Malini M. Patil

Work place: Department of Information Science and Engineering JSS Academy of Technical Education, Bengaluru-560060, India



Research Interests: Bioinformatics, Image Compression, Image Manipulation, Image Processing, Data Structures and Algorithms


Dr. Malini M. Patil is presently working as an Associate Professor in the Department of Information Science and Engineering at J.S.S. Academy of Technical Education, Bangalore, Karnataka, INDIA. She received her Ph.D. degree from Bharathiar University in the year 2015. Her research interests are big data analytics, bioinformatics, cloud computing, image processing. She has published more than 20 research papers in many reputed international journals and guiding four students. She has attended and presented papers in many international conferences in India and Abroad. Published article, entitled "Performance analysis of Hoeffding trees in data streams by using massive online analysis framework" in International Journal of Data Mining, Modelling and Management, Inderscience Publishers. Published article, entitled "Mining Data streams with concept drift in massive online analysis frame work", WSES Transactions on computers”, She is a member of IEEE, ISTE, CSI, IEI. She is a recipient of Distinguished Woman in Science Award for the year 2017 from Venus International Foundation. She has received a best paper presenter award in Second International Conference of Data Management, Analytics and Innovation – ICDMAI-2018, Pune, India. The award was sponsored by springer. Contact email:

Author Articles
A Review on Data Analytics for Supply Chain Management: A Case study

By Anitha P Malini M. Patil

DOI:, Pub. Date: 8 Sep. 2018

The present study bridges the gap between the two intersecting domains, data science and supply chain management. The data can be analyzed for inventory management, forecasting and prediction, which is in the form of reports, queries and forecasts. Because of the price, weather patterns, economic volatility and complex nature of business, the forecasts may not be accurate. This has resulted in the growth of Supply chain analytics. It is the application of qualitative and quantitative methods to solve relevant problems and to predict the outcomes by considering quality of data. The issues like increased collaboration between companies, customers, retailers and governmental organizations, companies are adopting Big Data solutions. Big Data applications can be linked for Supply Chain Management across the fields like procurement, transportation, warehouse operations, marketing and also for smart logistics. As supply chain networks becoming vast, more complex and driven by demands for more exacting service levels, the type of data that is managed and analyzed also becomes more complex. The present work aims at providing an overview of adoption of capabilities of Data Analytics as part of a “next generation” architecture by developing a linear regression model on a sales-data. The paper also covers the survey of how big data techniques can be used for storage, processing, managing, interpretation and visualization of data in the field of Supply chain.

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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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