Splunk Inc.

Gyanendra Rana

Iryna Vogler-Ivashchanka

Adam Jamison Oliner

Manish Sainani

Iman Makaremi

Harsh Keswani

Alexander Kim

Cory Eugene Burke

Andrew Hoy Stein

Sergey Slepian

Operational machine components of an information technology (IT) or other microprocessor- or microcontroller-permeated environment generate disparate forms of machine data. Network connections are established between these components and processors of an automatic data intake and query system (DIQS). The DIQS conducts network transactions on a periodic and/or continuous basis with the machine components to receive the disparate data and ingest certain of the data as measurement entries of a DIQS metrics datastore that is searchable for DIQS query processing. The DIQS may receive search queries to process against the received and ingested data via an exposed network interface. In one example embodiment, a query building component conducts a user interface using a network attached client device. The query building component may elicit search criteria via the user interface using a natural language interface, construct a proper query therefrom, and present new information based on results returned from the DIQS.

Generating and using alert definitions

Facilitating alerts for predicted conditions

Techniques are described for providing a 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.' For example, the ML data analytics application may enable users to create experiments related to prediction of numeric fields (for example, using linear regression techniques), predicting categorical fields (for example, using logistic regression), detecting numerical outliers (for example, using various distribution statistics), detecting categorical outliers (for example, using probabilistic statistics), forecasting time series data, and clustering numeric events (for example, using k-means, density-based spatial clustering of applications with noise (DBSCAN), spectral clustering, or other techniques), among other possible uses of various types of ML models to analyze data.

Guided workflows for machine learning-based data analyses

Facilitating metric forecasting via a graphical user interface

Metric forecasting interface with alert prediction

Growing community of inventors

Burnaby, Canada

Gyanendra Rana