Conceptual Framework for the Application of Big Data in the Automobile Industry

Shubham Fialoke; Vibhu Dharmadhikari; Varun Bhati; Vaibhav Gupta; Saurabh Agarwal

doi:https://doi.org/10.51976/ijari.441615

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Conceptual Framework for the Application of Big Data in the Automobile Industry

Shubham Fialoke, Vibhu Dharmadhikari, Varun Bhati, Vaibhav Gupta, Saurabh Agarwal

Vol 4 , Issue 4 , October - December 2016 | Pages: 81-85 | Research Paper

https://doi.org/10.51976/ijari.441615

Received: October 25, 2016 | Revised: November 20, 2016 | Accepted: November 28, 2016 | Published Online: December 15, 2016

Author Details ( * ) denotes Corresponding author

1. Shubham Fialoke, Department of Mechanical Engineering, & Automobile Engineering, Delhi Technological University, New Delhi, Delhi, India (shubhamfialoke@gmail.com)

2. Vibhu Dharmadhikari, Department of Mechanical Engineering, & Automobile Engineering, Delhi Technological University, New Delhi, Delhi, India

3. Varun Bhati, Department of Mechanical Engineering, & Automobile Engineering, Delhi Technological University, New Delhi, Delhi, India

4. Vaibhav Gupta, Department of Mechanical Engineering, & Automobile Engineering, Delhi Technological University, New Delhi, Delhi, India

5. Saurabh Agarwal, Department of Mechanical Engineering, & Automobile Engineering, Delhi Technological University, New Delhi, Delhi, India (agrawals.iit@gmail.com)

The paper sheds light on big data analytics, the science of machine learning viz. data science and how it can be used in today’s automobile industry. Data science is a new field that has emerged out of the coupling of computer science and statistics. The ‘what?’ and ‘how?’ of data sciences are explained thoroughly. After defining what Big Data and Data Science are, the job market of data science is explored. This includes the jobs data scientists undertake and the skills acquired by them viz. machine learning and natural language processing to find patterns in this data and make predictions. Furthermore, the future of big data analytics in manufacturing and production is analyzed, particularly in connected vehicular systems. Today, with the advancement of technology, much media attention has been focused on an area where big data and automobiles intersect: Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication collectively referred to as Machine-to-Machine (M2M) communication. This paper focuses primarily on data science in driverless cars and the advent of Internet of Things (IoT) in automobile technology.

Keywords

Automobile Industry; Data Science; Driverless Car.

Lo Frank; What is data science?; https://datajobs.com/what-is-datascience

Levine Daniel; How many data scientists are there?; https://blog.rjmetrics.com/2015/10/05/how-many-data-scientists-are-there/

Davenport Thomas, Patil DJ; Data scientist: The sexiest job of the 21st century; https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21stcentury/

Agarwal Ritu, Dhar Vasant ; Big Data, Data Science, and Analytics: The Opportunity and Challenge for IS Research ; Information Systems Research 25(3), 2014, 443–448

Future of big data analytics: machine data and connected vehicles http://www.simafore.com/blog/bid/207756/Future-of-big-data-analytics-machine-data-and-connected-vehicles

Augur Hannah ; How data science is driving the driverless car; http://dataconomy.com/how-data-science-is-driving-the-driverless-car/

Simoudis Evangelos ; The automotive industry’s big data challenge (Part 1) ; https://www.enterpriseirregulars.com/104636/automotive-industrys-big-data-challenge-part-1/

Schober Scott ; Cyber security and the future of smart cars: Are smart cars too smart for their own good? ; http://www.ibmbigdatahub.com/blog/cybersecurity-and-future-smart-cars