Survey of Large-Scale Data Management Systems for Big Data Applications

Continued technological advancements of the 21st Century afford massive data generation in sectors of our economy to include the domains of agriculture, manufacturing, and education. However, harnessing such large-scale data, using modern technologies for effective decision-making appears to be an evolving science that requires knowledge of Big Data management and analytics. Big data in agriculture, manufacturing, and education are varied such as voluminous text, images, and graphs. Applying Big data science techniques (e.g., functional algorithms) for extracting intelligence data affords decision markers quick response to productivity, market resilience, and student enrollment challenges in today's unpredictable markets. This chapter serves to employ data science for potential solutions to Big Data applications in the sectors of agriculture, manufacturing and education to a lesser extent, using modern technological tools such as Hadoop, Hive, Sqoop, and MongoDB.

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Affordances of Data Science in Agriculture, Manufacturing, and Education

Privacy and Security Policies in Big Data - Advances in Information Security, Privacy, and Ethics ◽

10.4018/978-1-5225-2486-1.ch002 ◽

2017 ◽

pp. 14-40 ◽

Cited By ~ 2

Author(s):

Krishnan Umachandran ◽

Debra Sharon Ferdinand-James

Keyword(s):

Big Data ◽

Large Scale ◽

Data Science ◽

Data Generation ◽

Large Scale Data ◽

Big Data Applications ◽

Effective Decision ◽

Effective Decision Making ◽

Text Images ◽

Scale Data

Continued technological advancements of the 21st Century afford massive data generation in sectors of our economy to include the domains of agriculture, manufacturing, and education. However, harnessing such large-scale data, using modern technologies for effective decision-making appears to be an evolving science that requires knowledge of Big Data management and analytics. Big data in agriculture, manufacturing, and education are varied such as voluminous text, images, and graphs. Applying Big data science techniques (e.g., functional algorithms) for extracting intelligence data affords decision markers quick response to productivity, market resilience, and student enrollment challenges in today's unpredictable markets. This chapter serves to employ data science for potential solutions to Big Data applications in the sectors of agriculture, manufacturing and education to a lesser extent, using modern technological tools such as Hadoop, Hive, Sqoop, and MongoDB.

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Analyzing Big Data with the Hybrid Interval Regression Methods

The Scientific World JOURNAL ◽

10.1155/2014/243921 ◽

2014 ◽

Vol 2014 ◽

pp. 1-8 ◽

Cited By ~ 1

Author(s):

Chia-Hui Huang ◽

Keng-Chieh Yang ◽

Han-Ying Kao

Keyword(s):

Support Vector Machine ◽

Big Data ◽

Information Technologies ◽

Large Scale ◽

Cloud Services ◽

Support Vector ◽

Large Scale Data ◽

Big Data Applications ◽

Smooth Support Vector Machine ◽

Scale Data

Big data is a new trend at present, forcing the significant impacts on information technologies. In big data applications, one of the most concerned issues is dealing with large-scale data sets that often require computation resources provided by public cloud services. How to analyze big data efficiently becomes a big challenge. In this paper, we collaborate interval regression with the smooth support vector machine (SSVM) to analyze big data. Recently, the smooth support vector machine (SSVM) was proposed as an alternative of the standard SVM that has been proved more efficient than the traditional SVM in processing large-scale data. In addition the soft margin method is proposed to modify the excursion of separation margin and to be effective in the gray zone that the distribution of data becomes hard to be described and the separation margin between classes.

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A Survey of Cloud-Based Services Leveraged by Big Data Applications

Web Services ◽

10.4018/978-1-5225-7501-6.ch088 ◽

2019 ◽

pp. 1706-1716

Author(s):

S. ZerAfshan Goher ◽

Barkha Javed ◽

Peter Bloodsworth

Keyword(s):

Big Data ◽

Data Storage ◽

Data Analytics ◽

Large Scale ◽

Future Trends ◽

Advantages And Disadvantages ◽

Large Scale Data ◽

Big Data Applications ◽

Big Data Storage ◽

Scale Data

Due to the growing interest in harnessing the hidden significance of data, more and more enterprises are moving to data analytics. Data analytics require the analysis and management of large-scale data to find the hidden patterns among various data components to gain useful insight. The derived information is then used to predict the future trends that can be advantageous for a business to flourish such as customers' likes/dislikes, reasons behind customers' churn and more. In this paper, several techniques for the big data analysis have been investigated along with their advantages and disadvantages. The significance of cloud computing for big data storage has also been discussed. Finally, the techniques to make the robust and efficient usage of big data have also been discussed.

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A Survey of Cloud-Based Services Leveraged by Big Data Applications

Advances in Data Mining and Database Management - Managing and Processing Big Data in Cloud Computing ◽

10.4018/978-1-4666-9767-6.ch008 ◽

2016 ◽

pp. 121-131

Author(s):

S. ZerAfshan Goher ◽

Barkha Javed ◽

Peter Bloodsworth

Keyword(s):

Big Data ◽

Data Storage ◽

Data Analytics ◽

Large Scale ◽

Future Trends ◽

Advantages And Disadvantages ◽

Large Scale Data ◽

Big Data Applications ◽

Big Data Storage ◽

Scale Data

Due to the growing interest in harnessing the hidden significance of data, more and more enterprises are moving to data analytics. Data analytics require the analysis and management of large-scale data to find the hidden patterns among various data components to gain useful insight. The derived information is then used to predict the future trends that can be advantageous for a business to flourish such as customers' likes/dislikes, reasons behind customers' churn and more. In this paper, several techniques for the big data analysis have been investigated along with their advantages and disadvantages. The significance of cloud computing for big data storage has also been discussed. Finally, the techniques to make the robust and efficient usage of big data have also been discussed.

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Data Modeling for Big Data

Jurnal ULTIMA InfoSys ◽

10.31937/si.v6i1.273 ◽

2015 ◽

Vol 6 (1) ◽

pp. 1-11 ◽

Cited By ~ 2

Author(s):

M Misbachul Huda ◽

Dian Rahma Latifa Hayun ◽

Zhin Martun

Keyword(s):

Big Data ◽

Data Management ◽

Relational Database ◽

Management System ◽

Database Management ◽

Large Scale ◽

Database Management System ◽

Large Scale Data ◽

Relational Database Management ◽

Scale Data

Today the rapid growth of the internet and the massive usage of the data have led to the increasing CPU requirement, velocity for recalling data, a schema for more complex data structure management, the reliability and the integrity of the available data. This kind of data is called as Large-scale Data or Big Data. Big Data demands high volume, high velocity, high veracity and high variety. Big Data has to deal with two key issues, the growing size of the datasets and the increasing of data complexity. To overcome these issues, today researches are devoted to kind of database management system that can be optimally used for big data management. There are two kinds of database management system, relational database management system and nonrelational system that can be optimally used for big data management. There are two kinds of database management, Relational Database Management and Non-relational Database Management. This paper will give reviews about these two database management system, including description, vantage, structure and the application of each DBMS. Index Terms - Big Data, DBMS, Large-scale Data, Non-relational Database, Relational Database.

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Review of Big Data Applications in Finance and Economics

Advances in Data Mining and Database Management - Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics ◽

10.4018/978-1-7998-3053-5.ch010 ◽

2021 ◽

pp. 181-202

Author(s):

Ulkem Basdas ◽

M. Fevzi Esen

Keyword(s):

Big Data ◽

Financial Services ◽

Continuous Flow ◽

Large Scale ◽

Business Processes ◽

Rapid Development ◽

Large Scale Data ◽

Big Data Applications ◽

Scale Data

Massively parallel processors and modern data management architectures have led to more efficient operations and a better decision making for companies to process and analyse such complex and large-scale data. Especially, financial services companies leverage big data to transform their business processes and they focus on understanding the concepts of big data and related technologies. In this chapter, the authors focus on the scope of big data in finance and economics. They discuss the need for big data towards the digitalisation of services, utilisation of social media and new channels to reach customers, demand for personalised services and continuous flow of vast amount of data in the sector. They investigate the role of big data in transformation of financial and economic environment by reviewing previous studies on stock market reading and monitoring (real-time algorithmic trading, high-frequency trading), fraud detection, and risk analysis. They conclude that despite the rapid development in the evolution of techniques, both the performance of techniques and area of implementation are still open to improvement. Therefore, this review aims to encourage readers to enlarge their vision on data mining applications.

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