Pratik MishraPratik Mishra

Authors

Mohit Sharma*, Pratik Mishra*, Sandeep Hans, Abhijnan Chakraborty, Vijay Arya

Mohit Sharma and Pratik Mishra contributed equally to this work.

Abstract

The performance of machine learning (ML) models heavily depends on the quality of the data they are trained on. While prior work often treats data quality as uniform across a dataset, we investigate whether it varies across different population subgroups within a dataset and examine its implications, a phenomenon we refer to as Data Quality Disparity (DQD). Our analysis reveals that many real-world datasets inherently exhibit DQD with underrepresented or marginalized groups frequently associated with lower-quality data compared to more privileged subgroups...

Authors

Pratik Mishra, Caner Gözübüyük, Seema Nagar, Prateeti Mohapatra, Raya Wittich, Arthur de Magalhaes

Abstract

Manual creation of IT monitoring dashboard widgets is slow, error-prone, and a barrier for both novice and expert users. We present NOVAID, an interactive chatbot that leverages Large Language Models (LLMs) to generate IT monitoring widgets directly from natural language queries. Unlike general natural language–to-visualization tools, NOVAID addresses IT operations–specific challenges -- specialized widget types like SLO charts, dynamic API-driven data retrieval, and complex contextual filters...