Using the AMIDST Toolbox, we prototyped models for early recognition of traffic
manoeuvre intentions using object oriented graphical models. Data was collected by car on-board sensors giving rise to a
large and quickly evolving data stream. This work was performed in collaboration
with DAIMLER and Hugin Expert.
Using the AMIDST Toolbox, we developed a model to do risk prediction in credit operations.
Data was collected continuously and reported on a monthly basis, this gives rise to a
streaming data classification problem. This work has been performed in collaboration
with one of our partners, the Spanish bank BCC.
Masegosa, A. R., Martínez, A. M., Ramos-López, D., Langseth, H., Nielsen, T. D., & Salmerón, A. (2020). Analyzing concept drift: A case study in the financial sector. Intelligent Data Analysis, 24(3), 665-688.
Using the AMIDST Toolbox, we worked on models to detect changes in pressure measurements at
the bit, and spikes in the pressure that is measured at the rig. This work has been
performed in collaboration with Verdande.
This technology transfer project (subject to a “non-disclousure agreement”) was also made in collaboration with the company Gobile. Within this project we developed novel data mining methods for predicting the spatio-temporal occurrence of the crimes of a city based on the historical record of crimes. We started from the fact that crimes do not distribute uniformly across the city. They tend to concentrate in some areas and at some time intervals. Police forces used this knowledge when making decisions over the assignment of the scarce resources. However, the application of data mining and machine learning techniques provides a rigorous approach to deal with this information and allows making better-informed predictions and decisions.
This technology transfer project (subject to a “non-disclousure agreement”) was made in collaboration with the company Gobile. The project mainly consisted on the design of an artifical based software to address the problems in the design of a sales forece. This problem involves the solution of several interrelated problems: sizing the sales force, the problem of finding the appropiate number of salesman; salesmen location, the problem of selecting the location of each salesman in one sales covarage unit; sales territory alignment, the problem of grouping or clustering sales coverage unit into larger geographical groups; and sales resources allocation, the problem of assigning work hours of the salesman todifferent sales territories, considering also a broad set of restrictions which are normallyassociated to it.