Section: New Results

Decision aiding with models and simulation data

Participants : Louis Bonneau de Beaufort, 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 7.2.1 ) a paper describing a generic framework for scenario exercises using models applied to water-resource management, has been written in cooperation with all the partners and published in Environmental Modelling and Software [5] .

A datawarehouse for simulation data

The Acassya project 7.2.2 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.

This year we have been working on the efficient computation of OLAP queries related to realistic scenarios proposed by experts in the domain. Precisely, we have devised indexing schemes to access the data in the OLAP cube. We have also worked on the visualization by a GIS (Geographical Information System) of the query results on maps of the geographical area under interest. A paper will be submitted to the COMPAG Journal in beginning 2013.

Efficient computation of skyline queries in an interactive context

Skyline queries retrieve from a database the objects that maximizes some criteria, related to user preferences for example, or objects that are the best compromises satisfying these criteria. When data are in huge volumes, 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.

This year we have worked at improving the formal setting of the partial materialization of skyline queries when dynamic preferences are refined online by the user. We have explicited which parts of the skyline evolve (which point are added or removed) when a new dimension is introduced in the computation. This led to an efficient incremental method for the online computation of the skyline corresponding to new user preferences [9] . An extended version of this paper is under submission to the Journal "Transactions on Large Scale Data and Knowledge Centered Systems" (TLDKS).

We are working now on a hierachical extension of our method that could be introduced in a datawarehouse context.

Influence Diagrams for Multi-Criteria Decision

For multi-criteria decision-making problems, we propose in [7] 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.

Modeling influence propagation by Bayesian causal maps

The goal of this project is modeling shellfish fishing to assess the impact of management pollution scenarios on the Rade de Brest. Cognitive maps were built from interviews with fishermen. To represent and reason about these cognitive maps, we propose to use Bayesian Causal Maps making use of fishermen knowledge, particularly to perform influence propagation [11] .

However, this model does not take into account the variety of influences asserted by the fishermen, but only the "mean" causal map. This year we have been working on an approach that could combine individual knowledge with belief functions in the way of Philippe Smets's Transferable Belief Model [67] .

This work is done in the framework of the RADE2BREST project, involving Agrocampus Ouest and CNRS (GEOMER/LETG), funded by "Ministère de l'Ecologie" (This project is not mentioned in section 7.2 because DREAM is not an official partner of this project.).

Mining simulation data by rule induction

In the framework of the Sacadeau project (see 7.2.1 ), a paper dedicated to mining simulation data by rule induction has been published in the COMPAG Journal [8] . Both qualitative and quantitative predictions from a model of an agro-environmental system are analysed. Two approaches in rule learning from spatial data (ILP and attribute-value approaches) are compared and show that results help identify factors with strong influence on herbicide stream-pollution.

We have also participated in a collaboration for modeling the effects crop rotations the results of which were published in the Science of the Total Environment Journal [6] .