Maana, Inc.

Splunk Inc.

Ralph Donald Thompson, Iii

Robert Povey

Kristal Curtis

Jason Forrest Mackay

William Deaderick

Chandrima Sarkar

Tanner Gilligan

Poonam Yadav

Sichen Zhong

Allen Geoffrey Jones

Wei Jie Gao

Tara Sanathanan Prakriya

Alina Mihaela Stoica-Beck

Ryan Kirk

Alexander Hussam Elkholy

Aleksander Stojanovic

Field Nicholas Cady

Alexander Stojanovic

Systems and methods are disclosed that are directed to improving the prioritization, display, and viewing of system alerts through the use of machine learning techniques to group the alerts and further to prioritize the groupings. Additionally, a graphical user interface is generated that illustrates the prioritized listing of the plurality of groupings. Thus, a system administrator or other user receives an improved experience as the number of notifications provided to the system administrator are reduced due to the grouping of individual alerts into related groupings and further due to the prioritization of the groupings. Previously, or in current technology, system alerts may be automatically generated and provided immediately to a system administrator. In some instances, any advantage of detecting system errors or system monitoring provided by the alerts is negated by the vast number of alerts and provision of minimally important alerts in a manner that concealed more important alerts.

Machine-learning based prioritization of alert groupings

A computerized method is disclosed for generating a prioritized listing of alerts based on scoring by a machine learning model and retraining the model based on user feedback. Operations of the method include receiving a plurality of alerts, generating a score for each of the plurality of alerts through evaluation of each of the plurality of alerts by a machine learning model, generating a prioritized listing of the plurality of alerts based on the generated scores, receiving user feedback on the prioritized listing, retraining the machine learning model based on the user feedback by generating a set of labeled alert pairs, wherein a labeled alert pair includes a first alert, a second alert, and an indication as to which of the first alert or the second alert is a higher priority in accordance with the user feedback, and evaluating subsequently received alerts with the retrained machine learning model.

System and method for machine-learning based alert prioritization

Embodiments are directed towards managing data. Attributes of model fields of a plurality of model objects may be analyzed. If the analysis of the attributes discovers primary key fields in the model objects, the characteristics of the primary keys may be compared with the characteristics of other model objects. If affirmative results of the comparison indicate that one or more foreign key fields may be in the other model objects, one or more relationships that associate the primary key fields with the foreign key fields may be provided. And, a system model may be provided based on the relationships and the model objects that include the primary key fields and the model objects that include the foreign key fields.

Machine-assisted key discovery and join generation

Embodiments are directed towards managing data. An attributes engine may be employed to perform various actions, including: analyzing characteristics of model object features of a plurality of model objects; classifying the model object features based on the characteristics, such that the characteristics include a data type and values of the model object features; and associating similarity tasks with the model object features based on their classification. A similarity engine may then be employed to perform further actions, including: providing a similarity model that includes the similarity tasks; employing the similarity model to provide candidate similarity scores based on exemplar model objects labeled as being similar; modifying the similarity model based on the exemplar model objects and the candidate similarity scores; employing the modified similarity model to provide similarity scores for model objects based on the one or more similarity tasks.

Machine-assisted exemplar based similarity discovery

Embodiments are directed towards dynamic semantic models having multiple indices. Source data may be provided to a network computer from at least one separate data source. A raw data graph may be generated from the source data such that the structure of the raw data graph may be based on the structure of the source data. Elements of the raw data graph may be mapped to a concept graph. Concept instances may be generated based on the concept graph, the raw data graph, and the source data. Model-identifiers (MIDs) that correspond to the concept instances may be generated to include at least a path in the concept graph The MID values may be indexed into a plurality of indices based on a content-type of the data associated with the MIDs. In response to a query, a result set may be generated that includes result MIDs.

Dynamic semantic models having multiple indices

Growing community of inventors

Sammamish, WA, United States of America

Ralph Donald Thompson, Iii