Knowledge management and decision-making

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:
  • 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 TessarisProjects 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

  1. Girard, John P.; Girard, JoAnn L. (2015). “Defining knowledge management: Toward an applied compendium”. Online Journal of Applied Knowledge Management. 3 (1): 14.
  2. Wikipedia contributors, “Knowledge management,” Wikipedia, The Free Encyclopedia, Link (accessed May 29, 2017).
  3. Wikipedia contributors, “Mathematical model,” Wikipedia, The Free Encyclopedia, Link (accessed May 29, 2017).
  4. Wikipedia contributors, “Knowledge acquisition,” Wikipedia, The Free Encyclopedia, Link (accessed May 29, 2017).
  5. Wikipedia contributors, “Data analysis,” Wikipedia, The Free Encyclopedia, Link (accessed May 29, 2017).
  6. Wikipedia contributors, “Machine learning,” Wikipedia, The Free Encyclopedia, Link (accessed May 29, 2017).
  7. Wikipedia contributors, “Data mining,” Wikipedia, The Free Encyclopedia, Link (accessed May 29, 2017).
  8. Wikipedia contributors, “Process mining,” Wikipedia, The Free Encyclopedia, Link (accessed May 29, 2017).
  9. Wikipedia contributors, “Time series,” Wikipedia, The Free Encyclopedia, Link (accessed May 29, 2017).
  10. Wikipedia contributors, “Knowledge representation and reasoning,” Wikipedia, The Free Encyclopedia, Link (accessed May 29, 2017).
  11. Wikipedia contributors, “Computational mathematics,” Wikipedia, The Free Encyclopedia, Link(accessed May 29, 2017).
  12. Wikipedia contributors, “Decision-making,” Wikipedia, The Free Encyclopedia, Link (accessed May 29, 2017).