Big Data
Big data research focuses on managing, analyzing, and extracting insights from massive datasets, primarily using machine learning and deep learning techniques. Current research emphasizes efficient algorithms and architectures, such as transformers, recurrent neural networks (like LSTMs), and various tree-based models, often within object-oriented programming frameworks to improve scalability and maintainability. This field is crucial for advancements in diverse sectors, including finance (risk management), healthcare (disease prediction), and environmental science (e.g., forest fire prediction), by enabling more accurate and timely decision-making through data-driven models.
Papers
What's In My Big Data?
Yanai Elazar, Akshita Bhagia, Ian Magnusson, Abhilasha Ravichander, Dustin Schwenk, Alane Suhr, Pete Walsh, Dirk Groeneveld, Luca Soldaini, Sameer Singh, Hanna Hajishirzi, Noah A. Smith, Jesse Dodge
Fraud Analytics Using Machine-learning & Engineering on Big Data (FAME) for Telecom
Sudarson Roy Pratihar, Subhadip Paul, Pranab Kumar Dash, Amartya Kumar Das