mobile Devices ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Future Application ◽  
Data Systems ◽  
Research Issues ◽  
Mobile Data ◽  
Cloud Environments ◽  
Big Data Systems

Multiple properties of big mobile data, namely volume, velocity, variety, and veracity make the big data analytics process a challenging task. It is desired that mobile devices initially process big data before sending it to big data systems to reduce the data complexity. However, the mobile devices have recourse constraints, and the challenge of processing big mobile data on mobile devices requires further exploration. This chapter presents a thorough discussion about mobile computing systems and their implication for big data analytics. It presents big data analytics with different perspectives involving descriptive, predictive, and prescriptive analytical methods. Moreover, the chapter presents a detailed literature review on mobile and cloud based big data analytics systems, and highlights the future application areas and open research issues that are relevant to big data analytics in mobile cloud environments. Lastly, the chapter provides some recommendations regarding big data processing, quality improvement, and complexity optimization.

Big Data Analytics in Mobile and Cloud Computing Environments

2016 ◽  
pp. 349-367 ◽  
Author(s):  
Muhammad Habib ur Rehman ◽  
Atta ur Rehman Khan ◽  
Aisha Batool
Keyword(s):  
Big Data ◽  
Mobile Devices ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Future Application ◽  
Data Systems ◽  
Research Issues ◽  
Mobile Data ◽  
Cloud Environments ◽  
Big Data Systems

Multiple properties of big mobile data, namely volume, velocity, variety, and veracity make the big data analytics process a challenging task. It is desired that mobile devices initially process big data before sending it to big data systems to reduce the data complexity. However, the mobile devices have recourse constraints, and the challenge of processing big mobile data on mobile devices requires further exploration. This chapter presents a thorough discussion about mobile computing systems and their implication for big data analytics. It presents big data analytics with different perspectives involving descriptive, predictive, and prescriptive analytical methods. Moreover, the chapter presents a detailed literature review on mobile and cloud based big data analytics systems, and highlights the future application areas and open research issues that are relevant to big data analytics in mobile cloud environments. Lastly, the chapter provides some recommendations regarding big data processing, quality improvement, and complexity optimization.

Data Science and Big Data Analytics

2020 ◽  
pp. 129-150
Author(s):  
Ananta Charan Ojha ◽  
Subhendu Kumar Pani
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics
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