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aspire-final

aspireui-new

bandwidth-efficient-byte-stuffing-implementation-

Implementation of Bandwidth-Efficient Byte Stuffing, based on IEEE ICC 2007 paper by Jamie S Cardoso, University of Porto, for Networks Lab, using Raw-Socket Programming in C.

dmallya93.github.io

evil_geniuses_phase3

• Developed a Spark program to calculate the Getis-Ord statistic of NYC Taxi Trip datasets(1.8 GB Dataset) using 4 Hadoop clusters. • Implemented using the Getis-Ord statics by finding the z-score of the data points to calculate the starting point of the top 50 hotspots.

https-dmallya93-dev.chain-builder.net-chainbuilder-chainbuilder

intelligent-web-services-discovery-using-clustering-method

Building a search engine to discovery web services specified using a natural language query using hierarchical clustering, implemented using Phython and DISCO (lingua tools). Clustering of upto 20,000 web services was performed.

interactive-wallpaper

Providing an interactive wallpaper with basic graphic principles of scan-filling algorithm, Bresenham's algorithm, DDA, Animation algorithm on a OpenGL and C based platform.

mc_assignment-1

Mobile Computing Assignment 1

sociopedia-twitter-knowledge-engine

Building a search engine to discovery web services specified using a natural language query that infers relationships using an ontology of Twitter data. Technologies used are NLTK, Python, Whoosh, Django and CMU Ark Tweet Parser. The fast information sharing on Twitter from millions of users all over the world leads to almost real-time reporting of events. It is extremely important for business and administrative decision makers to learn events popularity as quickly as possible, as it can buy extra precious time for them to make informed decisions. Therefore, we introduce the problem of predicting future popularity trend of events on microblogging platforms. Traditionally, trend prediction has been performed by using time series analysis of past popularity to forecast the future popularity changes.