Optimizing RPA for Intelligent Data Extraction from Heterogeneous Databases

Tapan Kumar Rath, Sibaram Prasad Panda

Abstract: We present a method for optimizing Robotic Process Automation (RPA) focused on intelligent data extraction from heterogeneous databases and creating a new record of aggregated information. The databases may differ in structure and in the kind of products registered on them. The method improves RPA efficiency in terms of FTE-hours and runtime. RPA aims to automate routine tasks carried out by humans, thus freeing their time for more creative activities. Robotic Process Automation is software-based technology that allows companies to create automated process flows that interact with different 'Digital Workers'. RPA employs a Digital Worker, called 'the Robot', who is created in the image of the Human Worker, and is programmed to execute the same repetitive route and follow the same rules of Human Workers on Digital Systems. The Employees spend their working hours completing a variety of simple, repetitive operations. The purpose of RPA is to enable the rapid and cheap construction of software tasks, which can be easily deployed and targeted at existing IT Systems, with enough flexibility to be able to take on all different exceptions programmers would normally be needed to cater for through traditional programming techniques.

Keywords: Robotic Process Automation (RPA), Natural language processing (NLP), Machine Learning (ML), Artificial Intelligence (AI), Transactional data, Relational databases

How to Cite?: Tapan Kumar Rath, Sibaram Prasad Panda, "Optimizing RPA for Intelligent Data Extraction from Heterogeneous Databases", Volume 14 Issue 5, May 2025, International Journal of Science and Research (IJSR), Pages: 610-618, https://www.ijsr.net/getabstract.php?paperid=SR25509102041, DOI: https://dx.doi.org/10.21275/SR25509102041