TO DEVELOP IMPROVE METHODS FOR BUSINESS PROCESS MODELING USING DATA MINING

Priyanka Mahakalkar — 2025-01-06

In today’s fast-evolving business landscape, managing business processes effectively has become increasingly important due to frequent changes in customer demands and the growing complexity of operations. Traditional approaches like workflow mining and process retrieval, while useful, often involve extensive manual effort. One prominent technique used in data mining is clustering, which divides datasets into meaningful groups by iterating through data and refining clusters until stable groupings are formed. Current search engines, however, struggle to provide personalized, comprehensive answers to tourists or visitors searching for specific information, such as transportation, tourist attractions, shopping options, accommodations, and restaurants within a city. To address this challenge, this research proposesaninnovative system thatintegratesdataminingtechniquestodelivertailored, efficientsolutions. Developedin a Hadoop environment, the system utilizes K-Means Clustering and Map Reduce to process large datasets and provide quick, personalized recommendations for travelers. The paper outlines the proposed architecture and demonstrates how this system can revolutionize the way tourism and transportation information is delivered, enhancing user experience by offering accurate, context-sensitive information.

International Journal for Research in Science Engineering & Technology

TO DEVELOP IMPROVE METHODS FOR BUSINESS PROCESS MODELING USING DATA MINING