A framework for reducing the number of textual items reviewed to determine the source of or reason for an anomaly in a time series that is used to track metrics in textual data is provided. According the framework, textual items in a time window corresponding to the anomaly are ranked according to the cross-entropy as determined by applying a language model to the relevant textual items and ranking textual items that most likely triggered an anomaly in time series data based on the cross-entropy value. In an aspect, a predetermined number of textual items having the highest cross-entropy are provided or all textual items having cross-entropy value higher than predetermine threshold are provided.

Automated event monitoring is performed utilizing a Knowledge Graph (KG) constructed by grouping and consolidation of a variety of log Entry Types. A log entry is received by a knowledge graph parser (Kg parser). That parser finds contiguous sub-strings in a log entry that have a parameterized basic-format. The parser figures out which basic-formats are present, where, and with which parameters. Given a sub-string, its basic-format and its parameters, the parser can correctly parse the sub-string to components (e.g., keys and values if a key-value format; fields if a structured format). A result of the parsing is an entity type tree structure. Next, a grouping and consolidation capability functions to modify the KG to incorporate an incoming new entry type structure. The KG may be consumed by a user (e.g., visualization; querying), and may provide an artifact to an event monitoring system to automatically trigger certain actions (e.g., alerts).

The present invention extends to methods, systems, and computer program products for deriving unified insights ad logs from DevOps CI/CD tools and pipeline data. In general, a data transformer facilitates data normalization and serialization converting raw data across multiple DevOps tools and stores the data into a Data Lake in accordance with a customized schema. A continuous orchestrator sequences, aggregates and contextualizes the logs, providing an intuitive way of troubleshooting issues across a DevOps environment, historical data for compliance and audit purposes, and a build manifest for root cause analysis. The continuous orchestrator also processes the logs and leverages a KPI framework, providing intelligent dashboards across 90+ KPI's and a plurality of different dimensions (Planning, Development/pipelines, security, quality, operations, productivity and source code) to help customers make smart decisions and do more with less.

The present disclosure relates to generating a filtered data set. Data from a plurality of systems of record of a plurality of data source providers may be accessed. A master data set generated using the data accessed from the plurality of systems of record may be maintained. Restriction policies including one or more rules for restricting sharing of data may be maintained. A filtered data set may be generated for a data source provider responsive to an application of restriction policies of other data source providers to the master data set. The filtered data set may be provisioned.

Systems and methods for generating a data map based on a dynamically updated data set. The system includes a client-side device. The client-side device includes a controller and is operably connected to a communication network. The controller includes a processor and a non-transitory computer readable data storage medium, the processor is configured to retrieve from the medium and execute computer readable instructions to receive a data set including one or more data assets and generate the data map based on the received data set. Each data asset includes four or more attributes. The data map includes one or more segments, and the one or more segments illustrate each of the four or more attributes of the one or more data assets.

Systems and methods for generating a data map based on a dynamically updated data set. The system includes a client-side device. The client-side device includes a controller and is operably connected to a communication network. The controller includes a processor and a non-transitory computer readable data storage medium, the processor is configured to retrieve from the medium and execute computer readable instructions to receive a data set including one or more data assets and generate the data map based on the received data set. Each data asset includes four or more attributes. The data map includes one or more segments, and the one or more segments illustrate each of the four or more attributes of the one or more data assets.

Processor(s) of a sampling profiler can identify an activity of multiple activities serviced by a central processing unit (CPU). Each activity can be performed by computing thread(s) of multiple computing threads executing various subroutines of a computer program. The processor(s) can set a target representing a total number of computing threads required to work simultaneously for a maximal use of the CPU. The processor(s) can determine a number of busy computing threads that are performing the activity by using the CPU. The processor(s) can calculate a number of wasted computing threads that are not performing the activity and not using the CPU by computing a difference between the target and the number of busy threads. The processor(s) can compute a CPU time usage for the activity by multiplying time duration of the activity by a value obtained by dividing the number of wasted threads by the number of busy threads.

Processor(s) of a sampling profiler can identify an activity of multiple activities serviced by a central processing unit (CPU). Each activity can be performed by computing thread(s) of multiple computing threads executing various subroutines of a computer program. The processor(s) can set a target representing a total number of computing threads required to work simultaneously for a maximal use of the CPU. The processor(s) can determine a number of busy computing threads that are performing the activity by using the CPU. The processor(s) can calculate a number of wasted computing threads that are not performing the activity and not using the CPU by computing a difference between the target and the number of busy threads. The processor(s) can compute a CPU time usage for the activity by multiplying time duration of the activity by a value obtained by dividing the number of wasted threads by the number of busy threads.

A vibration measurement sensor (3) adapted to measure the vibrations formed on a test object (O) with moving mechanical systems, at least one noise measurement sensor (4) adapted to measure sound intensity and/or particle velocity and/or sound pressure in at least one direction, i.e. on one axis, and a vibration and noise mapping system (1) that is adapted to control the vibration measurement sensor (3) and the noise measurement sensor (4), to provide the vibration and acoustic performance data of the test object (O) according to the data obtained from these units (3, 4) and to identify the areas on the test object (O) that are problematic or need to be studied further in order to improve vibration and acoustic performances thereof, and to control the operation of test objects (O) such as moving mechanical systems under different conditions.

A vibration measurement sensor (3) adapted to measure the vibrations formed on a test object (O) with moving mechanical systems, at least one noise measurement sensor (4) adapted to measure sound intensity and/or particle velocity and/or sound pressure in at least one direction, i.e. on one axis, and a vibration and noise mapping system (1) that is adapted to control the vibration measurement sensor (3) and the noise measurement sensor (4), to provide the vibration and acoustic performance data of the test object (O) according to the data obtained from these units (3, 4) and to identify the areas on the test object (O) that are problematic or need to be studied further in order to improve vibration and acoustic performances thereof, and to control the operation of test objects (O) such as moving mechanical systems under different conditions.