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

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.

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

Web Services ◽  
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.

scholarly journals Data Lake Ecosystem Workflow

2021 ◽  
Author(s):  
R. Salter ◽  
Quyen Dong ◽  
Cody Coleman ◽  
Maria Seale ◽  
Alicia Ruvinsky ◽  
...  
Keyword(s):  
Big Data ◽  
Language Processing ◽  
Data Analytics ◽  
Large Scale ◽  
Big Data Analytics ◽  
Lake Ecosystem ◽  
Data Governance ◽  
Government Organizations ◽  
Large Scale Data ◽  
Scale Data

The Engineer Research and Development Center, Information Technology Laboratory’s (ERDC-ITL’s) Big Data Analytics team specializes in the analysis of large-scale datasets with capabilities across four research areas that require vast amounts of data to inform and drive analysis: large-scale data governance, deep learning and machine learning, natural language processing, and automated data labeling. Unfortunately, data transfer between government organizations is a complex and time-consuming process requiring coordination of multiple parties across multiple offices and organizations. Past successes in large-scale data analytics have placed a significant demand on ERDC-ITL researchers, highlighting that few individuals fully understand how to successfully transfer data between government organizations; future project success therefore depends on a small group of individuals to efficiently execute a complicated process. The Big Data Analytics team set out to develop a standardized workflow for the transfer of large-scale datasets to ERDC-ITL, in part to educate peers and future collaborators on the process required to transfer datasets between government organizations. Researchers also aim to increase workflow efficiency while protecting data integrity. This report provides an overview of the created Data Lake Ecosystem Workflow by focusing on the six phases required to efficiently transfer large datasets to supercomputing resources located at ERDC-ITL.

Affordances of Data Science in Agriculture, Manufacturing, and Education

Web Services ◽  
2019 ◽  
pp. 953-978
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.

Affordances of Data Science in Agriculture, Manufacturing, and Education

2017 ◽  
pp. 14-40 ◽  
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.

scholarly journals Analyzing Big Data with the Hybrid Interval Regression Methods

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
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.

Big data breaches and customer compensation strategies

2017 ◽  
Vol 37 (1) ◽  
pp. 56-74 ◽  
Author(s):  
Thomas Kude ◽  
Hartmut Hoehle ◽  
Tracy Ann Sykes
Keyword(s):  
Big Data ◽  
Personality Traits ◽  
Data Analytics ◽  
Large Scale ◽  
Big Data Analytics ◽  
Content Type ◽  
Customer Data ◽  
Data Breaches ◽  
Large Scale Data ◽  
Scale Data

Purpose Big Data Analytics provides a multitude of opportunities for organizations to improve service operations, but it also increases the threat of external parties gaining unauthorized access to sensitive customer data. With data breaches now a common occurrence, it is becoming increasingly plain that while modern organizations need to put into place measures to try to prevent breaches, they must also put into place processes to deal with a breach once it occurs. Prior research on information technology security and services failures suggests that customer compensation can potentially restore customer sentiment after such data breaches. The paper aims to discuss these issues. Design/methodology/approach In this study, the authors draw on the literature on personality traits and social influence to better understand the antecedents of perceived compensation and the effectiveness of compensation strategies. The authors studied the propositions using data collected in the context of Target’s large-scale data breach that occurred in December 2013 and affected the personal data of more than 70 million customers. In total, the authors collected data from 212 breached customers. Findings The results show that customers’ personality traits and their social environment significantly influences their perceptions of compensation. The authors also found that perceived compensation positively influences service recovery and customer experience. Originality/value The results add to the emerging literature on Big Data Analytics and will help organizations to more effectively manage compensation strategies in large-scale data breaches.

scholarly journals Explanations as Discourse

2020 ◽  
Vol 24 ◽  
Author(s):  
Sadaf Afrashteh ◽  
Ida Someh ◽  
Michael Davern
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Large Scale ◽  
Big Data Analytics ◽  
Customer Engagement ◽  
Data Sets ◽  
Research Directions ◽  
Large Scale Data ◽  
Scale Data

Big data analytics uses algorithms for decision-making and targeting of customers. These algorithms process large-scale data sets and create efficiencies in the decision-making process for organizations but are often incomprehensible to customers and inherently opaque in nature. Recent European Union regulations require that organizations communicate meaningful information to customers on the use of algorithms and the reasons behind decisions made about them. In this paper, we explore the use of explanations in big data analytics services. We rely on discourse ethics to argue that explanations can facilitate a balanced communication between organizations and customers, leading to transparency and trust for customers as well as customer engagement and reduced reputation risks for organizations. We conclude the paper by proposing future empirical research directions.

scholarly journals Assessment of the Awareness of Nigerian Professionals in the Built Environment on the Big Data analytics (BDA) Applications in the Construction Industry.

2021 ◽  
pp. 1-7
Author(s):  
Emmanuel Jesse Amadosi
Keyword(s):  
Big Data ◽  
Built Environment ◽  
Data Analytics ◽  
Large Scale ◽  
Rapid Development ◽  
Big Data Analytics ◽  
Strong Relationship ◽  
Large Scale Data ◽  
Scale Data ◽  
Structured Questionnaire

With rapid development in technology, the built industry’s capacity to generate large-scale data is not in doubt. This trend of data upsurge labelled “Big Data” is currently being used to seek intelligent solutions in many industries including construction. As a result of this, the appeal to embrace Big Data Analytics has also gained wide advocacy globally. However, the general knowledge of Nigeria’s built environment professionals on Big Data Analytics is still limited and this gap continues to account for the slow pace of adoption of digital technologies like Big Data Analytics and the value it projects. This study set out to assess the level of awareness and knowledge of professionals within the Nigerian built environment with a view to promoting the adoption of Big Data Analytics for improved productivity. To achieve this aim, a structured questionnaire survey was carried out among a total of 283 professionals drawn from 9 disciplines within the built environment in the Federal Capital Territory, Abuja. The findings revealed that: a) a low knowledge level of Big Data exists among professionals, b) knowledge among professional and the level of Big Data Analytics application have strong relationship c) professional are interested in knowing more about the Big Data concept and how Big Data Analytics can be leveraged upon. The study, therefore recommends an urgent paradigm shift towards digitisation to fully embrace and adopt Big Data Analytics and enjoin stakeholders to promote collaborative schemes among practice-based professionals and the academia in seeking intelligent and smart solutions to construction-related problems.

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