Why BIG DATA Project?

In a day we process a numerous amount of data. If we take in to account the number of data it approximates up to nearly 2.5 quintillion bytes of data. All these Data’s comes from satellites which are used to gather many information’s like Climate, posts on social media, GPS signals, purchase records of online shopping and much more. We call this as Bid data. Any Company evaluates their technical and Economical trends based on the values of these data. These Data’s play a significant role in analysing the growth and fall statistics which aids for future Plans. The collection of these data’s and it’s processing requires a lot of technicality. Complicated techniques are not today’s strategy instead we go for Big data analysis.

Why BIG DATA Project at UNIQ?

At UNIQ Technologies, Coimbatore we offer IEEE final year projects on Big data. We train the students with the latest techniques adopted in Big data. Care is taken such that every module is learnt carefully with proper Documentation and graphical analysis. We provide A to Z materials for each project modules and guide them for review presentations till the end of their academic project. Training is more practical with graphical analysis rather than theoretical. A Convenient time is provided for training to facilitate students coming from different parts of the state. So why wait? Join us for better future ahead.

BIG DATA Projects

( Powered by UNIQ Technologies )


I. BIG DATA (Hadoop) Based DATA MINING (IEEE 2019)

1. Exploiting Aesthetic Features in Visual Contents for Movie Recommendation
2. Deep collaborative filtering for prediction of disease genes
3. A Joint Two-Phase Time-Sensitive Regularized Collaborative Ranking Model for Point of Interest Recommendation
4. A Prediction Approach for Stock Market Volatility Based on Time Series Data
5. Exploring Variability within Ensembles of Decadal Climate Predictions

II. BIG DATA (Hadoop) Based WEB MINING (IEEE 2019)

1. SentiDiff: Combining Textual Information and Sentiment Diffusion Patterns for Twitter Sentiment Analysis
2. Spammer Detection and Fake User Identification on Social Networks
3. Sentiment Lexicon Construction with Hierarchical Supervision Topic Model
4. Enabling Trusted and Privacy-preserving Healthcare Services in Social Media Health Networks
5. Investigating the Schedulability of Periodic Real-Time Tasks in Virtualized Cloud Environment