Splunk Inc.

Nghi Huu Nguyen

Adam Jamison Oliner

Zidong Yang

Jacob Barton Leverich

Kristal Curtis

Manish Sainani

Eric Sammer

Alexander Nicholas Johnson

Implementations include receiving a user provided example value of personally identifiable information (PII). Occurrences of the received example value are automatically identified in a dataset of events, wherein each occurrence is identified in a portion of raw machine data of a respective event of the events. For each occurrence of the identified occurrences, an extraction rule is generated, which defines a pattern of the occurrence of the example value and is executable to identify PII values in portions of raw machine data of the events using the pattern. Values of the PII are identified in a set of events using a set of extraction rules comprising the extraction rule of a plurality of the occurrences.

Masking personally identifiable information from machine-generated data

As described herein, a portion of machine data of a message may be analyzed to infer, using an inference model, a sourcetype of the message. The portion of machine data may be generated by one or more components in an information technology environment. Based on the inference, a set of extraction rules associated with the sourcetype may be selected. Each extraction rule may define criteria for identifying a sub-portion of text from the portion of machine data of the message to produce a value. The set of extraction rules may be applied to the portion of machine data of the message to produce a result set that indicates a number of values identified using the set of extraction rules. Based on the result set, at least one action may be performed on one or more of inference data associated with the inference model and one or more messages.

Data stream generation based on sourcetypes associated with messages

Methods and systems for determining event probabilities and anomalous events are provided. In one implementation, a method includes: receiving source data, where the source data is configured as a plurality of events with associated timestamps; searching the source data, where the searching provides a search result including N events from the plurality of events, where N is an integer greater than one, where each event of the N events includes a plurality of field values, where at least one event of the N events can include one or more categorical field values and one or more numerical field values; and for an event of the N events, determining a probability of occurrence for each field value of the plurality of field values; and using probabilities determined for the plurality of field values, determining a probability of occurrence for the event.

Graphical user interface indicating anomalous events

Feedback on inferred sourcetypes

Systems and methods include obtaining a set of events, each event in the set of events comprising a time-stamped portion of raw machine data, the raw machine data produced by one or more components within an information technology or security environment and reflects activity within the information technology or security environment. Thereafter, a first neural network is used to automatically identify variable text to extract as a field from the set of events. An indication of the variable text is provided as a field extraction recommendation, for example, to a user device for presentation to a user.

Providing field extraction recommendations for display

Embodiments of the present invention are directed to facilitating concurrent forecasting associating with multiple time series data sets. In accordance with aspects of the present disclosure, a request to perform a predictive analysis in association with multiple time series data sets is received. Thereafter, the request is parsed to identify each of the time series data sets to use in predictive analysis. For each time series data set, an object is initiated to perform the predictive analysis for the corresponding time series data set. Generally, the predictive analysis predicts expected outcomes based on the corresponding time series data set. Each object is concurrently executed to generate expected outcomes associated with the corresponding time series data set, and the expected outcomes associated with each of the corresponding time series data sets are provided for display.

Graphical user interface for concurrent forecasting of multiple time series

A computerized method of diagnosing a mislabeling of a source type of a received event. The method comprising operations of receiving an event by a source type analysis logic with a data index and query system, wherein the event includes a portion of raw machine data and is associated with a specific point in time, obtaining an original source type assigned to the event and one or more predicted source types. The one or more predicted source types are determined by analysis of a data representation of the event in view of training data and the training data includes a plurality of data representations corresponding to known source types. Additionally, the computerized method also includes an operation of, determining whether the event has been mislabeled and in response to determining the event has been mislabeled, diagnosing a source of the mislabeling.

Detecting, diagnosing, and directing solutions for source type mislabeling of machine data, including machine data that may contain PII, using machine learning

Facilitating concurrent forecasting of multiple time series

Messages of a first data stream may be accessed from an ingestion buffer in communication with a streaming data processor to receive data from the first data stream. At the streaming data processor and using an inference model, a sourcetype associated with one or more messages from the first data stream may be determined. The one or more messages may include a portion of machine data. Using the streaming data processor, a second data stream may be generated from the first data stream. The second data stream may include a subset of messages from the first data stream. A message of the subset of messages may be included in the second data stream based on a condition associated with the sourcetype for the message. At least one processing operation may be performed on at least one of the subset of messages from the second data stream.

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Union City, CA, United States of America

Nghi Huu Nguyen