Knowledge management is the process of creating, sharing, using and managing the knowledge and information of an organization. [1]. (See also [2].) Professors interested in knowledge management are: Diego Calvanese, Enrico Franconi, Giancarlo Guizzardi, Marco Montali, Werner Nutt, Sergio Tessaris, Markus Zanker, Roberto Confalonieri, Rosella Gennari, Andrea Janes, Oliver Kutz, Simon Razniewski, and Davide Taibi.

Related to knowledge management is also decision making, which includes all problem-solving activities that are concluded finding a satisfactory solution [12]. Professors interested in decision making are: Enrico Franconi, Johann Gamper, Sven Helmer, Bruno Carpentieri, Marco Montali, Francesco Ricci, Romain Robbes, Barbara Russo, Markus Zanker, Roberto Confalonieri, Anton Dignös, Mehdi Elahi, Vincenzo Del Fatto, Ilenia Fronza, Andrea Janes, Mouna Kacimi El Hassani, Fabio Persia, Daniele Porello, Panagiotis Symeonidis, and Marko Tkalcic.

Currently we adopt the following methods to support knowledge management and decision-making:

- Knowledge acquisition, which is concerned with the definition of rules and ontologies required for a knowledge-based system [3]. Professors interested in knowledge acquisition are: Francesco Ricci, Markus Zanker, Simon Razniewski, and Marko Tkalcic.
- Mathematical modeling, which is concerned with the description of a system using mathematical concepts and language. A model may help to explain a system and to study the effects of different components, and to make predictions about behaviour [4]. Professors interested in mathematical modeling are: Bruno Carpentieri, Francesco Ricci, Markus Zanker, Ilenia Fronza, Panagiotis Symeonidis, and Marko Tkalcic.
- Data analysis, is a process of importing, processing, and modeling data with the goal of discovering patterns or useful information, that can be used for prediction and decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques, which depends on the application domain. Typical domains where data analysis is useful are eHealth, eTourism, eScience, and SmartCities. (See also [5].) Almost everybody in the faculty deals with data analysis, as it is part of science to collect data about the phenomenon one is observing. In this context, we look at four methods in particular:
- Machine learning, which explores the study and construction of algorithms that can learn from and make predictions on data [6]. Professors interested in machine learning are: Claus Pahl, Francesco Ricci, Romain Robbes, Barbara Russo, Markus Zanker, Tillo Tammam, Roberto Confalonieri, Mehdi Elahi, Mouna Kacimi El Hassani, Simon Razniewski, Panagiotis Symeonidis, and Marko Tkalcic.
- Data mining aims to discover patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems [7]. Professors interested in data mining are: Johann Gamper, Francesco Ricci, Romain Robbes, Barbara Russo, Mouna Kacimi El Hassani, Simon Razniewski, Panagiotis Symeonidis, and Marko Tkalcic.
- Process mining focuses on discovering a process based on data traces left behind from the execution of a process. The result is a graphical representation of the a process model with the goal, on the one hand, of comparing this model with the one previously designed (for conformance checking), and, on the other hand, of enriching the model (with business intelligence, KPIs, etc.). (See also [8].) Professors interested in process mining are: Diego Calvanese, Marco Montali, Romain Robbes, Barbara Russo, and Andrea Janes. Projects in this area include: OnProm and Kaos.
- Time-series data anaysis, comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data [9]. Professors interested in time-series data anaysis are: Johann Gamper, Romain Robbes, and Barbara Russo. Projects in this area include: Dasa and Sustainable Agricolture.

- Knowledge representation and reasoning, which is a field of artificial intelligence dedicated to representing information about the world in a form that a computer system can utilize to solve complex tasks [10]. Professors interested in knowledge representation and reasoning are: Alessandro Artale, Diego Calvanese, Marco Montali, Werner Nutt, Sergio Tessaris, Roberto Confalonieri, Oliver Kutz, Rafael Penaloza Nyssen, Daniele Porello, and Guohui Xiao.
- Data and knowledge-aware processes, which is a line of research is concerned with investigating the dynamic properties of systems that evolve over time, and where data plays a central role, and is not abstracted away, as currently done in many process modeling formalisms. The challenging aspect of this kind of systems is that they are inherently infinite states, hence, any kind of inference task, such as verifying temporal properties over the evolution of such systems or synthesising the specification of such a system starting from temporal conditions, becomes undecidable. In order to ensure that these tasks stay decidable, sufficient and possibly tight conditions need to be investigated, that ensure that the infinite states can be abstracted away, obtaining a finite state system over which to apply traditional finite state techniques used in formal verification. Professors interested in data and knowledge aware processes are: Diego Calvanese, Marco Montali, Werner Nutt, and Sergio Tessaris. Projects in this area include: OnProm, Kaos, and ACSI.
- Computational mathematics, which is is focused on mathematical models that are applied to study and simulate real complex systems. It includes numerical methods used in scientific computation, for example, numerical linear algebra, numerical solution of partial differential equations, and self-adaptive and error-reducing algorithms. (See also [11]). Professors interested in computational mathematics are: Bruno Carpentieri.

### References

- Girard, John P.; Girard, JoAnn L. (2015). “Defining knowledge management: Toward an applied compendium”.
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