PagerDuty, Inc.

A tree data structure that includes an exponential root node and a plurality of regular child nodes arranged in hierarchical levels is generated. A number of the hierarchical levels corresponds to a specified precision value. A numerical data value is received. The numerical data value is truncated according to the specified precision value to generate a truncated value. Portions of the truncated value are stored in the tree data structure by storing an exponent of the truncated value in the exponential root node, and storing each significant digit of the truncated value in a respective regular child node based on significance level. Statistical information is computed for each node storing a portion of the truncated value. The statistical information is provided in response to a query.

A relationship network graph is constructed, where nodes represent entities and edges represent interactions between the entities, and wherein at least some of the entities represent respective persons, teams, or organizations of people. A dependency strength is determined from the relationship network graph between a first service associated with a first entity and a second service associated with a second entity. A first alert associated with the first service is received. In response to the first alert, and based on the dependency strength meeting a predefined threshold, a second alert associated with the second service is paused for a predefined pause period. It is determined whether the first alert remains unresolved after expiration of the pause period. If the first alert remains unresolved, the second alert is resumed.

Operations events received from different monitoring systems are transformed into a common event format having standardized fields including a source origin identifier, a source component identifier, a creation time, and event data. Frequency-time analysis is performed on the transformed Operations events by segmenting time-domain data into time bins and determining source origin counts for each time bin. Event clusters are identified based on time-frequency-space envelopes determined from the frequency-time analysis. Each event cluster is associated with resolution metrics including a time-to-resolve value and a number of responders required for resolution. A predictive model is trained using the event clusters and their associated resolution metrics. New incoming Operations events are grouped into a new event cluster. The trained predictive model is applied to the new event cluster to predict resolution requirements, and a remediation action is initiated based on the predicted resolution requirements.

Interaction data between entities responsible for services in an information technology environment are collected. The interaction data includes communications between the entities where at least some of the entities represent respective persons, teams, or organizations of people. A relationship network graph based is constructed on the interaction data. The nodes of the relationship network graph represent the entities and the edges of the relationship network graph represent interactions between the entities. Dependencies between the services are identified by traversing the relationship network graph.

Embodiments are directed to managing operations. If Operations events are provided, event clusters may be associated with one or more Operations events, such that the Operations events may be associated with the event clusters based on characteristics of the Operations events. Metrics including resolution metrics, root cause analysis, notes, and other remediation information may be associated with the event clusters. Then a modeling engine may be employed to train models based on the Operations events, the event clusters, and the resolution metrics, such that the trained model may be trained to correlate and predict the resolution metrics from real-time Operations events. If real-time Operations events may be provided, the trained models may be employed to predict the resolution metrics that are associated with the real-time Operations events. If model performance degrades beyond accuracy requirements, new observations may be added to the training set and the model re-trained.

A tree data structure that includes an exponential root node and a plurality of regular child nodes arranged in hierarchical levels is generated. A number of the hierarchical levels corresponds to a specified precision value. A numerical data value is received. The numerical data value is truncated according to the specified precision value to generate a truncated value. Portions of the truncated value are stored in the tree data structure by storing an exponent of the truncated value in the exponential root node, and storing each significant digit of the truncated value in a respective regular child node based on significance level. Statistical information is computed for each node storing a portion of the truncated value. The statistical information is provided in response to a query.

Interaction data between entities responsible for services in an information technology environment are collected. The interaction data includes communications between the entities where at least some of the entities represent respective persons, teams, or organizations of people. A relationship network graph based is constructed on the interaction data. The nodes of the relationship network graph represent the entities and the edges of the relationship network graph represent interactions between the entities. Dependencies between the services are identified by traversing the relationship network graph.

A machine learning model is applied to extract keywords from an event associated with network operations. A keyword vector is constructed where each keyword extracted is represented as a unique dimension in the keyword vector. The keyword vector is compared against event category vectors associated with event categories to identify potential matches. A relevance score is calculated for each category based on the comparing. The event is classified into one or more categories based on the relevance scores exceeding a predetermined threshold. A user interface configured to visually display and obtain feedback regarding the classifying is generated. At least one of the event categories is updated based on a user feedback received via the user interface and machine learning retraining.

Dependencies between services are determined based on the relationships between entities responsible for the services. The relationships are found based on past interactions between the entities. A relationship network graph having nodes representing entities and edges is built representing interactions between respective entities. The relationship network graph is analyzed to identify pairs of nodes having a relationship based on an evaluation between edge paths between the pairs of nodes compared to edge paths between other pairs of nodes. Associations are built between services owned by entities represented by the nodes of a pair of nodes identifying that nodes are dependent on one another.

Dependencies between services are determined based on the relationships between entities responsible for the services. The relationships are found based on past interactions between the entities. A relationship network graph having nodes representing entities and edges is built representing interactions between respective entities. The relationship network graph is analyzed to identify pairs of nodes having a relationship based on an evaluation between edge paths between the pairs of nodes compared to edge paths between other pairs of nodes. Associations are built between services owned by entities represented by the nodes of a pair of nodes identifying that nodes are dependent on one another.