Section: New Software and Platforms

Platforms

The Dream project-team, in collaboration with their applicative partners, has proposed and maintains several important software platforms for its main research topics.

Platform: Environmental decision-support systems

Participants : Marie-Odile Cordier, Christine Largouët, Véronique Masson, Yulong Zhao.

SACADEAU: the Sacadeau system is an environmental decision software (cf. 4.2 ) that implements the Sacadeau transfer model. The Sacadeau simulation model couples two qualitative models, a transfer model describing the pesticide transfer through the catchment and a management model describing the farmer decisions. Giving as inputs a climate file, a topological description of a catchment, and a cadastral repartition of the plots, the Sacadeau model simulates the application of herbicides by the farmers on the maize plots, and the transfer of these pollutants through the catchment until the river. The two main simulated processes are the runoff and the leaching. The output of the model simulation is the quantity of herbicides arriving daily to the stream and its concentration at the outlets. The originality of the model is the representation of water and pesticide runoffs with tree structures where leaves and roots are respectively up-streams and down-streams of the catchment.

The software allows the user to see the relationships between these tree structures and the rules learnt from simulations. A more elaborated version allows to launch simulations, to learn rules on-line and to access to two recommendation action algorithms. This year, we have developed a new visualization tool designed to compare two sets of rules learnt from simulations. The user can choose one (or more) rule(s) to compare from one set of rules, and a distance to apply from several multidimensional distances. The most similar rules in the second set of rules are found and the comparison can be easily visualized. The examples covered by "similar" rules can also be presented to the user by highlighting shared positive and negative covered examples. The software is mainly in Java.

The following website is devoted to the presentation of the SACADEAU : http://www.irisa.fr/dream/SACADEAU/ . See also [57] for a presentation.

ECOMATA: The EcoMata tool-box provides means for qualitative modeling and exploring ecosystems and for aiding to design environmental guidelines.We have proposed a new qualitative approach for ecosystem modeling (cf. 4.2 ) based on timed automata (TA) formalism combined to a high-level query language for exploring scenarios.

To date, EcoMata is dedicated to ecosystems that can be modeled as a collection of species (prey-predator systems) under various human pressures and submitted to environmental disturbances. It has two main parts: the Network Editor and the Query Launcher. The Network Editor let a stakeholder describe the trophic food web in a graphical way (the species icons and interactions between them). Only few ecological parameters are required and the user can save species in a library. The number of qualitative biomass levels is set as desired. An efficient algorithm generates automatically the network of timed automata. EcoMata provides also a dedicated window to help the user define different fishing pressures, a nice way being by using chronograms. In the Query Launcher, the user selects the kind of query and the needed parameters (for example the species biomass levels to define a situation). Results are provided in a control panel or in files that can be exploited later. Several additional features are proposed in EcoMata: building a species library, import/export of ecosystem model, batch processing for long queries, etc. EcoMata is developed in Java (Swing for the GUI) and the model-checker called for the timed properties verification is UPPAAL .

The following website is devoted to the presentation of ECOMATA : http://oban.agrocampus-ouest.fr:8080/ecomata .

PATURMATA: The Paturmata tool-box provides means for qualitative modeling and exploring agrosystems, specifically management of herd based on pasture [5] . The system is modelled using a hierarchical hybrid model described in timed automata formalism.

In PaturMata software, users can create a pasture system description by entering herds and plots information. For each herd, the only parameter is the number of animals. For each plot, users should enter the surface, the density, the herb height, the distance to the milking shed, a herb growth profile and an accessibility degree.

Users then specify pasturing and fertilization strategies. Finally, users can launch a pasture execution. PaturMata displays the results and a detailed trace of pasture. Users can launch a batch of different strategies and compare the results in order to find the best pasture strategy.

PaturMata is developed in Java (Swing for the GUI) and the model-checker that is called for the timed properties verification is UPPAAL .

Another feature which will be soon added to PaturMata is strategy synthesis. Users choose a pasture configuration or a type of pasture configuration and PaturMata proposes the best pasture and fertilization strategy in order to minimize the pasture procedure cost and use of nitrogen fertilizer.

Platform: Pattern Mining

Participants : Thomas Guyet, René Quiniou.

QTempIntMiner: the QTempIntMiner (Quantitative Temporal Interval Miner) data mining software implements several algorithms presented in [46] and [3] (QTIAPriori and QTIPrefixSpan ). The software is mainly implemented in Matlab. It uses the Mixmod toolbox [33] to compute multi-dimensional Gaussian distributions. The main features of QTempIntMiner are:

• a tool for generating synthetic noisy sequences of temporal events,

• an implementation of the QTempIntMiner , QTIAPriori and QTIPrefixSpan algorithms,

• a graphical interface that enables the user to generate or import data set and to define the parameters of the algorithm and that displays the extracted temporal patterns.

• a sequence transformer to process long sequences of temporal events. Long sequences are transformed into a database of short temporal sequences that are used as input instances for the available algorithms.

The software includes one new algorithm based on the separation of the set of interval to extract more efficiently but less accurately the time interval in temporal patterns. This new algorithm version is still under evaluation on simulated and real datasets (care pathways).

The following website gives many details about the algorithms and provides the latest stable implementation of QTempIntMiner : http://www.irisa.fr/dream/QTempIntMiner/ .

Platform: Diagnostic and Monitoring Systems

Participants : Marie-Odile Cordier, René Quiniou, Sophie Robin.

Odisseptale: the Odisseptale software implements disease detectors using monitoring of data provided by sensors placed on calves or cows. Sensors record streams of data such as body temperature, physical activity, feeding behavior, etc. These data are transmitted regularly to a monitoring software that aims to detect if a noticeable change has occurred on the data streams. Several detectors can be simultaneously active and each contribute to the final decision (detection of a disease). Two kinds of detectors have been implemented: a generic detector based on adaptive CUSUM and a symbolic pattern-based detector. Odisseptale provides also facilities for parameter setting and performance evaluation.

ManageYourself: the ManageYourself software comes from a collaborative project between Dream and the Telelogos company aiming at monitoring smartphones from a stream of observations made on the smartphone state.

Today’s smartphones are able to perform calls, as well as to realize much more complex activities. They are small computers. But as in computers, the set of applications embedded on the smartphone can lead to problems. The aim of the project ManageYourself is to monitor smartphones in order to avoid problems or to detect problems and to repair them.

The ManageYourself application includes three parts :

• A monitoring part which triggers preventive rules at regular time to insure that the system is working correctly, e.g. if the memory is full then delete the tmp directory. This part is always running on the smartphone.

• A reporting part which records regularly the state of the smartphone (the memory state - free vs allocated -, the connection state, which applications are running, etc.). This part also is always running on the smartphone. The current state is stored in a report at regular period and is labeled normal. When an application or the system bugs, the current buggy state is stored in a report and is labeled abnormal. At regular timestamps, all the reports are sent to a server where the learning process is executed.

• A learning part which learns new bug rules from the report dataset. This part is executed offline on the server. Once the bug rules are learnt, human experts translates them into preventive rules which are downloaded and integrated in the monitoring part of the smartphones.