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BIG DATA IN ENGINEERING APPLICATIONS

Prof.manoj kumar padhi

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Overview

  • Introduction
  • Why Big Data
  • Big Data(globally)
  • Big Data: 3 V’s
  • Big Data challenges
  • Big Data in Design Engineering
  • Reasons for the importance of Big Data
  • Cloud and Big Data
  • Big Data in Ecommerce
  • PLM in Big Data
  • Advantages
  • Conclusion

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INTRODUCTION

  • Big data is the term for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications.

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  • The challenges that we face with dbms tools and other technologies is capture, curation, storage, search, sharing, transfer, analysis, and visualization.

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Why Big data

  • Key enablers for the appearance and growth of ‘Big-Data’ are:

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    • Increase in storage capabilities
    • Increase in processing power
    • Availability of data

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Big data: 3 V’s

  • Big data is usually transformed in three dimensions- volume, velocity and variety.
  • Volume: Machine generated data is produced in larger quantities than non traditional data.
  • Velocity: This refers to the speed of data processing.
  • Variety: This refers to large variety of input data which in turn generates large amount of data as output.

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REF:2

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https://www.google.de/search?q=evolution+of+business+intelligence&newwindow=1&tbm=isch&tbo=u&source=univ&sa=X&ei=gEGoU5KXBuTb4QSGsoH4BQ&ved=0CDsQsAQ&biw=1366&bih=64

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http://www.meltinfo.com/ppt/ibm-big-data

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The Evolution of Business Intelligence

scale

scale

1990’s

2000’s

2010’s

https://www.google.de/search?q=evolution+of+business+intelligence&newwindow=1&tbm=isch&tbo=u&source=univ&sa=X&ei=gEGoU5KXBuTb4QSGsoH4BQ&ved=0CDsQsAQ&biw=1366&bih=64

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OLTP: Online Transaction Processing (DBMSs)

OLAP: Online Analytical Processing (Data Warehousing)

RTAP: Real-Time Analytics Processing (Big Data Architecture & technology)

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Big data in design and engineering

  • Engineering department of manufacturing companies.
  • Boeing’s new 787 aircraft is perhaps the best example of Big Data, a plane designed and manufactured.
  • Big Data needs to be transferred for conversion into machining related information to allow the product to be manufactured.

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Reasons for the importance of Big Data

  • Increase innovation and development of next generation product
  • Improve customer satisfaction
  • Sharpen competitive advantages
  • Create more narrow segmentation of customers
  • Reduce downtime

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Cloud and big data

  • In fact from a Cloud perspective I believe that the transfer and archiving of Big Data will become a key capability of a manufacturing focused cloud environment.
  • Servers based on the Intel® Xeon® processor E5 and E7 families are at the heart of infrastructure that supports both cloud and big data environments.
  • Ideal for storing and processing large volumes of data
  • Web based tools will allow you to upload your Big Data to the manufacturing cloud, 

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Bigdata in Ecommerce

  • Collect, store and organize data from multiple data sources.
  • Bigdata track and better understand a variety of information from many different sources(i.e., inventory management system, CRM, Adword/Adsence analytics, email service provider statastics etc).

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PLM in Big Data

  • Big data grows ridiculously fast
  • Most Big data is ephemeral by nature
  • Out-of-date Big data can undermine the results of your business analytics

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PLM adopts Big Data?

  • Too big and too abstract.

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  • This is not simple and will not happen overnight for most of manufacturing companies using PLM systems.

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  • PLM data size may reach to yotta bytes

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Advantages

  • Dialogue with consumers
  • Redevelop your products
  • Perform risk analysis
  • Keeping data safe
  • Customize your website in real time
  • Reducing maintenance cost

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Conclusion

  • Silicon valley and through social media is making Big Data a global phenomenon
  • Not only Big Data is “cool” it happens to be a huge growth area as well.

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Resources :

  1. https://www.google.de/search?q=evolution+of+business+intelligence&newwindow=1&tbm=isch&tbo=u&source=univ&sa=X&ei=gEGoU5KXBuTb4QSGsoH4BQ&ved=0CDsQsAQ&biw=1366&bih=64
  2. https://www.google.de/search?q=big+data+TRANSACTION+INTERACTION+OBSERVATION+EXAMPLE&newwindow=1&source=lnms&tbm=isch&sa=X&ei=DkaoU-H4K4Xe4QSO1oDwAg&ved=0CAgQ_AUoAQ&biw=1366&bih=643
  3. http://www.tcs.com/SiteCollectionDocuments/White%20Papers/Knowledge-Big-Data-Analytics-Product-Development-1213-1.pdf
  4. http://www.meltinfo.com/ppt/ibm-big-data
  5. http://wwwiti.cs.uni-magdeburg.de/iti_db/forschung/index.php#projekte
  6. http://datascienceseries.com/stories/ten-practical-big-data-benefits
  7. http://www.intel.com/content/dam/www/public/us/en/documents/product-briefs/big-data-cloud-technologies-brief.pdf
  8. http://www.bigdatalandscape.com/news/why-big-data-is-a-must-in-ecommerce
  9. http://www.intel.com/content/dam/www/public/us/en/documents/product-briefs/big-data-cloud-technologies-brief.pdf
  10. http://www.gxsblogs.com/morleym/2011/10/how-the-cloud-helps-manufacturers-address-%E2%80%98big-data%E2%80%99-challenges.html
  11. http://www.itbusinessedge.com/blogs/integration/three-reasons-why-life-cycle-management-matters-more-with-big-data.html
  12. http://www.forbes.com/sites/siliconangle/2012/02/29/big-data-is-creating-the-future-its-a-50-billion-market/
  13. http://plmtwine.com/tag/big-data/
  14. http://www.3dcadworld.com/big-data-will-important-manufacturers-future/

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