Basavaraj N. Hiremath

Work place: Dept. of Computer Science and Engineering, JSSATE Research Centre, J.S.S Academy of Technical Education, Bengaluru, Karnataka



Research Interests: Artificial Intelligence, Swarm Intelligence


Basavaraj N Hiremath is a research scholar working in the field of artificial intelligence at JSSATE Research Centre, Dept. of CSE, JSSATE, affiliated to VTU Belagavi, INDIA. He is pursuing research under Dr. Malini M Patil, Associate Professor, Dept., of ISE, JSSATE. Completed M. S. in Computer Cognition Technology in 2003 from Department of studies in Computer science, University of Mysore, Karnataka INDIA.

He has also worked in information technology industry as SOLUTION ARCHITECT in data warehouse, business Intelligence and analytics space in various business domains of Airlines, Retail, Logistics and FMCG. Published article, Basavaraj N Hiremath, and Malini M Patil, "A Comprehensive Study of Text Analytics", CiiT International Journal of Artificial Intelligent Systems and Machine Learning, 2017/4, vol 9, no 4,70-77

Mr Hiremath is a Fellow of Institution of Engineers (India), member IEEE, member Computer Society of India and Life member Indian Society for Technical Education, Member Association for the Advancement of Artificial Intelligence.

Author Articles
A Systematic Study of Data Wrangling

By Malini M. Patil Basavaraj N. Hiremath

DOI:, Pub. Date: 8 Jan. 2018

The paper presents the theory, design, usage aspects of data wrangling process used in data ware housing and business intelligence. Data wrangling is defined as an art of data transformation or data preparation. It is a method adapted for basic data management which is to be properly processed, shaped, and is made available for most convenient consumption of data by the potential future users. A large historical data is either aggregated or stored as facts or dimensions in data warehouses to accommodate large adhoc queries. Data wrangling enables fast processing of business queries with right solutions to both analysts and end users. The wrangler provides interactive language and recommends predictive transformation scripts. This helps the user to have an insight of reduction of manual iterative processes. Decision support systems are the best examples here. The methodologies associated in preparing data for mining insights are highly influenced by the impact of big data concepts in the data source layer to self-service analytics and visualization tools.

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