Exploring Big Data Analytic Approaches to Cancer Blog Text Analysis

2022 ◽  
pp. 1843-1863
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.

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 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.

Comprehensive Contemplation of Probabilistic Aspects in Intelligent Analytics

2020 ◽  
Vol 11 (1) ◽  
pp. 116-141 ◽  
Author(s):  
Neeti Sangwan ◽  
Vishal Bhatnagar
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Big Data Analysis ◽  
Future Research ◽  
Learning Approaches ◽  
Text Analytics ◽  
Review Of Literature ◽  
Classification Framework

In Big Data analysis, the application of machine learning has proven to be a revolutionary. The systematic review of literature shows that research has been carried out on the domain of big data analytics particularly text analytics with the inclusion of machine learning approaches. This extensive survey deals with the data at hand that provides different ways and issues while combining the machine learning approaches with the text. During the course of the survey, various publications in the field of synchronous application of machine learning in text analytics were searched and studied. Classification framework is proposed as the contribution of machine learning in text analytics. A classification framework represented the various application areas to motivate researchers for future research on the application of two emerging technologies.

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

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.

Hadoop Mapreduce Framework in Big Data Analytics

2014 ◽  
Vol 8 (3) ◽  
pp. 115-119
Author(s):  
Vidyullatha Pellakuri ◽  
◽  
Dr.D. Rajeswara Rao
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Mapreduce Framework ◽  
Hadoop Mapreduce

scholarly journals An Analysis of Big Data Analytics, IoT and Augmented Banking on Consumer Loan Banking Business in Germany

2021 ◽  
Vol 1 (1) ◽  
pp. 16
Author(s):  
Maulibian Putra
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Industry 4.0 ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Secondary Data ◽  
Academic Literature ◽  
Consumer Loans ◽  
Banking Business ◽  
Data Analytic

This research aims to analyze the particular component of industry 4.0 applying in the consumer loans banking business. Industry 4.0 comprises of a lot of technological advantages, this research selected three components of industry 4.0; Big data analytic, Internet of Things (IoT), and Augmented reality in banking. Secondary data are used to conduct this research, collected from the previous research and the academic literature. At first, this research explains why the consumer loans banking business in Europe, especially in Germany, needs to integrate Industry 4.0. secondly, this research analyzes the suitable component of industry 4.0 into the consumer loans banking business. The findings of this research deliver an essential piece of analysis to the consumer loans banking business' stakeholder with direction on implementing industry 4.0 into their business.

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