Section: Research Program

Decision support

Predictive sequential patterns have a direct application in diagnosis. LACODAM inherits a strong background in decision support systems with internationally recognized expertise in diagnosis from the former DREAM team. This AI subfield is concerned with determining whether a system is operating normally or not, and the cause of faulty behaviors. The studied system can be an agro- or eco-system, a software system, a living being, etc.

The increasing volumes of data coming from a range of different systems (ex: sensor data from agro-environmental systems, log data from software systems, biological data coming from health monitoring systems) can help human and software agents make better decisions. Hence, LACODAM builds upon the idea that decision support systems (an interest bequeathed from DREAM) should take advantage of the available data. This third and last research axis is thus a meeting point for all members of the team, as it requires the integration of AI techniques for traditional decision support systems with results from data mining techniques.

Two main research sub-axes are investigated in LACODAM:

• Diagnosis-based approaches. We are exploring how to integrate knowledge found from pattern mining approaches, possibly with the help of interactive methods, into the qualitative models. The goal of such work is to automate as much as possible the construction of prediction models, which can require a lot of human effort.

• Actionable patterns and rules. In many settings of “exploratory data mining”, the actual interestingness of a pattern is hard to assess, as it may be subjective. However, for some applications there are well defined measures of interestingness and applicability for patterns. Patterns and rules that can lead to actual actions –that are relevant to the user– are called "actionable patterns" and are of vital importance to industrial settings.