Awareness of Big Data Usage and Applications Among Librarians in Zimbabwe

2021 ◽  
pp. 22-34
Author(s):  
Josiline Phiri Chigwada ◽  
Justice Kasiroori
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Qualitative Study ◽  
Capacity Building ◽  
Research Institutions ◽  
Online Questionnaire ◽  
Data Usage ◽  
Current Trends ◽  
Big Data Applications ◽  
Tools And Techniques

The chapter showcases the awareness of big data usage among librarians in Zimbabwe. The concept of big data is new, and librarians are building capacity to move with the current trends in librarianship. This chapter assists in pointing areas where big data can be applied in libraries. It also documents the challenges that are faced when using big data applications and proffer solutions that can be applied to deal with those challenges. It answers the question of whether it is practical to utilise big data in any type of library. A qualitative study was done where an online questionnaire was administered to twenty librarians in research institutions in Zimbabwe. The findings revealed that librarians are aware of the big data concept but are not utilising the tools and techniques in data mining and analysis. The authors recommend that capacity building should be done to equip librarians with the requisite skills.

scholarly journals Novel IT Technologies on the Digital Battlefield: The Application of Big Data and Data Mining Technologies

Hadmérnök ◽  
2020 ◽  
Vol 15 (4) ◽  
pp. 141-158
Author(s):  
Eszter Katalin Bognár
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Processing ◽  
Relevant Information ◽  
Military Operations ◽  
Textual Data ◽  
Modern Warfare ◽  
Tools And Techniques

In modern warfare, the most important innovation to date has been the utilisation of information as a  weapon. The basis of successful military operations is  the ability to correctly assess a situation based on  credible collected information. In today’s military, the primary challenge is not the actual collection of data.  It has become more important to extract relevant  information from that data. This requirement cannot  be successfully completed without necessary  improvements in tools and techniques to support the acquisition and analysis of data. This study defines  Big Data and its concept as applied to military  reconnaissance, focusing on the processing of  imagery and textual data, bringing to light modern  data processing and analytics methods that enable  effective processing.

Big Data Applications in Healthcare

Big Data ◽  
2016 ◽  
pp. 1247-1259 ◽  
Author(s):  
Jayanthi Ranjan
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Industry ◽  
Big Data Applications ◽  
Business Functions ◽  
Automated Algorithms ◽  
Almost All ◽  
Tools And Techniques ◽  
Research Centres

Big data is in every industry. It is being utilized in almost all business functions within these industries. Basically, it creates value by converting human decisions into transformed automated algorithms using various tools and techniques. In this chapter, the authors look towards big data analytics from the healthcare perspective. Healthcare involves the whole supply chain of industries from the pharmaceutical companies to the clinical research centres, from the hospitals to individual physicians, and anyone who is involved in the medical arena right from the supplier to the consumer (i.e. the patient). The authors explore the growth of big data analytics in the healthcare industry including its limitations and potential.

A Review of Different Data Mining Techniques Used in Big Data Applications

2021 ◽  
pp. 59-89
Author(s):  
Chandrakanta Mahanty ◽  
Devpriya Panda ◽  
Brojo Kishore Mishra
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Mining Techniques ◽  
Big Data Applications

Big Data Mining

2018 ◽  
pp. 36-42
Author(s):  
V. Sucharita ◽  
P. Venkateswara Rao ◽  
A. Satya Kalyan ◽  
P. Rajarajeswari
Keyword(s):  
Data Mining ◽  
Big Data ◽  
The Self ◽  
Information Mining ◽  
Error Handling ◽  
Big Data Mining ◽  
Accessible Information ◽  
Tools And Techniques ◽  
Information Streams ◽  
Mining Tools

At present in Big Data era mining of Big Data can help us find learning which nobody has possessed the capacity to find some time recently. There is a developing interest for tools and techniques which can prepare and investigate Big Data effectively and proficiently. In this chapter, the accessible information mining tools and techniques which can deal with Big Data have been abridged. This paper additionally concentrates on tools and techniques for mining of data and information streams. Through better analysis of the vast volumes of information that are getting to be accessible, there is the potential for making speedier progresses in numerous scientific areas what's more, making strides the productivity what's more, victory of numerous organizations. The challenges incorporate not just the self-evident issues of scale, be that as it may too heterogeneity, need of structure, error handling, protection, opportunities at all stages of the analysis from acquisition of data to obtaining to result.

Mining Big Data in Manufacturing: Requirement Analysis, Tools and Techniques

2014 ◽  
Author(s):  
Utpal Roy ◽  
Bicheng Zhu ◽  
Yunpeng Li ◽  
Heng Zhang ◽  
Omer Yaman
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Product Design ◽  
Literature Study ◽  
Data Mining Techniques ◽  
Using Data ◽  
Tools And Techniques ◽  
Audio Video ◽  
Manufacturing Problems ◽  
Selection Of

Data Mining has tremendous potential and usefulness in improving the effectiveness of decision-making in manufacturing. Tools and techniques of data mining can be intelligently applied from product design analysis to the product repair and maintenance. Vast amount of data in the form of documents (text), graphical formats (CAD-file), audio/video, numbers, figures and/or hypertext are available in any typical manufacturing system. Our ultimate goal is to develop data-driven methodologies to solve manufacturing problems using data mining techniques. As a precursor, based on a literature study, this paper investigates selective manufacturing areas to identify the requirements for applying data mining techniques in solving potential manufacturing problems. The reviewed manufacturing areas are: (i) the “Design Intent” retrieval process for the product design and manufacturing, (ii) selection of materials, (iii) performance evaluations of manufacturing process design and operation management, and (iv) product inspection, and after-sales services (repair and maintenance). Industrial efforts towards addressing “Big Data” issues have also been briefly narrated in this paper. Lastly, the paper discusses two important data–related issues that may affect any applications of the data mining tools and techniques — (i) uncertainty involved in data collection, and (ii) interoperability of data collected at different levels of an enterprise.

scholarly journals Outlier Detection in Big Data

2014 ◽  
pp. 1762-1771 ◽  
Author(s):  
Victoria J. Hodge
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Outlier Detection ◽  
Cost Savings ◽  
Detection Methods ◽  
Complex Data ◽  
Research Directions ◽  
Research Issues ◽  
Big Data Applications ◽  
Time Savings

Outlier detection (or anomaly detection) is a fundamental task in data mining. Outliers are data that deviate from the norm and outlier detection is often compared to “finding a needle in a haystack.” However, the outliers may generate high value if they are found, value in terms of cost savings, improved efficiency, compute time savings, fraud reduction and failure prevention. Detection can identify faults before they escalate with potentially catastrophic consequences. Big Data refers to large, dynamic collections of data. These vast and complex data appear problematic for traditional outlier detection methods to process but, Big Data provides considerable opportunity to uncover new outliers and data relationships. This chapter highlights some of the research issues for outlier detection in Big Data and covers the solutions used and research directions taken along with an analysis of some current outlier detection approaches for Big Data applications.

scholarly journals Predicting Big Data Adoption in Companies With an Explanatory and Predictive Model

2021 ◽  
Vol 12 ◽  
Author(s):  
Ángel F. Villarejo-Ramos ◽  
Juan-Pedro Cabrera-Sánchez ◽  
Juan Lara-Rubio ◽  
Francisco Liébana-Cabanillas
Keyword(s):  
Big Data ◽  
Perceived Risk ◽  
Confusion Matrix ◽  
Theory And Practice ◽  
Intention To Use ◽  
Review Of The Literature ◽  
Utaut Model ◽  
Data Usage ◽  
Big Data Applications ◽  
Starting Model

The purpose of this paper is to identify the factors that affect the intention to use Big Data Applications in companies. Research into Big Data usage intention and adoption is scarce and much less from the perspective of the use of these techniques in companies. That is why this research focuses on analyzing the adoption of Big Data Applications by companies. Further to a review of the literature, it is proposed to use a UTAUT model as a starting model with the update and incorporation of other variables such as resistance to use and perceived risk, and then to perform a neural network to predict this adoption. With respect to this non-parametric technique, we found that the multilayer perceptron model (MLP) for the use of Big Data Applications in companies obtains higher AUC values, and a better confusion matrix. This paper is a pioneering study using this hybrid methodology on the intention to use Big Data Applications. The result of this research has important implications for the theory and practice of adopting Big Data Applications.

Big Data Applications in Healthcare

2014 ◽  
pp. 202-214 ◽  
Author(s):  
Jayanthi Ranjan
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Industry ◽  
Big Data Applications ◽  
Business Functions ◽  
Automated Algorithms ◽  
Almost All ◽  
Tools And Techniques ◽  
Research Centres

Big data is in every industry. It is being utilized in almost all business functions within these industries. Basically, it creates value by converting human decisions into transformed automated algorithms using various tools and techniques. In this chapter, the authors look towards big data analytics from the healthcare perspective. Healthcare involves the whole supply chain of industries from the pharmaceutical companies to the clinical research centres, from the hospitals to individual physicians, and anyone who is involved in the medical arena right from the supplier to the consumer (i.e. the patient). The authors explore the growth of big data analytics in the healthcare industry including its limitations and potential.

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