Big Data Analytics Lab
Big Data Analytics Lab
Overview
The Big Data Analytics Lab supports undergraduate and postgraduate students, along with research scholars, by providing hands-on exposure to modern data processing technologies. It enables learners to work with distributed frameworks for large-scale data storage and computation. The lab facilitates the use of the Hadoop Distributed File System (HDFS) and NoSQL databases, including MongoDB, HBase, and Cassandra, for handling unstructured and semi-structured data. Additionally, it serves as a platform for experimenting with machine learning techniques such as clustering, regression, and classification.
Objectives
- To support advanced research activities in big data analytics and data-driven decision-making.
- To provide a research-oriented environment for experimentation with large-scale and real-world datasets
- To facilitate both academic and applied research through hands-on exploration of emerging analytics techniques
- To foster interdisciplinary research collaboration across multiple departments and application domains
- To encourage active involvement of faculty and students in innovative research projects and problem-solving initiatives.
- To support scholarly publications, funded research initiatives, and the dissemination of research outcomes
Key Focus Areas
- The Big Data Analytics Lab focuses on core big data technologies, tools, and frameworks for large-scale data processing.
- It emphasizes data engineering and data management techniques for handling structured and unstructured data.
- The lab supports artificial intelligence approaches for intelligent data analysis.
- It enables advanced data analytics and visualization to extract insights and present information effectively.
- The lab facilitates scalable and real-time analytics using distributed and cloud-based computing platforms.
- It incorporates business and decision analytics while addressing data security, privacy, and ethical considerations
On-going Project Titles:
- Reducing AI Hallucinations in Indian Languages using a Multi-Agent "JSON-Pivot" Framework.
- Liver Cancer Detection System
Research Publications:
Vineetha, Borra, and Munirathinam Nirmala. “Tri-Algo guardian ensemble approach for fake news detection in social media.” Journal of Big Data 12, no. 1 (2025): 118.
- Khekare, Urvashi, and Rajay Vedaraj IS. “Adaptive deep Q-networks for accurate electric vehicle range estimation.” Frontiers in Big Data 8 (2025): 1697478.
Tyagi, Bhawana. “Hybrid synthetic minority over-sampling technique (HSMOTE) and ensemble deep dynamic classifier model (EDDCM) for big data analytics.” Scientific Reports 15, no. 1 (2025): 1-32.
