Abstract: The invention relates to a data processing method for preparing a dataset that includes a processor that receives a first plurality of data input streams to prepare an output dataset. The plurality of data input streams and the output dataset are different. The method includes standardizing the plurality of data input streams, encoding the normalized data, preprocessing missing data, and transmitting a preprocessed dataset. The invention further relates to a data processing system, and a recording medium on which the data processing program is recorded.

Abstract: The invention relates to a device and a method (100) for determining a technical incident risk value in an infrastructure (5), said method comprising: a step of receiving (120) performance indicator values, a step of identifying (140) anomalous performance indicators, so as to identify abnormal values, and identifying performance indicators associated with these abnormal values, a step of determining (150) at-risk indicators, comprising an identification of performance indicators of the computing infrastructure that are correlated with the identified anomalous indicators, a step of creating (160) an augmented anomalies vector, comprising the identifiers of the identified anomalous indicators and the identifiers of the determined at-risk indicators, a determination step (170), comprising the comparison of the augmented anomalies vector with predetermined technical incident reference data.

Abstract: The invention relates to a method and device for determining an anomaly prediction performance index value, said method comprising: a step of receiving (120) performance indicator values, a step of identifying (140) anomalous performance indicators, a step of identifying (150) first at-risk indicators, and a step of determining (170) an anomaly prediction performance index value comprising receiving (171) new performance indicator values; identifying (172) new confirmed anomaly indicators; and comparing (174) identified at-risk indicators to the new confirmed anomalous indicators in order to generate a performance index value.

Abstract: A method for determining an estimated duration before a technical incident in a computing infrastructure, executed by a computing device. The computing device including a data processing module, a storage module that stores in memory at least one correlation base between performance indicators, wherein the correlation base includes values of duration before becoming anomalous between correlated performance indicators, and a collection module. The method includes receiving performance indicator values, identifying anomalous performance indicators, identifying first and other at-risk indicators. The method includes determining an estimated duration before a technical incident including a calculation, from the anomalous indicators and at-risk indicators, a shorter path leading to a risk of technical incident, and a calculation of an estimated duration before a technical incident.

Abstract: The invention relates to a data processing method (100) for preparing a dataset comprising a data processing unit receiving a first plurality (2, 3, 4) of data input streams to prepare the output dataset (5), the plurality of data input streams and the output dataset being different, said method comprising: A step of standardizing (120) the plurality of data input streams, A step of encoding (130) the normalized data, A step of preprocessing (140) the missing data, A step of transmitting (190) a preprocessed dataset. The invention further relates to a data processing system, and a recording medium on which the data processing program is recorded.

Abstract: The invention relates to a method (1000) for designing a prediction model implemented by a computer system (1), said designing method (1000) comprising: a step of transmitting (250,350,450) data to the analyst client and the business client, a step of receiving (260, 360, 460) an instruction from each of the analyst client (20) and the business client (30) and in that a following step is initiated by the designer device (10) only if both instructions authorize said designer device (10) to do so.

Abstract: The invention relates to a method for selecting a learning model defined in particular by parameters and hyperparameters from among a plurality of learning models, implemented by a computing device, said computing device comprising a model selection module and a model repository including a plurality of series of instructions each corresponding to a learning model and each including hyperparameter values, said method comprising a step of selecting a model when the prediction performance value and the classification value are greater than predetermined second threshold and the hyperparameter value is greater than a predefined threshold value.

Abstract: The invention relates to a method for identifying variables relevant to a dataset, said variables being derived from a plurality of variables involved in processing the dataset, said method comprising: a step of generating a subset of variables from the plurality of variables, a step of assigning a quantization value to each variable of the generated subset of variables, a step of selecting a relevant variable, a further step of generating a new subset of variables when the quantitative value of the selected variable is below a predetermined threshold value.

Abstract: The invention relates to a method and a device for determining an estimated duration before a technical incident, said method comprising: a step (120) of receiving performance indicator values, a step (140) of identifying anomalous performance indicators, a step (150) of identifying first at-risk indicators, a step (160) of identifying other at-risk indicators, and a step (170) of determining an estimated duration before a technical incident comprising a calculation, from the anomalous indicators and at-risk indicators identified, of a shorter path leading to a risk of technical incident, and a calculation of an estimated duration before a technical incident, said estimated duration before a technical incident being calculated from the values of duration before becoming anomalous between correlated performance indicators for each of the performance indicators constituting the shortest path calculated.

Abstract: The invention relates to a device and a method (100) for determining a technical incident risk value in an infrastructure (5), said method comprising: a step of receiving (120) performance indicator values, a step of identifying (140) anomalous performance indicators, so as to identify abnormal values, and identifying performance indicators associated with these abnormal values, a step of determining (150) at-risk indicators, comprising an identification of performance indicators of the computing infrastructure that are correlated with the identified anomalous indicators, a step of creating (160) an augmented anomalies vector, comprising the identifiers of the identified anomalous indicators and the identifiers of the determined at-risk indicators, a determination step (170), comprising the comparison of the augmented anomalies vector with predetermined technical incident reference data.

Abstract: The invention relates to a method and device for determining an anomaly prediction performance index value, said method comprising: a step of receiving (120) performance indicator values, a step of identifying (140) anomalous performance indicators, a step of identifying (150) first at-risk indicators, and a step of determining (170) an anomaly prediction performance index value comprising receiving (171) new performance indicator values; identifying (172) new confirmed anomaly indicators; and comparing (174) identified at-risk indicators to the new confirmed anomalous indicators in order to generate a performance index value.