Big Data Projects

projects on big data
Big Data Projects

Big Data Projects

CITL Tech Varsity, Bangalore offers 2017-2018 IEEE Projects on Big Data for final year engineering Computer Science & Engineering students (CSE) and Final year engineering projects on Big Data for information science and engineering (ISE) students. Java based 2017-2018 IEEE Projects on for M.Tech, CSE, CNE (Computer Network engineer) and BE CSE, BE ISE students. CITL Tech Varsity, Bangalore also offer online training for projects on Big Data for final year engineering Computer Science & Engineering(CSE) and Final year engineering projects on Big Data for information science and engineering students. CITL offers 2017 IEEE Projects training on software JAVA at very cheap cost. See this section for list of Projects on Big Data or Contact us for details and projects on Big Data.

IEEE 2017-2018 bigdata (hadoop) project list on java based for mtech / MS / be / btech / mca / M.sc students in bangalore.

CITL Tech varsity offers Big data haddop based IEEE projects for Mtech and BE final year  computer science branch students. Here at CITL we use apache hadoop i.e., cloudera’s open source platform to work on . It is a java based programming which runs on apache hadoop i.e, on cloudera framework. We also work on Apache spark big data projects using scaa programming We have technical team who are skilled enough to provide solution on latest. IEEE related . Get analytics and hadoop based projects on big data for students using java as core programming language.

We assist students online for by taking teamviewer access and through skype.

  1. QoS-Aware Data Replications and Placements for Query Evaluation of Big Data Analytics
  2. Traffic-aware Task Placement with Guaranteed Job Completion Time for Geo-distributed Big Data
  3. Online Data Deduplication for In-Memory Big-Data Analytic Systems
  4. Novel Common Vehicle Information Model (CVIM) for Future Automotive Vehicle Big Data Marketplaces
  5. Holistic Perspective of Big Data in Healthcare
  6. Focusing on a Probability Element: Parameter Selection of Message Importance Measure in Big Data
  7. CryptMDB: A Practical Encrypted MongoDB over Big Data
  8. Cost Aware Cloudlet Placement for Big Data Processing at the Edge
  9. Big-Data-Driven Network Partitioning for Ultra-Dense Radio Access Networks
  10. Big Data Set Privacy Preserving through Sensitive Attribute-based Grouping
  11. Big Data Driven Information Diffusion Analysis and Control in Online Social Networks
  12. Big Data Analytics of Geosocial Media for Planning and Real-Time Decisions
  13. An Approximate Search Framework for Big Data
  14. A Reliable Task Assignment Strategy for Spatial Crowdsourcing in Big Data Environment
  15. A Queuing Method for Adaptive Censoring in Big Data Processing
  16. Achieving Efficient and Privacy-Preserving Cross-Domain Big Data Deduplication in Cloud
  17. A Profile-Based Big Data Architecture for Agricultural Context
  18. Review Based Service Recommendation for Big Data
  19. Big Data Challenges in Smart Grid IoT (WAMS) Deployment
  20. A data mining framework to analyze road accident data
  21. A Time Efficient Approach for Detecting Errors in Big Sensor Data on Cloud
  22. Big data, big knowledge: big data for personalised healthcare
  23. Deduplication on Encrypted Big Data in Cloud
  24. Processing Geo-Dispersed Big Data in an Advanced MapReduce Framework
  25. Recent Advances in Autonomic Provisioning of Big Data Applications on Clouds
  26. Privacy Preserving Data Analysis in Mental Health Research
  27. BFC: High-Performance Distributed Big-File Cloud Storage Based On Key-Value Store
  28. Performance Analysis of Scheduling Algorithms for Dynamic Workflow Applications
  29. PaWI: ParallelWeighted Itemset Mining by means of MapReduce
  30. Building a Big Data Analytics Service Framework for Mobile Advertising and Marketing
  31. Secure Sensitive Data Sharing on a Big Data Platform
  32. Load Balancing for Privacy-Preserving Access to Big Data in Cloud
  33. Enabling Efficient Access Control with Dynamic Policy Updating for Big Data in the Cloud
  34. MRPrePost-A parallel algorithm adapted for mining big data
  35. Privacy Preserving Data Analytics for Smart Homes
  36. Authorized Public Auditing of Dynamic Big Data Storage on Cloud with Efficient Verifiable Fine-grained Updates
  37. KASR: A Keyword-Aware Service Recommendation Method on MapReduce for Big Data
  38. Cost Minimization for Big Data Processing in Geo-Distributed Data Centers
  39. Dache: A Data Aware Caching for Big-Data Applications Using the MapReduce Framework
  40. ClubCF: A Clustering-based Collaborative Filtering Approach for Big Data Application

Big data is a term for data sets that are so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, data duration, search, sharing, storage, transfer, visualization, querying and information privacy. The term often refers simply to the use of predictive analytics and Analysis of data sets can find new correlations to "spot business trends, prevent diseases, and combat crime and so on. Projects on Big Data are growing rapidly because they are increasingly gathered by cheap and numerous information-sensing mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks.

Characteristics of projects on Big Data

Big data can be described by the following characteristics

  1. Veracity The quality of Data captured can vary greatly, affecting accurate analysis.
  2. Volume The quantity of generated and Data stored. The size of the data determines the value and potential insight and whether it can actually be considered big data or not.
  3. Velocity In this context, the speed at which the data is generated and processed to meet the demands and challenges that lie in the path of growth and development.
  4. Variability Inconsistency of the data set can hamper processes to handle and manage it.
  5. Variety Type and nature of the data. This helps people who analyze it to effectively use the resulting insight.

Data must be processed with advanced tools (analytics and algorithms) to reveal meaningful information. For example, to manage a factory one must consider both visible and invisible issues with various components. Information generation algorithms must detect and address invisible issues such as machine degradation, component wear, etc. on the factory floor so these things we can study in Projects on Big Data.

for engineering students

 for Engineering Students

Engineering students should choose big data for his final year project, because it Big-Data is the future of modern data science. We have best 2016-2017 for engineering students ideas, which is going to be extremely useful in day to day life. At CITL you will get expert training for any kind of projects based on Big Data. Engineering students can do their Big-Data projects on these area

  • Real time data recovery, getting missing values
  • Social Marketing Footprint discovery and analysis for Marketing
  • Smart City Maintenance, and Data Management system 2016
  • Auto spelling and grammar detection and correction
  • Human Activity Recognition, Public Transport, Machine Learning
  • Cloud computing object storage and integration system
  • DNA Database storage and analysis
  • Real Time query answering system form Big Data Source

Attend your big data final year projects at our institute in Bangalore or take online direct training classes from anywhere in India or world. Get top quality and trending IEEE from here and do it by yourself. We are continuously adding more big data final year project ideas, so you could find new opportunities in Big Data Science. Take reference or would like to start your training from our or yours idea on .

Find latest 2016-2017 topic ideas for M.Tech students, and for B.Tech students. Let us know your feedback and new ideas on .