In a world where the potential of GenZs is often questioned, two of our students, Md. Hadi Mahmood and Md Ahmed Raza Khan have proven that their engagement can deliver meaningful change. These III-year BTech CSE students have showcased an inspiring display of their intellect and innovative prowess at the Nation Building Case Study Competition, an annual event that aligns with the “Viksit Bharat” initiative led by Hon’ble Prime Minister Shri. Narendra Modi, inviting college teams from across the nation to develop creative strategies aimed at transforming India into a developed country by 20247.

Here’s an excerpt of their interview:

1. What was the competition/event about?

The NationBuilding Case Study Competition is an annual event organised to inspire young college students to contribute to India’s development by addressing critical national issues. Participants engage in a 2-month-long journey involving multiple rounds, including an online quiz, presentation submissions, zonal finals, and the national finals in New Delhi. The competition is judged by a distinguished panel of experts in the given domain.

This year’s competition, NationBuilding Case Study Competition 2025, focused on the problem statement of identifying the gaps in India’s sports environment. Participants were tasked with studying the sports systems of other countries, analysing their strengths, and drafting a model to achieve India’s vision of hosting and winning 100 medals in the Olympics by 2036. This theme aimed to encourage innovative solutions to elevate India’s standing in global sports and align with the broader goal of nation-building.

2. What place did you secure?

We secured the 1st rank in the South Zone during the third round of the competition, competing against teams from multiple prestigious institutions. This achievement has advanced us to the national finals, where we will compete against the top 2 teams from each of the six zones: North, South, East, North East, West, and Central.

3. How did you find out about the competition?

We discovered this opportunity on the Unstop platform. The NationBuilding Case Study Competition is widely recognised and attracts thousands of teams annually, making it a highly sought-after event for students across India.

4. Who were your competitors

In the South Zone, we competed against 10 teams from premier institutions. The competition was intense, as participants brought innovative solutions to the table, reflecting the high level of talent and dedication among the youth.

5. Your feelings on advancing this far and future aspirations.

We are excited to have advanced to the national finals. Competing against the best teams from across the country is both a challenge and an opportunity to showcase our strategic thinking and problem-solving skills. This competition has allowed us to apply our skills & knowledge to real-world issues, which has been incredibly rewarding. We look forward to the final round and hope to contribute meaningfully to the vision of a developed India.

This research paper, by Dr Ashu Abdul, Assistant Professor and research scholar Ms Surya Samantha Beri along with fourth-year student, Mr Jakkampudi Venkatasubbaiah from the Department of Computer Science and Engineering explore a framework designed to help users retrieve and analyse data without requiring any Structured Query Language (SQL) knowledge. The paper titled, “System and Method for Generating Structured Queries from Natural Language Inputs“, is particularly relevant as it enables individuals, like a car dealer seeking sales insights, to interact with their databases using everyday language. Such accessibility underscores the importance of this research in democratising data access for all users.

Abstract:

This project is centred around creating a framework that translates user queries into SQL statements and retrieves results without requiring any SQL knowledge from the user. By delving into the workings of various RDBMS systems, with a special focus on MySQL, I developed a solid understanding of database architecture and how databases are engineered for optimized performance. This knowledge was critical in designing a system that can seamlessly interact with any given database, analyse it, and provide relevant results in response to user input.

About the Framework:

This framework is intended to convert the user queries into sql statements and attain the results from the database without intervention of sql coding. Every time writing multiple SQL commands to apply filters, commands, and extracting data is time-consuming and requires having knowledge of SQL knowledge. This project is intended to analyze a database and answer the questions that are related to a particular database without writing sql commands.

Explanation in Layperson’s Terms:

In-General we rely on programmers who are efficient in programming SQL for finding the insights from the database which are related to business or information. So, Laymen cannot access data without knowing SQL this project makes it possible. Assume, Mr. A a car dealer owns a showroom has a software dealing with his Business he wants to access his data and get insights for understanding the sales. Now he is not familiar with using SQL so he relies on someone for that or opens software and applies multiple filters to analyze his data. But What if he can use a chatbot and get solutions for all his questions from his database?
Mr.A can get conclusions from his data within no-time that thought represents this entire project.

What are the use cases of this framework to a layman?

Laymen interaction with the database for understanding their data. Reducing the requirement to understand and search for the filters in the front-end. Faster data extraction from the database. Generating the results based on user queries in natural language without sql coding. Elimination of time and efforts required for writing SQL Commands or applying filters. Understanding data gets easier for engineers as well as unknown data can be understood easily.

Practical Implementation:

This project has been successfully integrated into several existing real-time applications, enabling precise identification of data locations. By fine-tuning and enhancing our algorithms, we have achieved significant improvements in accuracy. In practical terms, users can effortlessly explore and comprehend their data.

Furthermore, extensive testing across databases of varying sizes has demonstrated the project’s ability to deliver significant and well-structured results.

Future Enhancements:

Incorporating Natural Language Processing (NLP) to process and respond to queries in users’ native languages, including speech-to-text capabilities.

Facilitating the generation of dynamic reports in various formats such as PDFs and Excel sheets.

Expanding compatibility to support additional database systems like Oracle, PostgreSQL, and NoSQL models.

Enabling data extraction and analysis from Excel sheets and CSV files.