Modern technologies are now delivering great amounts of data generated from biological and biochemical systems. Alongside the remarkable advances in technology, there is a continuous demand for the development of statistical and machine learning methods able to translate these data into relevant information that can be incorporated in decision support systems. This talk will cover many applications where mathematics plays a promising role in the process of decision-making.

It’s a challenge to take insights of big volumes of data. In the case of retail and commerce, these insights can be, for example, relationships between products that are purchased in the same purchase act, through MBA (Market Basket Analysis). Also, it’s possible to apply a clusters analysis on these baskets and segment all kinds of visits that a particular store receives, as well as define Points of Interest. With this kind of analysis, it was possible to set the main objectives of my Dissertation. They are 1) to establish relations, with the help of Association Rules, between the purchased products in different baskets, using MBA; 2) group the different types of existing baskets in the dataset through clustering algorithms, defining different segments of baskets (shopping missions); and 3) group the Oil Stations, according to their behaviour regarding to some products sales. The results reveal the presence of associations between some products groups, as well as that there are some shopping missions beyond the simple purchase of fuel. Also, the results Oil Stations segmentation reveals that there are some similarities between stations regarding to specific product groups consumption. The analyzed dataset consists of transactional data, provided by GALP, containing baskets from January 2019 to November 2020, their transactions, and some geographic and temporal data to give some context.

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Marta Belchior Lopes is a researcher at NOVA School of Science and Technology and a member of the Center for Mathematics and Applications (CMA) and NOVA Laboratory for Computer Science and Informatics (NOVA LINCS). She holds a PhD in Biotechnology, a Master’s degree in Applied Mathematics, and a 5-year degree in Biology. Her main research activities have been focused on the development of mathematical and computational tools to extract meaningful information from high-dimensional data generated within pharmaceutical, biomedical, and environmental applications.

André Pedrinho has a Bachelor of Science in Engineering Sciences of Microelectronics and Nanotechnology, started in 2016 at FCT NOVA. In 2019, he started the Master's Degree in Analysis and Engineering of Big Data. Currently, he's finalist Master student and Data Scientist at BI4ALL.