Dr.Pavan Manjunath

Thesis: Blockchain-Enabled IoT Security with Post-Quantum Cryptography. Key Contributions: • Built end-to-end IoT load monitoring system (truck weight sensors → secure cloud/edge). • Designed Blockchain integrity layer (SHA-256) for tamper-proof IoT transactions. • Proposed two-layer encryption: Blockchain + Post-Quantum McEliece Cryptosystem with masking for high resilience. • Developed JSCL Algorithm integrating Blockchain + PQC into Software Defined Networks (SDN). • Implemented Operational/Decision/Forwarding planes for secure IoT routing. • Extended into AI/ML anomaly detection for IoT-blockchain data flows. Tech Stack: Blockchain • IoT Security • Post-Quantum Cryptography • Edge AI • SHA-256 • SDN • AI/ML • Encrypted Data Transmission • Smart Transportation

Thesis: Effective ETL Framework & Data Validation Tool Key Contributions: Researched automated validation of large-scale data pipelines without relying on commercial ETL tools. Designed a framework to test data transformation accuracy from multiple source systems into Hadoop-based destinations. Developed Excel-driven prototype for automated validation, ensuring consistency between source and processed data. Addressed challenge of verifying business logic transformations across diverse datasets. Proposed a reusable Data Validation Framework to improve data quality, integrity, and scalability in enterprise systems. Data Validation • Automated Testing • Big Data • Hadoop Framework • Data Quality • Transformation Logic • Source-to-Destination Consistency

Thesis Title: Guidant Data Warehousing Key Contributions: Designed and developed a Data Warehouse for Guidant Pharmaceuticals to generate flat files for Human Health Department reporting. Consolidated historical product sales data across heterogeneous sources (Oracle DB, XML files, and Flat Files) using Informatica ETL and Talend ETL tools. Categorized product data by group and family, enabling time-based usage analysis and seasonal trend reporting. Conducted comprehensive ETL/Data Warehouse testing across four engagements: New DW Testing: Built and validated DW from scratch. Migration Testing: Verified data migration across tools for efficiency gains. Change Request Testing: Integrated new business rules and data sources into existing DW. Report Testing: Validated report layout, data accuracy, and calculations for stakeholders. @Data Warehousing @ETL Testing @Informatica @Talend @Oracle @XML Integration @Reporting Validation @Health Care Domain @Hadoop @hive @hbase