Section: New Results

Decision aiding with models and simulation data

Participants : Tassadit Bouadi, Marie-Odile Cordier, Véronique Masson, Florimond Ployette, René Quiniou, Karima Sedki.

Models can be very useful for decision aiding as they can be used to play different plausible scenarios for generating the data representing future states of the modeled process. However, the volume of simulation data may be very huge. Thus, efficient tools must be investigated in order to store the simulation data, to focus on relevant parts of the data and to extract interesting knowledge from these data.

Exploring models thanks to scenarios: a generic framework

In the framework of the Appeau project (see 8.2.1 ), that ended in December 2010, a paper, describing a generic framework for scenario exercises using models applied to water-resource management, has been written during 2011 in cooperation with all the partners and submitted to Environmental Modelling and Software. It is currently under revision.

A datawarehouse for simulation data

The Acassya project aims at providing experts or stakeholders or farmers with a tool to evaluate the impact of agricultural practices on water quality. As the simulations of the deep model TNT2 are time-consuming and generate huge data, we have proposed to store these simulation results in a datawarehouse and to extract relevant information, such as prediction rules, from the stored data. We have devised a general architecture for agro-environmental data on top of the framework Pentaho. An article presenting the principles of this architecture as well as a set of realistic scenarios and their transformation into OLAP queries has been submitted to Compag (Computers and Electronics in Agriculture).

Efficient computation of skyline queries in an interactive context

Skyline queries retrieve from a database the objects that optimizes multiple criteria, related to user preferences for example, or objects that are the best compromises satisfying these criteria. When data are huge such objects may shed light on interesting parts of the dataset. However, computing the skylines (i.e. retrieving the skyline points) may be time consuming because of many dominance tests. This is, especially the case in an interactive setting such as querying a data cube in the context of a datawarehouse. We have worked on how to answer efficiently to skyline queries by the materialization of precomputed skyline queries related to dynamic user preferences. An article has been submitted to the conference SIGMOD 2012.

Influence Diagrams for Multi-Criteria Decision-Making

For multi-criteria decision-making problems, we propose in [6] a model based on influence diagrams able to handle uncertainty, represent interdependencies among the different decision variables and facilitate communication between the decision-maker and the analyst. The model makes it possible to take into account the alternatives described by an attribute set, the decision-maker's characteristics and preferences, and other information (e.g., internal or external factors) that influence the decision. Modeling the decision problem in terms of influence diagrams requires a lot of work to gather expert knowledge. However, once the model is built, it can be easily and efficiently used for different instances of the decision problem. In fact, using our model simply requires entering some basic information, such as the values of internal or external factors and the decision-maker's characteristics.

Recommending actions from classification rules

In the framework of the Sacadeau project (see 8.2.1 ), a paper dedicated to building recommendation actions for a given situation, from the set of classification rules, learnt from simulation results, has been published in the KAIS journal [7] .