EcoMata is a tool-box for modeling and exploring qualitatively trophic-food web. To date, it is dedicated to ecosystems that can be modeled as a collection of species (prey-predator systems) under various human pressures and to environmental disturbances. EcoMata is a free Java software.
LogAnalyzer is software for visualization, exploration and analysis of access logs to HTTP servers. This software was developed under the project SESUR.
LogAnalyzer can detect intrusions adaptively. This software also allows the visualization and exploration of logs (filtering, analysis of the structure of the server or transactions). The software also contains tools to collect statistics on the logs and make the insertion of artificial intrusions into logs (useful for the evaluation of intrusion detection methods).
QTempIntMiner considers the problem of discovering frequent temporal patterns in a database of temporal sequences, where a temporal sequence is a set of items with associated dates and durations. Since the quantitative temporal information appears to be fundamental in many contexts, it is taken into account in the mining processes and returned as part of the extracted knowledge. To this end, we have adapted the classical GSP framework to propose an efficient algorithm based on a hyper-cube representation of temporal sequences. The extraction of quantitative temporal information is performed using a density estimation of the distribution of event intervals from the temporal sequences. An evaluation on synthetic data sets shows that the proposed algorithm can robustly extract frequent temporal patterns with quantitative temporal extents.
QTIPrefixSpan and QTIAPriori extends respectivelly algorithms PrefixSpan and GSP with a multi-dimensional interval clustering step for extracting the representative temporal intervals associated to events in patterns.
SACADEAU is a tool enabling:
- the simulation of scenarios describing, at a watershed, crop management and typical weather situations. The results of simulations are chronicle of temporal variables reflecting the quality of water. The simulator is easy and simple to use, particularly by the watershed manager when he wants to introduce new information.
- the automatic extraction, by learning from simulation results, of relationships between the explanatory variables describing the types of climate, agricultural practices … and those describing the water quality.
CarDeCRS: a chronicle-based distributed diagnosis platform
CarDeCRS (Chronicles Applied to error Recognition in Distributed Environments, through CRS) is a generic diagnosis platform which is currently being developed within the DREAM project.
This platform aims at monitoring complex systems, thanks to specifically designed distributed chronicles. Its goal is to establish an end-to-end system, acting from the acquisition of the chronicles to the final diagnosis, with no need for extra software.
The implementation mainly relies on:
- a chronicle recognition system called CRS, developed by C. Dousson and his team, from France Telecom Recherche & Developpement Lannion (for the diagnosis part);
- an RMI/multithreaded based environment (for the low level part).
The platform has been completed in 2009. It has been experimented in the framework of the WS-Diamond European project for monitoring web services.
Contact: Laurence Rozé
The platform Calicot (Cardiac Arrhythmias Learning for Intelligent Classification of On-line Tracks) integrates the different software components used for learning and recognition of cardiac arrhythmias. The signal processing modules implemented in Matlab provide detection and classification of waves from an ECG to produce a stream of symbolic time-stamped events. This flow can be placed at the input of the chronicle recognizer CRS in order to perform the detection of cardiac arrhythmias. CRS, made at our disposal by France Telecom R & D (http://crs.elibel.tm.fr/), comes in the form of a library of Java programs from which it is possible to write chronicle recognizers adapted to various application domains. The stream of symbolic events can also be used to build an example database from chronicles will be learned. Learning is achieved by means of ICL (http://www.cs.kuleuven.ac.be/~wimv/ICL/), a machine learning system using inductive logic programming.
ManageYourself is 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 ManagerYourself 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.