scholarly journals Impact of Healthcare Predictions with Big Data Analytics and Cognitive Computing Techniques

2019 ◽  
Vol 8 (2) ◽  
pp. 4757-4762
Keyword(s):  
Big Data ◽  
Information Society ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Extensive Study ◽  
Comprehensive Analysis ◽  
Cognitive Computing ◽  
The World ◽  
Complex Relationships ◽  
Insight Into

The world has transformed into an information society that exceedingly depends on data. Since information frameworks create large measures of records each day, consistently, it appears the world is achieving the level of data overload. Big data is used to process the enormous volumes of data into revealing shrouded designs, complex relationships, and other helpful information. This work has done a comprehensive analysis of enormous information investigation in medicinal services. A brief insight into the importance of cognitive computing in healthcare has been presented. The extensive study concludes that the Cognitive computing has more impact on healthcare predictions than the big data analytics.

DATA VISUALIZATION OF COVID-19 DATA USING TABLEAU

2021 ◽  
Vol 1 (10) ◽  
pp. 308-315
Author(s):  
Nirmit Singhal ◽  
Amita Goel, ◽  
Nidhi Sengar ◽  
Vasudha Bahl
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Data Analytics ◽  
Information Sources ◽  
Big Data Analytics ◽  
Data Sources ◽  
Visualization Technique ◽  
Global Pandemic ◽  
The World ◽  
Drop Interface

The world generated 52 times the amount of data in 2010 and 76 times the number of information sources in 2022. The ability to use this data creates enormous opportunities, and in order to make these opportunities a reality, people must use data to solve problems. Unfortunately, in the midst of a global pandemic, when people all over the world seek reliable, trustworthy information about COVID-19 (Coronavirus). Tableau plays a key role in this scenario because it is an extremely powerful tool for quickly visualizing large amounts of data. It has a simple drag-and-drop interface. Beautiful infographics are simple to create and take little time. Tableau works with a wide variety of data sources. COVID-19 (Coronavirus)analytics with Tableau will allow you to create dashboards that will assist you. Tableau is a tool that deals with big data analytics and generates output in a visualization technique, making it more understandable and presentable. Data blending, real-time reporting, and data collaboration are one of its features. Ultimately, this paper provides a clear picture of the growing COVID19 (Coronavirus) data and the tools that can assist more effectively, accurately, and efficiently. Keywords: Data Visualization, Tableau, Data Analysis, Covid-19 analysis, Covid-19 data

The Compute Infrastructures for Big Data Analytics

2014 ◽  
pp. 74-109 ◽  
Author(s):  
Pethuru Raj
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Sources ◽  
Steady Increase ◽  
Data Generation ◽  
Abnormal Growth ◽  
The World ◽  
Tremendous Amount ◽  
And Storage

The implications of the digitization process among a bevy of trends are definitely many and memorable. One is the abnormal growth in data generation, gathering, and storage due to a steady increase in the number of data sources, structures, scopes, sizes, and speeds. In this chapter, the author shows some of the impactful developments brewing in the IT space, how the tremendous amount of data getting produced and processed all over the world impacts the IT and business domains, how next-generation IT infrastructures are accordingly getting refactored, remedied, and readied for the impending big data-induced challenges, how likely the move of the big data analytics discipline towards fulfilling the digital universe requirements of extracting and extrapolating actionable insights for the knowledge-parched is, and finally, the establishment and sustenance of the dreamt smarter planet.

Big Data Analytics in Action

2019 ◽  
pp. 266-294 ◽  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Insight Into

Have you ever wondered how companies that adopt big data and analytics have generated value? Which algorithm are they using for which situation? And what was the result? These points will be discussed in this chapter in order to highlight the importance of big data analytics. To this end, and in order to give a quick introduction to what is being done in data analytics applications and to trigger the reader's interest, the author introduces some applications examples. This will allow you, in more detail, to gain more insight into the types and uses of algorithms for data analysis. So, enjoy the examples.

Big Data Analytics

2019 ◽  
pp. 67-81
Author(s):  
Nitigya Sambyal ◽  
Poonam Saini ◽  
Rupali Syal
Keyword(s):  
Big Data ◽  
Open Problem ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Capture ◽  
Data Sets ◽  
Full Potential ◽  
Processing Application ◽  
Nosql Databases ◽  
The World

The world is increasingly driven by huge amounts of data. Big data refers to data sets that are so large or complex that traditional data processing application software are inadequate to deal with them. Healthcare analytics is a prominent area of big data analytics. It has led to significant reduction in morbidity and mortality associated with a disease. In order to harness full potential of big data, various tools like Apache Sentry, BigQuery, NoSQL databases, Hadoop, JethroData, etc. are available for its processing. However, with such enormous amounts of information comes the complexity of data management, other big data challenges occur during data capture, storage, analysis, search, transfer, information privacy, visualization, querying, and update. The chapter focuses on understanding the meaning and concept of big data, analytics of big data, its role in healthcare, various application areas, trends and tools used to process big data along with open problem challenges.

Predictive Analysis for Digital Marketing Using Big Data

2017 ◽  
pp. 259-283
Author(s):  
Balamurugan Balusamy ◽  
Priya Jha ◽  
Tamizh Arasi ◽  
Malathi Velu
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Big Data ◽  
Statistical Analysis ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Predictive Analysis ◽  
Cognitive Computing ◽  
Internet Resources ◽  
To Come

Big data analytics in recent years had developed lightning fast applications that deal with predictive analysis of huge volumes of data in domains of finance, health, weather, travel, marketing and more. Business analysts take their decisions using the statistical analysis of the available data pulled in from social media, user surveys, blogs and internet resources. Customer sentiment has to be taken into account for designing, launching and pricing a product to be inducted into the market and the emotions of the consumers changes and is influenced by several tangible and intangible factors. The possibility of using Big data analytics to present data in a quickly viewable format giving different perspectives of the same data is appreciated in the field of finance and health, where the advent of decision support system is possible in all aspects of their working. Cognitive computing and artificial intelligence are making big data analytical algorithms to think more on their own, leading to come out with Big data agents with their own functionalities.

Cognitive computing, Big Data Analytics and data driven industrial marketing

2020 ◽  
Vol 90 ◽  
pp. 663-666
Author(s):  
Miltiadis Lytras ◽  
Anna Visvizi ◽  
Xi Zhang ◽  
Naif Radi Aljohani
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Driven ◽  
Cognitive Computing ◽  
Industrial Marketing

scholarly journals I-Os in the Vanguard of Big Data Analytics and Privacy

2015 ◽  
Vol 8 (4) ◽  
pp. 555-563 ◽  
Author(s):  
Adam J. Ducey ◽  
Nigel Guenole ◽  
Sara P. Weiner ◽  
Hailey A. Herleman ◽  
Robert E. Gibby ◽  
...  
Keyword(s):  
Big Data ◽  
Informed Consent ◽  
Data Analytics ◽  
Data Privacy ◽  
Big Data Analytics ◽  
Global Scale ◽  
Data Sources ◽  
Critical Elements ◽  
The World

In this response to Guzzo, Fink, King, Tonidandel, and Landis (2015), we suggest industrial–organizational (I-O) psychologists join business analysts, data scientists, statisticians, mathematicians, and economists in creating the vanguard of expertise as we acclimate to the reality of analytics in the world of big data. We enthusiastically accept their invitation to share our perspective that extends the discussion in three key areas of the focal article—that is, big data sources, logistic and analytic challenges, and data privacy and informed consent on a global scale. In the subsequent sections, we share our thoughts on these critical elements for advancing I-O psychology's role in leveraging and adding value from big data.

scholarly journals Deep Learning for Big Data Analytics

2019 ◽  
pp. 1-21
Author(s):  
Priti Srinivas Sajja ◽  
Rajendra Akerkar
Keyword(s):  
Neural Networks ◽  
Big Data ◽  
Deep Learning ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Future Research ◽  
Cognitive Computing ◽  
Intelligent Support ◽  
Artificial Neural ◽  
Traditional Approaches

Traditional approaches like artificial neural networks, in spite of their intelligent support such as learning from large amount of data, are not useful for big data analytics for many reasons. The chapter discusses the difficulties while analyzing big data and introduces deep learning as a solution. This chapter discusses various deep learning techniques and models for big data analytics. The chapter presents necessary fundamentals of an artificial neural network, deep learning, and big data analytics. Different deep models such as autoencoders, deep belief nets, convolutional neural networks, recurrent neural networks, reinforcement learning neural networks, multi model approach, parallelization, and cognitive computing are discussed here, with the latest research and applications. The chapter concludes with discussion on future research and application areas.

scholarly journals Business Open Big Data Analytics to Support Innovative Leadership and Management Decision in Canada

2020 ◽  
Vol 4 (2) ◽  
pp. 56-74
Author(s):  
Nadia Delanoy ◽  
arina Kasztelnik
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Big Data ◽  
Data Analytics ◽  
Customer Relations ◽  
Big Data Analytics ◽  
Innovative Leadership ◽  
Innovative Management ◽  
The World ◽  
Leadership Decisions

This paper summarizes how social media and other technologies continue to proliferate; the shifting economic landscape will precipitate more adaptive approaches for managers attempting to understand the multi-dimensional virtual aspects of communication with the artificial intelligence aspect. Also, we discover the different existing support of big data analytics to make a rational business decision. The methodology is the systematization literature sources within this context and approaches for the underlining approach to open big data analytics and support innovative leadership decisions in Canada. The paper is carried out in the following logical sequence to gain an understanding of how customer relations managers could utilize social media within a data analytics frame from scholar and practitioner perspectives. This literature research review original paper outlines the main themes including the role of social media, the experiences of using data analytics for customer relations management, and the notion that customer-centric technologies could change the dynamic of understanding customer intentions, leadership decisions and introduce the innovative management with using the big data analytics in place. The results of the critical thinking with analysis both authors can be useful for any business around the World that would like to start using Artificial Intelligence to support innovative management decisions. The emergent themes that were highlighted based on the realities of customer relations management may be significant to how the integration of social media feedback resulting from crowdsourcing in addition to existing data analytics could better position organizations in this evolving world. The implications of linking innovative management processes such as demographic analysis, platform understanding, and communication methods together are crucial for any public business with a global impact. Finally, the understanding of innovation management in a social media era and understanding how customers utilized open big data analytics sources could help leadership practices across industries around the World. Keywords: Big Data Analytics, Innovative Leadership, Management of Social Media, Open Sources.

Big-Data-Based Architectures and Techniques

2019 ◽  
pp. 19-50
Author(s):  
Gopala Krishna Behara
Keyword(s):  
Big Data ◽  
Real World ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Sources ◽  
Reference Architecture ◽  
The Real ◽  
Key Concepts ◽  
Insight Into

This chapter covers the essentials of big data analytics ecosystems primarily from the business and technology context. It delivers insight into key concepts and terminology that define the essence of big data and the promise it holds to deliver sophisticated business insights. The various characteristics that distinguish big data datasets are articulated. It also describes the conceptual and logical reference architecture to manage a huge volume of data generated by various data sources of an enterprise. It also covers drivers, opportunities, and benefits of big data analytics implementation applicable to the real world.

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