Generating machine learning-based outlier detection models using timestamped event data

    公开(公告)号:US12014255B1

    公开(公告)日:2024-06-18

    申请号:US18334996

    申请日:2023-06-14

    Applicant: Splunk Inc.

    CPC classification number: G06N20/00 G06F16/9038 G06F17/18

    Abstract: Techniques are described for providing a machine learning (ML) data analytics application including guided ML workflows that facilitate the end-to-end training and use of various types of ML models, where such guided workflows may also be referred to as ML “experiments.” One such model is an outlier detection model to assist in the monitoring of computer network traffic and computer performance. For example, the ML data analytics application may generate an outlier detection model using user-identified data from a data source and parameter information. The generates outlier detection model can include distribution functions of distribution types selected from a plurality of distribution types by a distribution fitting algorithm.

    Dynamic resolution estimation for a detector

    公开(公告)号:US12013880B2

    公开(公告)日:2024-06-18

    申请号:US17721251

    申请日:2022-04-14

    Applicant: SPLUNK Inc.

    CPC classification number: G06F16/287 G06F16/24568 G06F16/2477 H04L43/08

    Abstract: Described are systems, methods, and techniques for collecting, analyzing, processing, and storing time series data and for evaluating and dynamically estimating a resolution of one or more streams of data points and updating an output resolution. Responsive to receiving a stream of data points, a data resolution can be derived and an output resolution can be set to a first value. When a change to the data resolution is detected, the output resolution can be changed, modifying a frequency at which output data points are generated and/or transmitted. In some instances, a detector can be implemented to trigger an alert responsive to ingested data points corresponding with triggering parameters. An output resolution for the detector can be dynamically modified based on dynamically detecting a change to the data resolution of the stream of data.

    Supporting graph data structure transformations in graphs generated from a query to event data

    公开(公告)号:US12001426B1

    公开(公告)日:2024-06-04

    申请号:US18295567

    申请日:2023-04-04

    Applicant: Splunk Inc.

    CPC classification number: G06F16/24526 G06F8/77 G06F16/212

    Abstract: Systems and methods are disclosed for supporting transformations of a graph generated from a query to event data. The event data may be unstructured event data, from which instances of a journey can be identified that represent sequences of related events describing actions performed in a computing environment. When evaluating journey instances, it can be helpful to visualize the instances as a graph. Depending on the instances viewed, a user may desire different modifications to the graph. While such modifications can be made when initially building instances from the unstructured event data, this can limit reuse of the resulting instances (since the modification would also be present when evaluating other subsets). To address this, embodiments of the present disclosure enable graph modifications to be applied to subsets of journey instances after building those instances from unstructured event data, increasing reuse of instances built from a query against the unstructured data.

    Assigning raw data size of source data to storage consumption of an account

    公开(公告)号:US11989707B1

    公开(公告)日:2024-05-21

    申请号:US17329384

    申请日:2021-05-25

    Applicant: SPLUNK Inc.

    CPC classification number: G06Q20/102 G06F16/316 G06Q20/08

    Abstract: Provided are systems and methods for managing storage of machine data. In one embodiment, a method can be provided. The method can include receiving, from one or more data sources, raw machine data; processing the raw machine data to generate processed machine data; storing the processed machine data in a data store; and determining an allocated data size associated with the processed machine data stored in the data store, wherein the allocated data size is the size of the raw machine data corresponding to the processed machine data stored in the data store.

    GENERATION OF MODIFIED QUERIES USING A FIELD VALUE FOR DIFFERENT FIELDS

    公开(公告)号:US20240143612A1

    公开(公告)日:2024-05-02

    申请号:US18051458

    申请日:2022-10-31

    Applicant: Splunk Inc.

    CPC classification number: G06F16/248 G06F16/2425

    Abstract: Systems and methods are described for generation and execution of modified queries. An input can be received via a visualization of a user interface. The input may identify a first field value and a first field for execution of a query. A set of data for execution of the query can be identified based on the input. Alias data may identify a second field that is associated with the first field. Using the alias data, a modified query can be generated based on the query and the second field. The modified query can be executed to generate query results. The query results can be displayed via a visualization of the user interface based on the first field.

    Highly available message ingestion by a data intake and query system

    公开(公告)号:US11954541B1

    公开(公告)日:2024-04-09

    申请号:US17588074

    申请日:2022-01-28

    Applicant: Splunk Inc.

    Inventor: Craig Keith Carl

    CPC classification number: G06F9/546

    Abstract: Techniques are described for providing a highly available data ingestion system for ingesting machine data sent from remote data sources across potentially unreliable networks. To provide for highly available delivery of such data, a data intake and query system provides users with redundant sets of ingestion endpoints to which messages sent from users' computing environments can be delivered to the data intake and query system. Users' data sources, or data forwarding components configured to obtain and send data from one or more data sources, are then configured to encapsulate obtained machine data into discrete messages and to send copies of each message to two or more of the ingestion endpoints provisioned for a user. The ingestion endpoints receiving the messages implement a deduplication technique and provide only one copy of each message to a subsequent processing component (e.g., to an indexing subsystem for event generation, event indexing, etc.).

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