Abstract: Methods allow a predicting and detecting potential anomalies at a service infrastructure. A strings table having entries that define character strings and corresponding anomaly probabilities is accessed. A log entry related to an event occurring in the service infrastructure is generated in a database. The log entry includes a character string designating a name of a file or an IP address and a domain name hosted by the service infrastructure. A search is made for the character string in the strings table. The domain name is marked as suspect if the character string is found in the strings table and if an anomaly probability for the character string exceeds a predetermined threshold. The anomaly probabilities may be calculated using a Bayesian filter that accounts for a number of domains hosted by the service infrastructure on which the character string has recently appeared.

Abstract: A method for sharing a source dataset between a first user device and a second user device, the method being executed by a first processor communicably connected to the first user device. The method includes accessing a configuration file having instruction sets enabling retrieval of source datasets stored respectively in corresponding data storage services; receiving an indication from the first user device including a selected source dataset from the source datasets, and a corresponding selected data storage service from the data storage services; extracting an instruction set corresponding to the selected source dataset; executing the extracted instruction set, and retrieving the selected source dataset onto the first user device; and transferring the configuration file to the second user device to enable the second user device to, when executing the extracted instruction set, retrieve the selected source dataset from the selected data storage service.

Abstract: A method and system for detecting anomalies in a data pipeline are disclosed. Data tables are extracted from data pipelines. A best time column is calculated for each data table. Table lines are aggregated so as to establish counts of data lines per a time interval, and corresponding sequences of points are produced that are being analysed through regression methods. An anomaly is raised when points have counts of data lines that lie outside confidence intervals around expected values for such counts.

Abstract: A method and system for detecting anomalies in a data pipeline are disclosed. Data tables are extracted from data pipelines. A best time column is calculated for each data table. Table lines are aggregated so as to establish counts of data lines per a time interval, and corresponding sequences of points are produced that are being analysed through regression methods. An anomaly is raised when points have counts of data lines that lie outside confidence intervals around expected values for such counts.

Abstract: A system and a method for operating a data center. The operating comprising executing predictive maintenance of the data center or network monitoring of the data center. The operating being based on a generated machine learning (ML) pipeline, the method comprising accessing data relating to operations of the data center, the data being suitable for evaluating respective performances of the plurality of ML pipelines. The method comprises generating the plurality of ML pipelines, selecting a sub-set of ML pipelines from the plurality of ML pipelines, evolving the sub-set of ML pipelines to generate evolved ML pipelines, selecting a sub-set of evolved ML pipelines from the evolved ML pipelines and iterating until determination is made that iterating is to be stopped. The method also involves operating, by an operation monitoring system of the data center, at least one of the ML pipelines from the sub-set of evolved ML pipelines.

Abstract: Methods allow a predicting and detecting potential anomalies at a service infrastructure. A strings table having entries that define character strings and corresponding anomaly probabilities is accessed. A log entry related to an event occurring in the service infrastructure is generated in a database. The log entry includes a character string designating a name of a file or an IP address and a domain name hosted by the service infrastructure. A search is made for the character string in the strings table. The domain name is marked as suspect if the character string is found in the strings table and if an anomaly probability for the character string exceeds a predetermined threshold. The anomaly probabilities may be calculated using a Bayesian filter that accounts for a number of domains hosted by the service infrastructure on which the character string has recently appeared.