Splunk Inc.

Novell, Inc.

Ramprasad Siva Golla

Ramkumar Chandrasekharan

Shailendra Suryawanshi

Allen Clay Tietjen

Paul Fredrick MacKay

Tristan Antonio Fletcher

William Deaderick

Steven Patterson Burns

Xiang Zhou

Trent A Badger

Lila Fridley

Stephen C McBride

Alpesh Sheth

Vinod Kumar Dashora

Phillip Earl Karren

Bryan Keith Walton

Systems and methods are described for using a streaming data processor to group notable events reflecting operation of a computing system into episodes of related events reflecting an incident on the computing system, such as to enable root cause analysis of the incident. Each notable event can be generated based on one or more events detected within raw machine data. The streaming data processor can ingest a data stream of notable events, and apply a clustering algorithm to the events to cluster those events into episodes. When the episodes satisfy an action rule, the streaming data processor can take an action appropriate to that rule, such as transmitting an alert or programmatically altering operation of the computing system. The streaming data processor can utilize feedback as to the grouping of events into episodes to modify the clustering algorithm and improve accuracy of clustering.

Grouping evens into episodes using a streaming data processor

Systems and methods are described for training a machine learning (ML) model to group notable events reflecting operation of a computing system into episodes of related events reflecting an incident on the computing system, such as to enable root cause analysis of the incident. The ML model is trained using pairwise binary similarity labels (PBSLs) indicating that two events must or must not be grouped together. An interface is provided that facilitates rapid generating of PBSLs by relocating one or more events from a first episode to a second episode. The relocation input is translated into PBSLs that are then used to train the ML model.

Interface for incorporating user feedback into training of clustering model

A method and apparatus for creating a non-native display is disclosed. A display application is loaded having a plurality of native window classes, a window frame, and a display bar located within the window frame. One of the native window classes is associated with a non-native window class by passing a value from a non-native file to the display application. A composite child window is created having a non-native display configuration. The composite child window is located within the window frame of the display application. The process of creating the composite child window involves the steps of creating an instance of the associated native window class, and creating an instance of the non-native window class. The display bar owned by the display application is controlled using the composite child window.

Processes and apparatuses for creating non-native displays on a computer

A method and apparatus for generically viewing and editing values and attributes of distributed directory objects is disclosed. A data structure is adapted to represent one or more attributes and associated values of a target object in a distributed directory. A user interface receives inputs from a human user and displays at least a portion of the data structure, irrespective of the target object type. One or more syntax editors receives inputs from a human user and modify the data structure. Each of the one or more syntax editors correspond to at least one attribute syntax in the distributed directory. A mechanism modifies the target object in the distributed directory to include modifications made to the data structure with the attribute editors.

Method and apparatus for generically viewing and editing objects

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San Jose, CA, United States of America

Ramprasad Siva Golla