Analyzing Indian healthcare data with big data

2018 ◽  
Vol 7 (3.29) ◽  
pp. 88
Author(s):  
R Venkateswara Reddy ◽  
Dr D. Murali
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Big Data Analytics ◽  
Active Role ◽  
Heterogeneous Data ◽  
Retail Banking ◽  
Healthcare Data ◽  
Emergency Situations ◽  
Competitive Edge ◽  
The Right

Big Data is the enormous amounts of data, being generated at present times. Organizations are using this Big Data to analyze and predict the future to make profits and gain competitive edge in the market. Big Data analytics has been adopted into almost every field, retail, banking, governance and healthcare. Big Data can be used for analyzing healthcare data for better planning and better decision making which lead to improved healthcare standards. In this paper, Indian health data from 1950 to 2015 are analyzed using various queries. This healthcare generates the considerable amount of heterogeneous data. But without the right methods for data analysis, these data have become useless. The Big Data analysis with Hadoop plays an active role in performing significant real-time analyzes of the enormous amount of data and able to predict emergency situations before this happens.  

Data Science and Big Data Analytics in Financial Services

Web Services ◽  
2019 ◽  
pp. 1301-1329
Author(s):  
Suren Behari ◽  
Aileen Cater-Steel ◽  
Jeffrey Soar
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Financial Services ◽  
Data Science ◽  
Big Data Analytics ◽  
Disruptive Innovation ◽  
Big Data Analysis ◽  
Future Trends ◽  
The Right

The chapter discusses how Financial Services organizations can take advantage of Big Data analysis for disruptive innovation through examination of a case study in the financial services industry. Popular tools for Big Data Analysis are discussed and the challenges of big data are explored as well as how these challenges can be met. The work of Hayes-Roth in Valued Information at the Right Time (VIRT) and how it applies to the case study is examined. Boyd's model of Observe, Orient, Decide, and Act (OODA) is explained in relation to disruptive innovation in financial services. Future trends in big data analysis in the financial services domain are explored.

scholarly journals ‘SasCsvToolkit’ - A versatile parallel `bag-of-tasks` job submission application on heterogeneous and homogeneous platforms for Big Data Analytics such as for Biomedical Informatics

2019 ◽  
Author(s):  
Abhishek Singh
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Big Data Analytics ◽  
Large Data ◽  
Big Data Analysis ◽  
Organizational History ◽  
Corporate Groups ◽  
File Formats ◽  
The Right ◽  
Training Background

Abstract Background: The need for big data analysis requires being able to process large data which are being held fine-tuned for usage by corporates. It is only very recently that the need for big data has caught attention for low budget corporate groups and academia who typically do not have money and resources to buy expensive licenses of big data analysis platforms such as SAS. The corporates continue to work on SAS data format largely because of systemic organizational history and that the prior codes have been built on them. The data-providers continue to thus provide data in SAS formats. Acute sudden need has arisen because of this gap of data being in SAS format and the coders not having a SAS expertise or training background as the economic and inertial forces acting of having shaped these two class of people have been different. Method: We analyze the differences and thus the need for SasCsvToolkit which helps to generate a CSV file for a SAS format data so that the data scientist can then make use of his skills in other tools that can process CSVs such as R, SPSS, or even Microsoft Excel. At the same time, it also provides conversion of CSV files to SAS format. Apart from this, a SAS database programmer always struggles in finding the right method to do a database search, exact match, substring match, except condition, filters, unique values, table joins and data mining for which the toolbox also provides template scripts to modify and use from command line. Results: The toolkit has been implemented on SLURM scheduler platform as a `bag-of-tasks` algorithm for parallel and distributed workflow though serial version has also been incorporated. Conclusion: In the age of Big Data where there are way too many file formats and software and analytics environment each having their own semantics to deal with specific file types, SasCsvToolkit will find its functions very handy to a data engineer.

Data Science and Big Data Analytics in Financial Services

2016 ◽  
pp. 396-423 ◽  
Author(s):  
Suren Behari ◽  
Aileen Cater-Steel ◽  
Jeffrey Soar
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Financial Services ◽  
Data Science ◽  
Big Data Analytics ◽  
Disruptive Innovation ◽  
Big Data Analysis ◽  
Future Trends ◽  
The Right

The chapter discusses how Financial Services organizations can take advantage of Big Data analysis for disruptive innovation through examination of a case study in the financial services industry. Popular tools for Big Data Analysis are discussed and the challenges of big data are explored as well as how these challenges can be met. The work of Hayes-Roth in Valued Information at the Right Time (VIRT) and how it applies to the case study is examined. Boyd's model of Observe, Orient, Decide, and Act (OODA) is explained in relation to disruptive innovation in financial services. Future trends in big data analysis in the financial services domain are explored.

Current Approaches to Advanced Research in Data Science at International Top League Universities

2020 ◽  
Vol 17 (6) ◽  
pp. 2806-2811
Author(s):  
Wahidah Hashim ◽  
A/L Jayaretnam Prathees ◽  
Marini Othman ◽  
Andino Maseleno
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
World Data ◽  
The People ◽  
Software Engineers ◽  
Data Scientist ◽  
The Right

Data Science also known as Analytics, has a high demand in the industries right now, where professionals who are well trained in this field are being recruited by many large companies. Before the existence of data science, companies and industries search for software engineers and data analysis to sort IT related problems. However, as the internet start to being used by most of the people in the world, data keep on pouring in a large volume and velocity, software engineers and data analysis could not handle it anymore. Analyzing the tremendous size of data is called Big Data Analytics. Corporate companies have already started to realize that data scientists are the right person to tackle Big Data related problems. Low supply of data scientist has hiked in the salary of the data scientist, as the pay for a data scientist many more time higher compare to other IT related professionals. Knowledge in data science can solve any data related problems in this world. Data scientist are not only recruited by tech-giants like Google and Amazon, medium organizations also started to understand the importance of data science and they too recruit data scientist for their company. In this paper, we will explore on the requirement and knowledges of data science that can be covered in UNITEN’s computer science syllabus.

scholarly journals Big Data Analytics in urology: the story so far and the road ahead

2021 ◽  
Vol 13 ◽  
pp. 175628722199813
Author(s):  
B. M. Zeeshan Hameed ◽  
Aiswarya V. L. S. Dhavileswarapu ◽  
Nithesh Naik ◽  
Hadis Karimi ◽  
Padmaraj Hegde ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Early Stage ◽  
Big Data Analytics ◽  
Positive Trend ◽  
The Road ◽  
Healthcare Data ◽  
Urological Disorders ◽  
Different Sources

Artificial intelligence (AI) has a proven record of application in the field of medicine and is used in various urological conditions such as oncology, urolithiasis, paediatric urology, urogynaecology, infertility and reconstruction. Data is the driving force of AI and the past decades have undoubtedly witnessed an upsurge in healthcare data. Urology is a specialty that has always been at the forefront of innovation and research and has rapidly embraced technologies to improve patient outcomes and experience. Advancements made in Big Data Analytics raised the expectations about the future of urology. This review aims to investigate the role of big data and its blend with AI for trends and use in urology. We explore the different sources of big data in urology and explicate their current and future applications. A positive trend has been exhibited by the advent and implementation of AI in urology with data available from several databases. The extensive use of big data for the diagnosis and treatment of urological disorders is still in its early stage and under validation. In future however, big data will no doubt play a major role in the management of urological conditions.

scholarly journals Towards Implementation of Big Data Concepts in a Pharmaceutical Company

2020 ◽  
Vol 10 (1) ◽  
pp. 343-356
Author(s):  
Snezana Savoska ◽  
Blagoj Ristevski
Keyword(s):  
Information Technology ◽  
Big Data ◽  
Data Analysis ◽  
Pharmaceutical Company ◽  
Big Data Analytics ◽  
Structured Interviews ◽  
Holistic View ◽  
Analysis Methodology ◽  
Decision Making Processes ◽  
Research Demands

AbstractNowadays, big data is a widely utilized concept that has been spreading quickly in almost every domain. For pharmaceutical companies, using this concept is a challenging task because of the permanent pressure and business demands created through the legal requirements, research demands and standardization that have to be adopted. These legal and standards’ demands are associated with human healthcare safety and drug control that demands continuous and deep data analysis. Companies update their procedures to the particular laws, standards, market demands and regulations all the time by using contemporary information technology. This paper highlights some important aspects of the experience and change methodology used in one Macedonian pharmaceutical company, which has employed information technology solutions that successfully tackle legal and business pressures when dealing with a large amount of data. We used a holistic view and deliverables analysis methodology to gain top-down insights into the possibilities of big data analytics. Also, structured interviews with the company’s managers were used for information collection and proactive methodology with workshops was used in data integration toward the implementation of big data concepts. The paper emphasizes the information and knowledge used in this domain to improve awareness for the needs of big data analysis to achieve a competitive advantage. The main results are focused on systematizing the whole company’s data, information and knowledge and propose a solution that integrates big data to support managers’ decision-making processes.

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.

scholarly journals A multilevel approach to big data analysis using analytic tools and actor network theory

2018 ◽  
Vol 20 (1) ◽  
Author(s):  
Tiko Iyamu
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Network Theory ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Actor Network Theory ◽  
Big Data Analysis ◽  
Data Sets ◽  
Multilevel Approach ◽  
Actor Network

Background: Over the years, big data analytics has been statically carried out in a programmed way, which does not allow for translation of data sets from a subjective perspective. This approach affects an understanding of why and how data sets manifest themselves into various forms in the way that they do. This has a negative impact on the accuracy, redundancy and usefulness of data sets, which in turn affects the value of operations and the competitive effectiveness of an organisation. Also, the current single approach lacks a detailed examination of data sets, which big data deserve in order to improve purposefulness and usefulness.Objective: The purpose of this study was to propose a multilevel approach to big data analysis. This includes examining how a sociotechnical theory, the actor network theory (ANT), can be complementarily used with analytic tools for big data analysis.Method: In the study, the qualitative methods were employed from the interpretivist approach perspective.Results: From the findings, a framework that offers big data analytics at two levels, micro- (strategic) and macro- (operational) levels, was developed. Based on the framework, a model was developed, which can be used to guide the analysis of heterogeneous data sets that exist within networks.Conclusion: The multilevel approach ensures a fully detailed analysis, which is intended to increase accuracy, reduce redundancy and put the manipulation and manifestation of data sets into perspectives for improved organisations’ competitiveness.

scholarly journals A Hybrid-Algorithm for E-project Selection on Social Media

2020 ◽  
Vol 2 (2) ◽  
pp. 116-122
Author(s):  
Dr. Pasumponpandian A.
Keyword(s):  
Social Media ◽  
Big Data ◽  
Hybrid Algorithm ◽  
Optimal Algorithm ◽  
Big Data Analytics ◽  
Minimal Risk ◽  
Cognitive Computing ◽  
Development Environment ◽  
Nsga Ii ◽  
The Right

The integration of two of the biggest giants in the computing world has resulted in the development and advancement of new methodologies in data processing. Cognitive computing and big data analytics are integrated to give rise to advanced technologically sound algorithms like MOIWO and NSGA. There is an important role played by the E-projects portfolio selection (EPPS) issue in the web development environment that is handled with the help of a decision making algorithm based on big data. The EPPS problem tackles choosing the right projects for investment on the social media in order to achieve maximum return at minimal risk conditions. In order to address this issue and further optimize EPPS probe on social media, the proposed work focuses on building a hybrid algorithm known as NSGA-II-MOIWO. This algorithms makes use of the positive aspects of MOIWO algorithm and NSGA-II algorithm in order to develop an efficient one. The experimental results are recorded and analyzed in order to determine the most optimal algorithm based on the return and risk of investment. Based on the results, it is found that NSGA-II-MOIWO outperforms both MOIWO and NSGA, proving to be a better hybrid alternative.

Understanding the role of big data analytics for coordination of electronic retail service supply chain

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fatao Wang ◽  
Di Wu ◽  
Hongxin Yu ◽  
Huaxia Shen ◽  
Yuanjun Zhao
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Data Analysis ◽  
Big Data Analytics ◽  
Big Data Analysis ◽  
Coordination Mechanism ◽  
Market Demand ◽  
Content Type ◽  
Service Supply Chain ◽  
Retail Service

PurposeBased on the typical service supply chain (SSC) structure, the authors construct the model of e-tailing SSC to explore the coordination relationship in the supply chain, and big data analysis provides realistic possibilities for the creation of coordination mechanisms.Design/methodology/approachAt the present stage, the e-commerce companies have not yet established a mature SSC system and have not achieved good synergy with other members of the supply chain, the shortage of goods and the greater pressure of express logistics companies coexist. In the case of uncertain online shopping market demand, the authors employ newsboy model, applied in the operations research, to analyze the synergistic mechanism of SSC model.FindingsBy analyzing the e-tailing SSC coordination mechanism and adjusting relevant parameters, the authors find that the synergy mechanism can be implemented and optimized. Through numerical example analysis, the authors confirmed the feasibility of the above analysis.Originality/valueBig data analysis provides a kind of reality for the establishment of online SSC coordination mechanism. The establishment of an online supply chain coordination mechanism can effectively promote the efficient allocation of supplies and better meet consumers' needs.

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