Load assumption process for durability design using new data sources and data analytics

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

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The Compute Infrastructures for Big Data Analytics

Advances in Data Mining and Database Management - Handbook of Research on Cloud Infrastructures for Big Data Analytics ◽

10.4018/978-1-4666-5864-6.ch004 ◽

2014 ◽

pp. 74-109 ◽

Cited By ~ 1

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.

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Big Data Methods

Organizational Research Methods ◽

10.1177/1094428116677299 ◽

2016 ◽

Vol 21 (3) ◽

pp. 525-547 ◽

Cited By ~ 55

Author(s):

Scott Tonidandel ◽

Eden B. King ◽

Jose M. Cortina

Keyword(s):

Machine Learning ◽

Data Mining ◽

Big Data ◽

Data Analytics ◽

Data Science ◽

Data Sources ◽

Future Research ◽

Organizational Science ◽

Associated Data ◽

Organizational Sciences

Advances in data science, such as data mining, data visualization, and machine learning, are extremely well-suited to address numerous questions in the organizational sciences given the explosion of available data. Despite these opportunities, few scholars in our field have discussed the specific ways in which the lens of our science should be brought to bear on the topic of big data and big data's reciprocal impact on our science. The purpose of this paper is to provide an overview of the big data phenomenon and its potential for impacting organizational science in both positive and negative ways. We identifying the biggest opportunities afforded by big data along with the biggest obstacles, and we discuss specifically how we think our methods will be most impacted by the data analytics movement. We also provide a list of resources to help interested readers incorporate big data methods into their existing research. Our hope is that we stimulate interest in big data, motivate future research using big data sources, and encourage the application of associated data science techniques more broadly in the organizational sciences.

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

Industrial and Organizational Psychology ◽

10.1017/iop.2015.83 ◽

2015 ◽

Vol 8 (4) ◽

pp. 555-563 ◽

Cited By ~ 7

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.

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Data Analytics and Mining in Healthcare with Emphasis on Causal Relationship Mining

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d6492.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 195-204

Keyword(s):

Data Mining ◽

Big Data ◽

Causal Relationship ◽

Data Analytics ◽

Statistical Tests ◽

Analytical Techniques ◽

Data Sources ◽

Healthcare Personnel ◽

Related Data ◽

Healthcare Data

High volumes and varieties of data is piling every day from healthcare and related fields. This big data sources if managed and analysed properly will provide vital knowledge. Data mining and data analytics have been playing an important role in extracting useful information from healthcare and related data sources. The knowledge extracted from these data sources guiding patients and healthcare personnel towards improved health conditions. Analytical techniques from statistics, functionalities from data mining and machine learning already proved their capability with significant contributions to healthcare industry. The dominant functionality of data mining is classification which has been in use in mining healthcare data. Though classification is a good learning technique it may not provide a causation model which will be a reliable model for better decision making particularly in the medical field. The present models for causality have limitations in terms of scalability and reliability. The present study is targeted to study causal models for causal relationship mining. This study tried to conclude with some proposals for causal relationship discovery which are efficient, reliable and scalable. The proposed model is going to make use of some qualities of decision trees along with statistical tests and analytics. It is proposed to build the learning models on healthcare big data sources.

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Big-Data-Based Architectures and Techniques

Predictive Intelligence Using Big Data and the Internet of Things - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-6210-8.ch002 ◽

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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The IT Readiness for the Digital Universe

Big Data ◽

10.4018/978-1-4666-9840-6.ch035 ◽

2016 ◽

pp. 757-777

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 authors show 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 being 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 smarter planet.

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Combining Live Drilling Data Stream with a Cloud Data Analytics Pipeline to Perform Real-Time Automated Projections to the Bit

10.2118/204065-ms ◽

2021 ◽

Author(s):

Daniel Cardoso Braga ◽

Mohammadreza Kamyab ◽

Brian Harclerode ◽

Deep Joshi

Keyword(s):

Trend Analysis ◽

Real Time ◽

Data Stream ◽

Data Analytics ◽

Data Sources ◽

Rotary Drilling ◽

Cloud Data ◽

West Texas ◽

Multiple Data ◽

Drilling Data

Abstract During drilling, surveys to determine the wellbore trajectory are performed at every drilling connection. However, due to the offset between the survey instrument and the bit (typically between 30-100 ft), this survey represents the sensor's position which is lagged compared to the bit. This paper describes a method to automatically calculate projections to the bit in real-time utilizing multiple data sources: WITSML stream, BHA components and rotary trend analysis while rotary drilling. The projection to the bit calculation routine is performed in real time every 30 seconds. This paper presents results of projections for four horizontal unconventional wells drilled in West Texas. Nearly 75,000 projections were generated on the four wells, validated with 839 survey stations, with median divergence of the projections from the nearest survey stations being less than one foot.

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Managing Data through the Lens of an Ontology

AI Magazine ◽

10.1609/aimag.v39i2.2802 ◽

2018 ◽

Vol 39 (2) ◽

pp. 65-74 ◽

Cited By ~ 4

Author(s):

Maurizio Lenzerini

Keyword(s):

Information Technology ◽

Data Analytics ◽

Data Sources ◽

Information Technology Industry ◽

Data Preparation ◽

Data Governance ◽

Technology Industry ◽

Complex Organization ◽

Challenging Tasks ◽

Preparation Phase

While the amount of data stored in current information systems continuously grows, and the processes making use of such data become more and more complex, extracting knowledge and getting insights from these data, as well as governing both data and the associated processes, are still challenging tasks. The problem is complicated by the proliferation of data sources and services both within a single organization, and in cooperating environments. Effectively accessing, integrating and managing data in complex organizations is still one of the main issues faced by the information technology industry today. Indeed, it is not surprising that data scientists spend a comparatively large amount of time in the data preparation phase of a project, compared with the data minining and knowledge discovery phase. Whether you call it data wrangling, data munging, or data integration, it is estimated that 50%-80% of a data scientists time is spent on collecting and organizing data for analysis. If we consider that in any complex organization, data governance is also essential for tasks other than data analytics, we can conclude that the challenge of identifying, gathering, retaining, and providing access to all relevant data for the business at an acceptable cost, is huge.

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