Big Data Technolgies

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BIG DATA TECHNOLGIES 1726204
FREE PDF 1726204
Code: 1726204 Credits: 4 Semester 2 IOT

Big Data Technolgies

Get the full GTU study material, English & Gujarati textbook PDFs, syllabus curriculum, and solved previous year question papers.

Big Data Technolgies Syllabus — Unit-wise Content

The official GTU curriculum for Big Data Technolgies (subject code 1726204) as prescribed for IOT · Semester 2 divides the course into 5 units, covering 29 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.

Download the official syllabus PDF (1726204)

Unit 1 Big Data Platforms for the Internet of Things: network protocol-

8 lecture hours

  • data dissemination – current state of art- Improving Data and Service Interoperability with Structure
  • Conformance and Context Awareness: interoperability problem in the IoT context- Big Data Management Systems for the Exploitation of Pervasive Environments - Big Data challenges and requirements coming from different Smart City applications

Unit 2 RFID False Authentications: On RFID False Authentications: YA

  • TRAP – Necessary and sufficient condition for false authentication prevention - Adaptive Pipelined Neural Network Structure in Self Aware Internet of Things: self-healing systems- Role of adaptive neural network-Spatial Dimensions of Big Data: Application of Geographical 08 20 GUJARAT TECHNOLOGICAL UNIVERSITY Master of Engineering w.e.f
  • AY 2021-22 Concepts and Spatial Technology to the Internet of Things- Applying spatial relationships
  • functions, and models

Unit 3 Fog Computing: Fog Computing A Platform for Internet of

8 lecture hours

  • Things and Analytics: a massively distributed number of sources- Big Data Metadata Management in Smart Grids: semantic inconsistencies– role of metadata

Unit 4 Web Enhanced Building: Toward Web Enhanced Building

9 lecture hours

  • Automation Systems: heterogeneity between existing installations and native IP devices - loosely-coupled Web protocol stack –energy saving in smart building- Intelligent Transportation Systems and Wireless Access in Vehicular Environment Technology for Developing Smart Cities: advantages and achievements- Emerging Technologies in Health Information Systems: Genomics Driven Wellness Tracking and Management System (GO-WELL) – predictive care – personalized medicine

Unit 5 Sustainability Data and Analytics: Sustainability Data and

  • Analytics in Cloud-Based M2M Systems – potential stakeholders and their complex relationships to data and analytics applications – Social Networking Analysis - Building a useful understanding of a social network – Leveraging Social Media and IoT to Bootstrap Smart Environments: lightweight Cyber-Physical Social Systems - citizen actuation 09 20 o Recent Development in Big Data Technologies (Study from latest research papers from Scopus indexed/web of science journals
  • 04 hours during laboratory hours) Reference Book: 1
  • ” Big Data and The Internet of Things Enterprise Information Architecture for A New Age”
  • John Bates
  • “Thingalytics - Smart Big Data Analytics for the Internet of Things”
  • 2015 Course Outcome: After completion of the Course
  • Students will be able to: No Course Outcomes RBT Level* 01 Understand the concepts of big data analytics
  • RFID authentication
  • Fog computing
  • web-enhanced building
  • sustainability data UN 02 Implement RFID authentication and fog computing on uses cases AP GUJARAT TECHNOLOGICAL UNIVERSITY Master of Engineering w.e.f
  • AY 2021-22 03 Apply web-enhanced building
  • sustainability data and analytics for practical problems AP 04 Analyze Big Data computing for practical problems AN 05 Evaluate various techniques of Big Data computing for real-time IoT solutions EL *RM: Remember
  • UN: Understand
  • AP: Apply
  • AN: Analyze
  • EL: Evaluate
  • CR: Create Suggested Course Practical List: ● At Least 10-12 practicals based on topics of the syllabus have to be performed either in tools or in hardware kits
  • List of Laboratory/Learning Resources Required: ● List of Hardware: IoT Builder
  • Raspberry Pi, Arduino
  • UNO board ● List of Software: Hadoop
  • HPCC, and Strom ● List of Useful websites/MOOCs: https://nptel.ac.in/courses/106/104/106104189/

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