Big Data Analysis and Its Application in Different Industrial Domains

Abstract

Big data can bring “great value” to our lives in almost every aspect. Technologically, Big Data brings changes in our lives as it enables the full integration and analysis of diverse and heterogeneous data to help us make decisions. Today, with Big Data technology, thousands of data from seemingly unrelated domains can help make important decisions. This is the power of Big Data. Application Areas Health, Well-being, Policy Making and Public Opinion, Smart Cities and More Efficient Society and Robotic Interaction, New Online Education Model: Model student-teacher geometry Foundations in Big Data Analytics Research: Develop and research fundamental theories, algorithms, techniques, methodologies, technologies to solve problems effectively and efficiently to allow the application of Big Data problems. Scientists in the field and big data researchers. This Paper Presentation talks about Big Data analysis and its application in different industrial domains under the following sub topic: Introduction: A brief overview of Big Data analysis and its significant application in different industry and in the field of research. Case study: Discussion of a case study of Big Data together with IoT provides the infrastructure for collecting the data for different domain application of smart cities as important application goal. Work flow: Showcases simplified version of overall work flow of the case study. Conclusion: Discussion of concluded outcome and breakthrough of data analytics.

Introduction

I. INTRODUCTION

In today's omnichannel world, data analytics has gone a long way in measuring and predicting impact at the channel level to understand where best to spend marketing money. Questions about who to target, which channels and how much to spend per channel will always be with us. To answer these questions and more, machine learning pushed all boundaries to develop a multi-step composite model. After preprocessing the data, machine learning is used to update the decay parameters for each channel. Since we are using a sampling algorithm to estimate the following distribution, this can take a long time. The attribution side of the model is established at the final stage of estimating the impact of each channel at an individual level. This is done using decay parameters updated with Machine Learning. The model is built with the constraints and prior knowledge of the channel impact values ??applied from the model at the channel level. Compared to standard approaches, attribution models are able to predict significantly greater impact on omnichannel marketing campaigns. Using the results of the model, micro-segmentation allows to group customers into viable segments to receive specific channels and/or advertise with a personalized message that will be relevant to the audience. The non-linear channel response curves use an attribution model that allows for marginal attribution measurement, allows for optimal allocation of investments to maximize the model, and can also be updated as the campaign progresses. are working to support campaign optimization decisions. This advanced modeling solution captures impact behavior changes over several months to show which channels, tactics, and metrics need to be tweaked to improve campaign results. Results from past and present campaigns are integrated and leveraged by marketers. These templates and revision information keep your preferences and channel combinations relevant and readily available for the next round of campaign planning, establishing a culture of continuous learning and optimization.

II. CASE STUDY: RELATION TO SMART CITIES AND IOT (INTERNET OF THINGS) IN EAST KOWLOON PROJECT OF ENERGIZING HONG KONG

Via Smart Cities

Big Data together with IoT provides the infrastructure for collecting the data for different domain application of smart cities as important application goal.

Big Data Analytics: Data mining and machine learning Large-scale machine learning, data mining and data visualization

Big Data Computing: Data centre support for Analytics

Big data collection and transformation, integration and distributed data management and computing

Big Data Theory, Privacy & Security issues on Analytics

Big data sampling and statistical theory, big data security and privacy

Big Data Science: 4th Paradigm – Analytics for Science and Engineer  Big Data and Multi-disciplines (Bio, Chemistry, Engineering, Social)

Conclusion

New methods and solutions are provided by Big Data research in various industry sectors. New apps impact society and industry in Hong Kong and beyond. New Digital Economy is created based on Big Data Research. New educational programs for students who nurture leaders for society and the Big Data industry. New algorithms, methodologies, systems and applications are provided by Big Data research in various industrial fields. New insights from Big Data applications in scientific, engineering and social issues. New perspectives on real-world Big Data practice. New ways to protect the security and privacy of big data involve individuals and organizations.

References

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Copyright

Copyright © 2022 Vijayamahantesh S Kanavi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.