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Name: Nandita Rao
Type: User
Company: MediaOcean
Location: New York City, New York
Name: Nandita Rao
Type: User
Company: MediaOcean
Location: New York City, New York
This is a web application that uses Struts2 framework and Hibernate ORM model. The app basically lets a user plan his itinerary for a day at Times Square. The database used is MySQL. Data related to Entertainment, Restaurant and Shopping in Times Square is pulled from open-data-source "nycopendata" and stored in the database. This information is provided to the user who can pick any of these events to plan his itinerary. It also has the provision where, if the user logs into facebook then with his permission, all his friends who are using this app too will get added on his friend list in the app automatically.
A Java application which uses multi-threaded programming , basically behaves like a chat system for bidding, where the Client side handles the putting up of items for sale and the bids by customers and the Server side handles the processing of the bids.
The application makes use of the Enron email corpus. It outputs the top 5 authoritative in-links and out-links. The purpose is basically to build a graph like structure that shows the authoritative in-link and out-link between each executive in the email corpus. All the processing is done using Amazon EMR.
The application requires the user to enter a location and a category, on providing this information the user gets all the tweets related to that category in the location entered by him, the top 10 buzz words in those tweets and the sentiment associated with those buzz words.
The application has 2 parts : 1)On-demand infrastructure 2)Elastic infrastructure. On-demand infratsruture: It has an on-demand provide and release mechanism as a base service, it also has the ability to snapshot a VM and then restore the snapshot as and when required. It persists data on a persistent storage. It uses CloudWatch to detect when machines are idle. Elastic infrastructure: Everytime the usage goes above a particular percentage, it creates a new instance for the user and if the machine is idle then the instance is terminated.
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The application is a cloud service that provides the functionality of performing sentiment analysis on stock market and financial data. The application can be hosted on Google App Engine and makes use of many of the GAE services like Search Service, MemCache, DataStore etc. Given the name of a company, data from various sources like Twitter, Facebook Graph, Google News, Google Finance etc is aggregated. For each source, different models have been pretrained using some prior data. Using different models provided us with a chance to utilize different Machine Learning methodologies based on the type of data from each source. The various techniques that we have built and tested on are :Naive Bayes, Multinomial and Bernoulli text representations, KNN.
The application provides similar services as Youtube. The user can upload and delete videos. He can also provide a rating and at all times average rating for a particular video is displayed. The user can also stream the videos, this is done using JWPlayer. The videos are stored in Amazon S3 buckets and are distributed using CloudFront which is a content distribution server. It has booth Streaming and Downloading distributions.
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