Abstract: Embodiments of the present invention are directed to facilitating data preprocessing for machine learning. In accordance with aspects of the present disclosure, a training set of data is accessed. A preprocessing query specifying a set of preprocessing parameter values that indicate a manner in which to preprocess the training set of data is received. Based on the preprocessing query, a preprocessing operation is performed to preprocess the training set of data in accordance with the set of preprocessing parameter values to obtain a set of preprocessed data. The set of preprocessed data can be provided for presentation as a preview. Based on an acceptance of the set of preprocessed data, the set of preprocessed data is used to train a machine learning model that can be subsequently used to predict data.

Abstract: 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.

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.

Abstract: Systems and methods are described for processing ingested data in an asynchronous manner as the data is being ingested to detect potential anomalies. For example, one or more streaming data processors can convert data as the data is ingested into a comparable data structure, determine whether the comparable data structure should be assigned to an existing data pattern or a new data pattern, and optionally update a characteristic of the data pattern to which the comparable data structure is assigned. The streaming data processor(s) can perform these operations automatically in real-time or in periodic batches. Once one or more comparable data structures have been assigned to one or more data patterns, the streaming data processor(s) can analyze the comparable data structures assigned to a particular data pattern to determine whether any of the comparable data structures appear to be anomalous.

Abstract: Systems and methods are described for processing ingested data in an asynchronous manner as the data is being ingested to detect potential anomalies. For example, one or more streaming data processors can convert data as the data is ingested into a comparable data structure, determine whether the comparable data structure should be assigned to an existing data pattern or a new data pattern, and optionally update a characteristic of the data pattern to which the comparable data structure is assigned. The streaming data processor(s) can perform these operations automatically in real-time or in periodic batches. Once one or more comparable data structures have been assigned to one or more data patterns, the streaming data processor(s) can analyze the comparable data structures assigned to a particular data pattern to determine whether any of the comparable data structures appear to be anomalous.

Abstract: 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.

Abstract: 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.

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.

Abstract: Embodiments of the present invention are directed to facilitating data preprocessing for machine learning. In accordance with aspects of the present disclosure, a training set of data is accessed. A preprocessing query specifying a set of preprocessing parameter values that indicate a manner in which to preprocess the training set of data is received. Based on the preprocessing query, a preprocessing operation is performed to preprocess the training set of data in accordance with the set of preprocessing parameter values to obtain a set of preprocessed data. The set of preprocessed data can be provided for presentation as a preview. Based on an acceptance of the set of preprocessed data, the set of preprocessed data is used to train a machine learning model that can be subsequently used to predict data.

Abstract: 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.

Abstract: Systems and methods are described for processing ingested data in an asynchronous manner as the data is being ingested to detect potential anomalies. For example, one or more streaming data processors can convert data as the data is ingested into a comparable data structure, determine whether the comparable data structure should be assigned to an existing data pattern or a new data pattern, and optionally update a characteristic of the data pattern to which the comparable data structure is assigned. The streaming data processor(s) can perform these operations automatically in real-time or in periodic batches. Once one or more comparable data structures have been assigned to one or more data patterns, the streaming data processor(s) can analyze the comparable data structures assigned to a particular data pattern to determine whether any of the comparable data structures appear to be anomalous.

Abstract: Embodiments of the present invention are directed to facilitating data preprocessing for machine learning. In accordance with aspects of the present disclosure, a training set of data is accessed. A preprocessing query specifying a set of preprocessing parameter values that indicate a manner in which to preprocess the training set of data is received. Based on the preprocessing query, a preprocessing operation is performed to preprocess the training set of data in accordance with the set of preprocessing parameter values to obtain a set of preprocessed data. The set of preprocessed data can be provided for presentation as a preview. Based on an acceptance of the set of preprocessed data, the set of preprocessed data is used to train a machine learning model that can be subsequently used to predict data.

Abstract: Embodiments of the present invention are directed to facilitating data preprocessing for machine learning. In accordance with aspects of the present disclosure, a training set of data is accessed. A preprocessing query specifying a set of preprocessing parameter values that indicate a manner in which to preprocess the training set of data is received. Based on the preprocessing query, a preprocessing operation is performed to preprocess the training set of data in accordance with the set of preprocessing parameter values to obtain a set of preprocessed data. The set of preprocessed data can be provided for presentation as a preview. Based on an acceptance of the set of preprocessed data, the set of preprocessed data is used to train a machine learning model that can be subsequently used to predict data.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Embodiments of the present invention are directed to facilitating data preprocessing for machine learning. In accordance with aspects of the present disclosure, a training set of data is accessed. A preprocessing query specifying a set of preprocessing parameter values that indicate a manner in which to preprocess the training set of data is received. Based on the preprocessing query, a preprocessing operation is performed to preprocess the training set of data in accordance with the set of preprocessing parameter values to obtain a set of preprocessed data. The set of preprocessed data can be provided for presentation as a preview. Based on an acceptance of the set of preprocessed data, the set of preprocessed data is used to train a machine learning model that can be subsequently used to predict data.