Machine-learning data analysis tool
First Claim
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1. A method comprising:
- generating, by a computer system, a graphical user interface that enables a user of a processing device to;
select a machine learning (ML) model for training, from a plurality of selectable ML models,specify a first dataset of timestamped machine data events based on which the selected ML model is to be trained;
invoke training of the selected ML model based on the first dataset and view a trained ML model as a result of the training, andinvoke application of the trained ML model to a second dataset of timestamped machine data events and view a result of the application of the trained ML model to the second dataset;
dynamically generating user guidance on potential analysis paths for the user to take, based on the first dataset, wherein the user guidance on potential ML analysis paths for the user to take comprisesa suggested data field of the first dataset upon which to base training of the ML model; and
a suggested type of ML analysis to apply; and
causing the user guidance to be output to the user via the graphical user interface.
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Abstract
Disclosed herein is a computer-implemented tool that facilitates data analysis by use of machine learning (ML) techniques. The tool cooperates with a data intake and query system and provides a graphical user interface (GUI) that enables a user to train and apply a variety of different ML models on user-selected datasets of stored machine data. The tool can provide active guidance to the user, to help the user choose data analysis paths that are likely to produce useful results and to avoid data analysis paths that are less likely to produce useful results.
12 Citations
27 Claims
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1. A method comprising:
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generating, by a computer system, a graphical user interface that enables a user of a processing device to; select a machine learning (ML) model for training, from a plurality of selectable ML models, specify a first dataset of timestamped machine data events based on which the selected ML model is to be trained; invoke training of the selected ML model based on the first dataset and view a trained ML model as a result of the training, and invoke application of the trained ML model to a second dataset of timestamped machine data events and view a result of the application of the trained ML model to the second dataset; dynamically generating user guidance on potential analysis paths for the user to take, based on the first dataset, wherein the user guidance on potential ML analysis paths for the user to take comprises a suggested data field of the first dataset upon which to base training of the ML model; and a suggested type of ML analysis to apply; and causing the user guidance to be output to the user via the graphical user interface. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25)
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26. A computer system comprising:
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a communication device through which to communicate on a computer network; and at least one processor operatively coupled to the communication device and configured to execute operations including generating a graphical user interface that enables a user of a processing device to; select a machine learning (ML) model for training, from a plurality of selectable ML models, specify a first dataset of timestamped machine data events based on which the selected ML model is to be trained; invoke training of the selected ML model based on the first dataset and view a trained ML model as a result of the training, and invoke application of the trained ML model to a second dataset of timestamped machine data events and view a result of the application of the trained ML model to the second dataset; dynamically generating user guidance on potential analysis paths for the user to take, based on the first dataset, wherein the user guidance on potential ML analysis paths for the user to take comprises a suggested data field of the first dataset upon which to base training of the ML model; and a suggested type of ML analysis to apply; and causing the user guidance to be output to the user via the graphical user interface.
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27. A non-transitory machine-readable storage medium for use in a processing system, the non-transitory machine-readable storage medium storing instructions, an execution of which in the processing system causes the processing system to perform operations comprising:
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generating a graphical user interface that enables a user of a processing device to; select a machine learning (ML) model for training, from a plurality of selectable ML models, specify a first dataset of timestamped machine data events based on which the selected ML model is to be trained; invoke training of the selected ML model based on the first dataset and view a trained ML model as a result of the training, and invoke application of the trained ML model to a second dataset of timestamped machine data events and view a result of the application of the trained ML model to the second dataset; dynamically generating user guidance on potential analysis paths for the user to take, based on the first dataset, wherein the user guidance on potential ML analysis paths for the user to take comprises a suggested data field of the first dataset upon which to base training of the ML model; and a suggested type of ML analysis to apply; and causing the user guidance to be output to the user via the graphical user interface.
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Specification