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 Redback?

At Redback IT Solutions, Vellore 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 Redback IT Solutions )

BIGDATA (Hadoop) based DATA MINING

• Collaborative Filtering-Based Recommendation of Online Social Voting (IEEE 2017).
• A Health Decision Support System for Disease Diagnosis based on Wearable Medical Sensors and Machine Learning Ensembles (IEEE 2017).
• Predicting Social Emotions from Readers’ Perspective (IEEE 2017)..
• Real-Time or Near Real-Time Persisting Daily Healthcare Data into HDFS and Elastic-search Index inside a Big Data Platform (IEEE 2017).
• Learning to Extract Action Descriptions from Narrative Text (IEEE 2017).

BIGDATA (Hadoop) based WEB MINING

• Studying the Scope of Negation for Spanish Sentiment Analysis on Twitter (IEEE 2017).
• Statistical Features Based Real-time Detection of Drifted Twitter Spam (IEEE 2017).
• Improving Performance of Heterogeneous Map Reduce Clusters with Adaptive Task Tuning (IEEE 2017).
• Sorting of Fully Homomorphic Encrypted Cloud Data: Can Partitioning be effective? (IEEE 2017).)
• On Distributed Fuzzy Decision Trees for Big Data (IEEE 2017).