Abstract: The invention relates to a method for automatically analyzing a transaction log of a distributed computing system comprising a plurality of lines. The method includes, for each line, cutting the line into words, constructing a comparison vector by comparing the line with the other lines of the same size as the line, constructing a pattern from the comparison vector, and creating an event per pattern. The invention includes constructing a prediction model by training an artificial neural network on a group of training events, the prediction model being configured to predict the next event in the transaction log. The invention includes, for at least one event, using the prediction model to predict the event, from a group of prediction events, and generating from the prediction model, a causal graph of the event comprising a causal relation for each event of the group of prediction events responding to a relevance condition.

Abstract: Disclosed is a method for training a neural network, for the recognition of a sequence of characters in an image and without a predefined format, including: a step of creating an artificial database of a plurality of sequences of characters in images, some sequences being generated randomly, other sequences being derived from transformations of sequences generated randomly; a learning step that teaches the neural network to recognize a sequence of characters in an image, directly on at least some of the sequences of the artificial database, without a preliminary subdividing of these sequences into their component characters.

Abstract: A device (DS) monitors a process using at least one electronic device (EE1-EE4) in operation and generating first data of a metric. This device (DS) comprises: learning means (MA) configured to analyse automatically second data which are representative of events that have occurred in the course of the process, in order to determine anomalies of a chosen type, and then automatically determine an indicator representative of this metric, then a correlation between these determined anomalies and this indicator, and then at least one rule defining this correlation, and monitoring means (MS1) configured to analyse newly generated first data periodically, and group by group, by checking whether at least one value of the indicator determined on the basis of the aforesaid data satisfies this determined rule, in order to predict the occurrence of the anomaly in a future group of first data when this at least one value satisfies this rule.

Abstract: An aspect of the invention relates to a method for automatically analysing a transaction log of a distributed computing system, comprising a plurality of lines, the method comprising the following steps: For each line: Cutting the line into words; Constructing a comparison vector by comparing the line with the other lines of the same size as the line; Constructing a pattern from the comparison vector; Creating an event per pattern; Constructing at least one prediction model by training an artificial neural network on a group of training events, the prediction model being configured to predict the next event in the transaction log; For at least one event: Using the prediction model to predict the event, from a group of prediction events; Generating from the prediction model, a causal graph of the event comprising a causal relation for each event of the group of prediction events responding to a relevance condition.

Abstract: Disclosed is a method for training a neural network, for the recognition of a sequence of characters in an image and without a predefined format, including: a step of creating an artificial database of a plurality of sequences of characters in images, some sequences being generated randomly, other sequences being derived from transformations of sequences generated randomly; a learning step that teaches the neural network to recognize a sequence of characters in an image, directly on at least some of the sequences of the artificial database, without a preliminary subdividing of these sequences into their component characters.

Abstract: A device (DS) monitors a process using at least one electronic device (EE1-EE4) in operation and generating first data of a metric. This device (DS) comprises: learning means (MA) configured to analyse automatically second data which are representative of events that have occurred in the course of the process, in order to determine anomalies of a chosen type, and then automatically determine an indicator representative of this metric, then a correlation between these determined anomalies and this indicator, and then at least one rule defining this correlation, and monitoring means (MS1) configured to analyse newly generated first data periodically, and group by group, by checking whether at least one value of the indicator determined on the basis of the aforesaid data satisfies this determined rule, in order to predict the occurrence of the anomaly in a future group of first data when this at least one value satisfies this rule.