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android-messenger

This project aims to provide ordering guarantees to five AVD's group messenger. The project guarantees both total ordering as well as FIFO ordering. We store all the messages in the content provider. The different is that when we store the messages and assign sequence numbers, the project mechanism provides total and FIFO ordering guarantees.

apache-solr-wikipedia-and-related-news-articles

The project aims to provide a customized search engine for Wikipedia Articles and once the user clicks on the Article, a related news is also retrieved using a the description of the article to formulate a search query. Also, relevant facebook post will also be shown

aws-node-termination-handler

Gracefully handle EC2 instance shutdown within Kubernetes

aws-simple-ec2-cli

aws-simple-ec2-cli-1

A CLI tool that simplifies the process of launching, connecting and terminating an EC2 instance.

boolean-search-engine

Project parses the news corpus and retrieves all the relevant information from the article such as Author, Date, Place, Titile etc. by parsing it and indexes these fields in separate indexes. The user when enters his search query, then a boolean query is formulated using AND, OR and NOT and relevant results are retrieved along with snippets. We use Okapi BM25 model for tf-idf in order to rank the documents.

ec2-fleet-plugin

The EC2 Spot Jenkins plugin launches EC2 Spot instances as worker nodes for Jenkins CI server, automatically scaling the capacity with the load.

handwritten-digits-classification

A Multi-Layer Perceptron Neural Network approach for determining hand-written digits along with regularization.

homebrew-tap

Homebrew formulae that allows installation of AWS tools through the Homebrew package manager.

nasdaq-stocksvolatility-mapreduce

The project is aimed to find top ten most volatile stocks and top ten least volatile stocks using HDFS. HDFS is designed to store very large datasets reliably. Given are three datasets called large, medium and small. These datasets contain many files; each having stocks pricing data with respect to the company. We aim to use MapReduce in order to speed-up the process of reading files and processing them in parallel. The input files are read by multiple mappers and partitioned into smaller parts for their processing. The mapper then outputs <key, value> pair which serves as an input to the reducer. Reducer then takes the <key, List<value>> pair as it’s input and processes and outputs the results to the user.

repository-permissions-updater

Artifactory permissions synchronization tool and data set

sentiment-analysis

This Project involves a process of analyzing sentiments about any particular movie using user reviews available on social networking sites like Facebook and Twitter into categories namely, Positive and Negative. The idea behind this was to help user make better judgement about the product by reading only positive reviews or negative reviews related to the product. Sentiment analysis involved extraction and measurement of the sentiment or “attitude” of a review using natural language processing steps such as stemming, stop-words removal and formation of similarity matrix using Stanford NLP libraries.

simple-dht

The project implements a simple DHT based on Chord. Although the design is based on Chord, it is a simplified version of Chord; we do not implement finger tables and finger-based routing. There are three things are implemented: 1) ID space partitioning/re-partitioning, 2) Ring-based routing, and 3) Node joins. The app has an activity and a content provider. However, the main activity is be used for testing only and does not implement any DHT functionality. The content provider implements all DHT functionalities and supports insert and query operations. Thus, if we run multiple instances of the app, all content provider instances form a Chord ring and serve insert/query requests in a distributed fashion according to the Chord protocol.

simple-dynamo

The main goal of this project is implementing a simplified version of Dynamo. There are three main pieces that are included: 1) Partitioning, 2) Replication, and 3) Failure handling. The main goal is to provide both availability and linearizability at the same time. In other words, the implementation always perform read and write operations successfully even under failures. At the same time, a read operation should always return the most recent value. Project provides you both availability and linearizability at the same time. The exception is partitioning and replication, which should be done exactly the way Dynamo does.

terraform-provider-aws

Terraform AWS provider