- Redesigned database structure to align with industry standards, improved existing Events and Procedures, and implemented an Archival system to move year-old data - Streamlined report generation by replacing Python-based workflows with efficient SQL solutions, reducingcomplexity, enhancing communication, cutting review time by 60%, improving accuracy, and minimizing logical errors.- Working on Azure data pipelines, automated data insertion tasks, and optimizing query performance through indexing, locking, and server parameter adjustments.

User Activity TrackingThe primary goal of this project was to capture user activity sequences and identify the active and passive time spent by users on each medical document during processing.Devlopment work:> Implemented default and custom triggers with Matomo to track state changes (e.g., clicks, scrolls, screen events).> Designed the relational database (RDBMS) and table structure to store Matomo logs as per their guidelines.> Logged the data generated by Matomo for each screen event in respective tables.>Since Matomo provided only event data per action, I developed procedures to distinguish documents within a session by identifying start and end points based on specific entry and exit events.> To reduce high query times from document separation procedures, I created timed events that preprocessed data and stored it in an intermediate table. This optimization reduced wait times and simplified final queries for analytical tools like Metabase.> Report Format: For any given user, the report provided details on document access (open and close times), entry mode (view/edit), active time, idle instances (count), and exit mode (save/review to next level).> Beyond document-level analysis, the system revealed usage patterns, identifying frequently used features and event patterns.> Overall accuracy exceeded 95%, with minor inconsistencies due to factors like unexpected page closures, low network speeds, and data loss during logging. The system was able to detect these scenarios for more robust analysis.

(SDE Intern | Project: DPDK based RTE Flows R&D in Preparation for Napatech)◦ Developed efficient C code for high-performance packet processing in DPDK, focusing on GTP packetidentification, decapsulation, and advanced filtering (port&IP).◦ Implemented optimized interrupt handling in DPDK to reduce latency and enhance packet processing andfiltering.◦ Leveraged low-level C programming and hardware acceleration in DPDK for significant performanceimprovements in packet manipulation.