Projects

1. CSE Dashboard

This is a Web application dashboard for TAMU CSE Students, Student Bodies and Administrators, that we made as part of the course CSCE 606. The team members were: Satya Kesav, Krit Gupta, Pranoy Kovuri, Sandeep Gottimukkala and Kevin Mathew (Team CosmicSoft).

A Running Instance can be seen at CSE-DashBoard, while the documentation for the project can be seen here.

The youtube channel for the project can be seen here.

The project was developed in a complete Agile environment, with multiple iterations, each encompassing a short product demo (with completed features) to the client, TDD/BDD, regular and proper documentation. We achieved a test coverage of over 93%!

Technologies/Frameworks/Languages used : Ruby on Rails, Heroku, Cloud9, PostgreSQL, Bootstrap, Pivotal Tracker, Git, JavaScript, SimpleCov, Capybara, Cucumber.

2. Database Schema Design and Implementation

#Tagged

The project is trying to store data generated on Facebook (particularly related to the pictures) in an Instagram-inspired database, so that operations can be performed on it. The Schema of the Database has been designed keeping in mind how the data is stored on the Instagram servers.

The ER-Diagram and the Relational Schema Diagram are attached in the project report.
The database has been normalised to 3.5NF (Boyce–Codd normal form - BCNF)

Frameworks/Languages used: Node.js, Express, MySQL Database, Heroku with a ClearDB add-on, MySQLWorkbench, Python for web scraping data (script attached in the project report)

Additional libraries: Faker, urllib2, json (python library for reading data).

The project report can be accessed here.

The URL of the project is: https://infinite-hamlet-44584.herokuapp.com/ 􏰀

3. Implementation of Tiny-SQL Compiler

The project designs and implements TinySQL (a basic and simple SQL). The language’s grammar has been provided in the project statement. The project makes an interpreter which utilizes a scanner to read the given input language commands – manually and multiple statements at once from a .txt file. It then understands the language, breaks it down into components and runs sub-routines based on the kind of the input statement. It then generates output based on mimicking reading and writing to an actual disk (disk I/Os) and outputs the results of the language based on the execution of the statement. The following components are included in the project:

Parser: Accepts the input Tiny-SQL queries and converts into a parse tree.
Logical Query Plan Generator: Converts a parse tree into a logical query plan. And also includes optimisations. (1 Pass and 2 Pass Algorithms)
Physical Query Plan Generator: Converts optimised logical query plans into executable physical query plans.
Subroutines: Implement various data query operations used for the execution of Tiny-SQL queries.

The detail project report can be viewed here.

Languages/Framework: Java, StorageManager Library (given in the project statement)

4. Network Optimisation

The project deals with the Maximum Bandwidth Path Problem. The project implements a network routing protocol using data structures and algorithms. Working on the problem of the Maximum-Bandwidth-Path Problem and having implemented three algorithms, namely:

Modified Dijkstra’s Algorithm
Modified Dijkstra’s Algorithm using a heap structure for fringes
Modified Kruskal's Algorithm

The report discusses the implementation and the results of the implementation of these three algorithms on two types of graphs:

Sparse Graph – Graph with 5000 vertices. The average vertex degree is 8. The graph edges have been assigned random positive weights.
Dense Graph – Graph with 5000 vertices. Each vertex is adjacent to about 20% of the other vertices, which are randomly chosen, which means that each vertex is connected to an approximate 1000 other vertices. The edges have been assigned random positive weights. In the process of the implementation, I created various other data structures, such as:
Max – Heap – needed for modified Dijkstra’s Algorithm.
Set (Union – Find) – needed for modified Kruskal’s Algorithm. The results are stored in the results.xlsx file, and the attached project report discusses the results in details.

Implementation Language - Python

5. Machine Learning and Web Development Projects

While learning through various online tutorials, I have developed a few side projects. Nothing fancy, just basic stuff to learn various concepts.

The Web Development projects can be found here.

The Machine Learning/ Data Science projects can be viewed here.

This goes without saying that as and when I learn something new and do an implementation, I will be updating the GitHub profile accordingly.

6. Automation Bots

While working in NextSpace Technologies back in India, I had the opportunity to develop various bots to automate routine jobs.

Various Frameworks/Languages/Libraries used were: Python, AppleScript, Excel Macros, Flask (Basic), WebScraping -BeautifulSoup, Urllib2, Selenium, Webdrivers - Chrome webdriver & geckodriver.

More information can be found here.

7. Android Application Mobile Development

Through various online tutorials, I learnt the basics of Android Development and got to apply the skills while working at NextSpace Technologies. Being a startup, I got to experiment and try various technologies with a talented bunch of people, Android development being one.

One of the mobile application that we developed was for a construction company - Mahabir. The backend and the APIs were built on Ruby on Rails, while the Android Application, simply rendered the APIs on different pages.

The app can be seen here.

Another mobile application that I built was in my final year B.E. Project, which connected itself to a Hear Rate Monitor through Bluetooth, received data and sent the readings to the doctor as an email attachment.

Frameworks/Languages used: Java, XML, Android Studio, Eclipse.

I will be soon working on another mobile application in React Native for the startup, Vize, that I am currently a part of.

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