• DCM

    • DCM is a temporal sequences analysis tool. It extracts discriminant chronicles from a large set of labeled sequences. A sequence is made of timestamped events. Each sequence of events is associated to a label (e.g. positive and negative sequences). A chronicle is a temporal model that characterizes a behavior by a set of events linked by temporal constraints. The DCM algorithm extracts chronicles that occurs more in positive sequences than in negative sequences.

  • OCL

    • This pattern mining software builds a user model preference from implicit feedback of the user in order to automatically choice the type of patterns and algorithms used. The principle builds upon the algorithm introduced by M. Boley et al, "One click mining: interactive local pattern discovery through implicit preference and performance learning”. In addition OCL integrates algorithms dealing with temporal series.

  • EcoMata

    • The EcoMata toolbox provides means for qualitative modeling and exploration of ecosystems in order to aid the design of environmental guidelines. We have proposed a new qualitative approach for ecosystem modeling based on the timed automata (TA) formalism combined to a high-level query language for exploring scenarios.


    • From two cohorts of genotyped individuals (case and control), the SSDPS software performs a Genome Wide Association Study based on data mining techniques and generates several patterns of SNPs that correlate with a given phenotype. The algorithm implemented in SSDPS directly uses relative risk measures such as risk ratio, odds ratio and absolute risk reduction combined with confidence intervals as anti-monotonic properties to efficiently prune the search space. The algorithm discovers a complete set of discriminating patterns with regard to given thresholds or applies heuristic strategies to extract the largest statistically significant discriminating patterns in a given dataset.


    • In the 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.

  • Promise

    • Promise is a software that predicts rare events in industrial production systems from data analysis of energy consumption data. The data is represented as a time series. The program takes as input the temporal series of energy consumption, an abnormal pattern (rare event) and a temporal dilatation, and outputs a set of sub-series similar (according to a similarity metric) to the abnormal pattern.

  • AMIE

    • AMIE takes as input a file that contains a knowledge base. This file must have one of the following formats:

      subject DELIM predicate DELIM object [whitespace/tabulation .] NEWLINE
      factid DELIM subject DELIM predicate DELIM object [whitespace/tabulation .] NEWLINE

      The default delimiter DELIM is the tabulation (.tsv files) but can be changed using the -d option. Any trailing whitespaces followed by a point are ignored. This allows parsing most NT files using the option: -d" ".

      However make sure the factid, subject, predicate nor the object contains the delimiter used (particularly in literal facts files). Otherwise the parsing may fail or facts may be wrongfully recognized as the second format.

      In the near future, AMIE will be able to parse the W3C Turtle format as well.


    • The NTGSP algorithm is a sequential pattern mining algorithm. It analyses a large database of temporal sequences, i.e., events with timestamps, by extracting its regularities (the patterns). A pattern describes the behavior as a sequence of events that frequently occurred in sequences. What makes NTGSP novel is its ability to handle patterns with negations, i.e., the description of a behavior that specifies the absence of an event. More precisely, it extracts frequent sequences with positive and negative events, as well as temporal information about the delay between these events.

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