scholarly journals Navigating the Benford Labyrinth: A big-data analytic protocol illustrated using the academic library context

2016 ◽  
pp. 138-157
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Academic Library ◽  
Academic Research ◽  
Big Data Analytics ◽  
Research Libraries ◽  
Related Data ◽  
Second Stage ◽  
Information Sets ◽  
Data Analytic

Objective: Big Data Analytics is a panoply of techniques the principal intention of which is to ferret out dimensions or factors from certain data streamed or available over the WWW. We offer a subset or “second” stage protocol of Big Data Analytics (BDA) that uses these dimensional datasets as benchmarks for profiling related data. We call this Specific Context Benchmarking (SCB). Method: In effecting this benchmarking objective, we have elected to use a Digital Frequency Profiling (DFP) technique based upon the work of Newcomb and Benford, who have developed a profiling benchmark based upon the Log10 function. We illustrate the various stages of the SCB protocol using the data produced by the Academic Research Libraries to enhance insights regarding the details of the operational benchmarking context and so offer generalizations needed to encourage adoption of SCB across other functional domains. Results: An illustration of the SCB protocol is offered using the recently developed Benford Practical Profile as the Conformity Benchmarking Measure. ShareWare: We have developed a Decision Support System called: SpecificContextAnalytics (SCA:DSS) to create the various information sets presented in this paper. The SCA:DSS, programmed in Excel VBA, is available from the corresponding author as a free download without restriction to its use. Conclusions: We note that SCB effected using the DFPs is an enhancement not a replacement for the usual statistical and analytic techniques and fits very well in the BDA milieu.

Big Data and Big Data Analytics for Improved Healthcare Service and Management

2020 ◽  
Vol 8 (1) ◽  
pp. 13-51
Author(s):  
Pijush Kanti Dutta Pramanik ◽  
Saurabh Pal ◽  
Moutan Mukhopadhyay
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Services ◽  
Healthcare Sector ◽  
Future Market ◽  
Data Generation ◽  
Related Data ◽  
Healthcare Data ◽  
Different Types

Like other fields, the healthcare sector has also been greatly impacted by big data. A huge volume of healthcare data and other related data are being continually generated from diverse sources. Tapping and analysing these data, suitably, would open up new avenues and opportunities for healthcare services. In view of that, this paper aims to present a systematic overview of big data and big data analytics, applicable to modern-day healthcare. Acknowledging the massive upsurge in healthcare data generation, various ‘V's, specific to healthcare big data, are identified. Different types of data analytics, applicable to healthcare, are discussed. Along with presenting the technological backbone of healthcare big data and analytics, the advantages and challenges of healthcare big data are meticulously explained. A brief report on the present and future market of healthcare big data and analytics is also presented. Besides, several applications and use cases are discussed with sufficient details.

Big data adoption in academic libraries: a literature review

2020 ◽  
Vol 37 (4) ◽  
pp. 1-5
Author(s):  
Nove E. Variant Anna ◽  
Endang Fitriyah Mannan
Keyword(s):  
Big Data ◽  
Data Management ◽  
Academic Libraries ◽  
Data Analytics ◽  
Academic Library ◽  
Big Data Analytics ◽  
Research Data ◽  
Management Policy ◽  
Content Type ◽  
Scopus Database

Purpose The purpose of this paper is to analyse the publication of big data in the library from Scopus database by looking at the writing time period of the papers, author's country, the most frequently occurring keywords, the article theme, the journal publisher and the group of keywords in the big data article. The methodology used in this study is a quantitative approach by extracting data from Scopus database publications with the keywords “big data” and “library” in May 2019. The collected data was analysed using Voxviewer software to show the keywords or terms. The results of the study stated that articles on big data have appeared since 2012 and are increasing in number every year. The big data authors are mostly from China and America. Keywords that often appear are based on the results of terminology visualization are including, “big data”, “libraries”, “library”, “data handling”, “data mining”, “university libraries”, “digital libraries”, “academic libraries”, “big data applications” and “data management”. It can be concluded that the number of publications related to big data in the library is still small; there are still many gaps that need to be researched on the topic. The results of the research can be used by libraries in using big data for the development of library innovation. Design/methodology/approach The Scopus database was accessed on 24 May 2019 by using the keyword “big data” and “library” in the search box. The authors only include papers, which title contain of big data in library. There were 74 papers, however, 1 article was dropped because of it not meeting the criteria (affiliation and abstract were not available). The papers consist of journal articles, conference papers, book chapters, editorial and review. Then the data were extracted into excel and analysed as follows (by the year, by the author/s’s country, by the theme and by the publisher). Following that the collected data were analysed using VOX viewer software to see the relationship between big data terminology and library, terminology clustering, keywords that often appear, countries that publish big data, number of big data authors, year of publication and name of journals that publish big data and library articles (Alagu and Thanuskodi, 2019). Findings It can be concluded that the implementation of big data in libraries is still in an early stage, it is shown from the limited number of practical implementation of big data analytics in library. Not many libraries that use big data to support innovation and services since there were lack of librarian skills of big data analytics. The library manager’s view of big data is still not necessary to do. It is suggested for academic libraries to start their adoption of big data analytics to support library services especially research data. To do so, librarians can enhance their skills and knowledge by following some training in big data analytics or research data management. The information technology infrastructure also needs to be upgraded since big data need big IT capacity. Finally, the big data management policy should be made to ensure the implementation goes well. Originality/value This paper discovers the adoption and implementation of big data in library, many papers talk big data in business and technology context. This is offering new idea for many libraries especially academic library about the adoption of big data to support their services. They can adopt the big data analytics technology and technique that suitable for their library.

Exploring Big Data Analytic Approaches to Cancer Blog Text Analysis

2019 ◽  
Vol 14 (4) ◽  
pp. 1-20 ◽  
Author(s):  
Viju Raghupathi ◽  
Yilu Zhou ◽  
Wullianallur Raghupathi
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Word Association ◽  
Big Data Analytics ◽  
Text Analytics ◽  
Hadoop Mapreduce ◽  
Cancer Management ◽  
Exploratory Approach ◽  
Clustering And Classification ◽  
Data Analytic

In this article, the authors explore the potential of a big data analytics approach to unstructured text analytics of cancer blogs. The application is developed using Cloudera platform's Hadoop MapReduce framework. It uses several text analytics algorithms, including word count, word association, clustering, and classification, to identify and analyze the patterns and keywords in cancer blog postings. This article establishes an exploratory approach to involving big data analytics methods in developing text analytics applications for the analysis of cancer blogs. Additional insights are extracted through various means, including the development of categories or keywords contained in the blogs, the development of a taxonomy, and the examination of relationships among the categories. The application has the potential for generalizability and implementation with health content in other blogs and social media. It can provide insight and decision support for cancer management and facilitate efficient and relevant searches for information related to cancer.

scholarly journals Approximate computation for big data analytics

2021 ◽  
pp. 1-8
Author(s):  
Shuai Ma ◽  
Jinpeng Huai
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Optimal Solution ◽  
Approximate Computation ◽  
Approximate Query Processing ◽  
Data Approximation ◽  
Approximation Techniques ◽  
Approximate Query ◽  
Data Analytic

Over the past a few years, research and development has made significant progresses on big data analytics. A fundamental issue for big data analytics is the efficiency. If the optimal solution is unable to attain or unnecessary or has a price to high to pay, it is reasonable to sacrifice optimality with a "good" feasible solution that can be computed efficiently. Existing approximation techniques can be in general classified into approximation algorithms, approximate query processing for aggregate SQL queries and approximation computing for multiple layers of the system stack. In this article, we systematically introduce approximate computation, i.e. , query approximation and data approximation, for efficient and effective big data analytics. We explain the ideas and rationales behind query and data approximation, and show efficiency can be obtained with high effectiveness, and even without sacrificing for effectiveness, for certain data analytic tasks.

scholarly journals Underpinnings of Big Data Analytics and Its Applications

2020 ◽  
pp. 75-81
Author(s):  
Anand Kumar Pandey ◽  
Rashmi Pandey ◽  
Ashish Tripathi
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Research Issues ◽  
New Developments ◽  
New Ideas ◽  
Analytical Assessment ◽  
Data Analytic ◽  
Big Data Technology

Big data and Data Mining are co-related to each other and also emphasize the phenomena of extracting and analysis useful data from considerable database. The concept of Big Data analytics plays a very significant role in several fields, such as Data Mining, Education and Training, cloud computing, E-commerce, healthcare and life science, Banking and Agriculture. Big data Analytic is a technique for looking at big set of data to expose hidden patterns. A large amount of data is continuously generated every day using modern information system and technologies. As a result this paper provides a platform to investigate applications of big data at various stages. In future, it come forward to be a required for an analytical assessment of new developments in the big data technology. In addition, it also explores a new and suitable outlook for researchers to expand the solution, based on the literature survey, challenges, new ideas and open research issues.

scholarly journals Studi On Big Data Analytics Framework in Smart City Context

2021 ◽  
Vol 328 ◽  
pp. 04022
Author(s):  
Rahmawati Dinda ◽  
Arief Assaf ◽  
Do Abdullah Saiful Saiful
Keyword(s):  
Big Data ◽  
Smart City ◽  
Data Analytics ◽  
Smart Cities ◽  
Big Data Analytics ◽  
Future Research ◽  
Technology Governance ◽  
Advantages And Disadvantages ◽  
The Government ◽  
Data Analytic

The issue of global urbanization, which is a separate problem faced by the government, is the very rapid growth of population density in cities. To face this challenge, the government launched a smart city project by targeting sustainable economic growth and improving the quality of life. Information and Communication Technology governance is the key to realizing a smart city. However, each of these I.C.T. tools produce large amounts of data known as Big Data. Data processing with the Big Data approach is becoming a trend in information systems to provide better public services and provide references in the policy-making process. However, to obtain important information in the scope of big data, a Big Data Analytics process is needed, also known as Big Data Value Chain. Extracting knowledge from the related literature can identify the characteristics of the big data analytic framework for smart cities. This paper reviews several big data analytic frameworks applied to smart cities. This paper is to find the advantages and disadvantages of each framework so that it can be a direction for future research

scholarly journals Exploring Big Data Analytic Approaches to Cancer Blog Text Analysis (Preprint)

2018 ◽  
Author(s):  
Viju Raghupathi ◽  
Yilu Zhou ◽  
Wullianallur Raghupathi
Keyword(s):  
Social Media ◽  
Big Data ◽  
Data Analytics ◽  
Word Association ◽  
Early Stage ◽  
Big Data Analytics ◽  
Free Text ◽  
Health Related ◽  
Data Analytic ◽  
Insight Into

BACKGROUND In recent years researchers have begun to realize the value of social media as a source for data that helps us understand health-related phenomena. Health blogs in particular are rich with information for decision-making. While there are web crawlers and blog analysis software that generate statistics related to blogs, these are relatively primitive and are not useful computationally to aid with the analysis and understanding of the social networks and medical blogs that are evolving around healthcare. There is a need for sophisticated tools to fill this gap. Furthermore, to our knowledge there are not many big data studies or applications in the text analytics of cancer blogs. This study attempts to fill this specific gap while analyzing cancer blogs. OBJECTIVE In this exploratory research, we examine the potential of applying big data analytic techniques to the analysis of blogs that exist in the cancer domain. Our objective is twofold: to extract from the blogs, patterns and insight about cancer diagnosis, treatment, and management; and to apply advanced computation techniques in processing large amounts of unstructured health data. METHODS We applied the big data analytics architecture of Hadoop MapReduce via the Cloudera platform to the analysis of cancer blog content, in order to extract patterns and insight on cancer diagnoses. We apply a series of algorithms to gain insight into the content and develop a vocabulary and taxonomy of keywords based on existing medical nomenclature. By applying a number of algorithms, we gained insight into the blog content. The study identifies, for instance, the most discussed topics as well as associations that relate to key phenomena RESULTS Using several text analytic algorithms, including word count, word association, clustering, and classification, we were able to identify and analyze the patterns and keywords in cancer blog postings. This gave insight into some of the key issues that are discussed in blogs such as the type of cancer (breast cancer being the dominant topic), diagnosis, treatments, and others. CONCLUSIONS In general, big data analytics has the potential to transform the way practitioners and researchers gain insight from health social media, especially those in free text, unstructured form. Big data analytics and applications in health-related social media are still at an early stage, and rapid acceleration is possible with the advancements in models, tools, and technologies.

scholarly journals Examination of Big Dataset using LEOS, JOSE, SVM on Map Reduce

2019 ◽  
Vol 9 (1S) ◽  
pp. 449-455
Keyword(s):  
Big Data ◽  
Data Structures ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Map Reduce ◽  
Information Sets ◽  
Speed Up ◽  
Integrated Technique ◽  
The Moment ◽  
Medical Dataset

Data analytics (DA) is the job of reviewing datasets in order to frame conclusions about the information they have, increasingly using specialized systems and software. As with the emergence of Big Data, data analytics was needed. The problems that we are considering are going to be in a fraud detection application. Where we'll considering major aspects such application-independent format(XML/JSON) for the clusterization process based on the no label classification algorithm where we will focusing on the clusters to enhance the oversampling process and utilize the merits of parallel computing to speed up our system. We aim to use MapReduce functionality in our application and deploy it on Amazon AWS. Datasets gathered for studies often comprise millions of records and can carry hard-to-detect concealed pitfalls. In this paper, we are working on two datasets. The first one is a medical dataset and the second one is a customer dataset. Big Data Analytics is the suggested solution in this day and age, with growing demands for analyzing huge information sets and performing the required processing on complicated data structures. The problem faced at the moment is mainly, how to store and analyze the large amount of data which is generated from heterogeneous sources like social media and what to use to make data fast accessible as well as in pocket budget. To resolve all problems Map-Reduce framework is useful-by offering an integrated technique towards machine learning, it speeds up processing. In this, we will explore the LEOS algorithm, SVM, MapReduce and JOSE algorithm, their requirements, their benefits, their disadvantages, difficulties, and their corresponding solutions.

scholarly journals The Potential Use of FinTech Developments in Takaful

2021 ◽  
Vol 8 (2) ◽  
pp. 109-121
Author(s):  
Hafidh Abdulla Hemed ◽  
Arwa Abubaker Abdullah Alamoudi ◽  
Anas Abdulkadir Abubakar Al Qassim ◽  
Bandar Mohammed Saif Qasem
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Insurance Industry ◽  
Academic Research ◽  
Big Data Analytics ◽  
Customer Experience ◽  
Business Environment ◽  
Competitive Intelligence ◽  
Islamic Insurance ◽  
Potential Use

Despite the increasingly important role that fintech play in the takaful industry, academic research in this area is quite limited. The overall aim of this paper it thus to explore the potential use of fintech in the Islamic insurance industry, especially in terms of its opportunities and challenges. Specifically, big data analytics and robo-advisory were explored and how takaful operators might incorporate them for better customer experience and gathering competitive intelligence. To remain competitive in a fast changing business environment, takaful operators need to identify and adopt fintech that could influence positively customer experience and optimise cost efficiency. This paper reviews the literature on big data analytics and robo-advisory, aiming to shed the light on the barriers and benefits of harnessing these technological advancements for takaful operators.

Integrating Big Data Analytics into Advertising Curriculum

2017 ◽  
pp. 228-250
Author(s):  
Kenneth C. C. Yang ◽  
Yowei Kang
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
The World ◽  
Challenges And Opportunities ◽  
The Status ◽  
Consumer Data ◽  
Tools And Techniques ◽  
Data Analytic ◽  
Collection Methods

With the assistance of new computing technologies and consumer data collection methods, advertising professionals are capable of generating better targeted advertising campaigns. Big Data analytics are particularly worth noticing and have presented ample opportunities for advertising researchers and practitioners around the world. Although Big Data analytic courses have been offered at major universities, existing advertising curricula have yet to address the opportunities and challenges offered by Big Data. This chapter collects curricular data from major universities around the world to examine what Big Data has posed challenges and opportunities to existing advertising curricula in an international context. Curricula of 186 universities around the world are reviewed to describe the status of integrating these developments into better preparing advertising students for these changes. Findings show that only selected advertising programs in the U.S. have begun to explore the potential of the data analytics tools and techniques. Practical and educational implications are discussed.

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