Abstract: Embodiments include generating data models that may give semantic meaning for unstructured or structured data that may include data generated and/or received by search engines, including a time series engine. A method includes generating a data model for data stored in a repository. Generating the data model includes generating an initial query string, executing the initial query string on the data, generating an initial result set based on the initial query string being executed on the data, determining one or more candidate fields from one or results of the initial result set, generating a candidate data model based on the one or more candidate fields, iteratively modifying the candidate data model until the candidate data model models the data, and using the candidate data model as the data model.

Abstract: Embodiments of the present invention are directed to identifying related data, in particular, data associated with different source types. In embodiments, a first source type related to a second source type associated with a search query is identified. Field set pairs are identified from a first data set associated with the first source type and a second data set associated with the second source type. Each field set pair can include one field set associated with the first source type and another field set associated with the second source type. For each field set pair, an extent of similarity is determined between the corresponding field sets. Based on the extent of similarities between the corresponding field sets, at least one pair of related field sets is identified. An indication of the at least one pair of related field sets is provided, for example, for presentation to a user.

Abstract: Disclosed is a system and method for cross-silo acquisition, reporting and analysis of enterprise data. A computer system receives enterprise data related to various vertical units of an enterprise, including machine-generated data and human-generated data. The computer system stores the machine-generated data with associations to at least some of the human generated data, and associates persona data representing a plurality of personas with the plurality of vertical units of the enterprise, such that at least one persona is associated with each of the vertical units. The computer system further associates a plurality of user-defined key performance indicators (KPIs) with the personas, and associates each of a plurality of users with at least one of the personas. The computer system computes the KPIs based on the enterprise data, and controls access by the users to the computed KPIs, based on personas to which the users are assigned.

Abstract: Embodiments include generating data models that may give semantic meaning for unstructured or structured data that may include data generated and/or received by search engines, including a time series engine. A method includes generating a data model for data stored in a repository. Generating the data model includes generating an initial query string, executing the initial query string on the data, generating an initial result set based on the initial query string being executed on the data, determining one or more candidate fields from one or results of the initial result set, generating a candidate data model based on the one or more candidate fields, iteratively modifying the candidate data model until the candidate data model models the data, and using the candidate data model as the data model.

Abstract: Embodiments of the present invention are directed to identifying and providing related data field sets. In one embodiment, a first portion of a graphical user interface (GUI) configured to receive a search query is displayed. The GUI enables user interaction to specify a source type in association with the search query. In accordance with a first source type specified in the search query, a first field set associated with the first source type is identified as related to a second field set associated with a second source type. A second portion of the GUI is displayed that includes a relationship indication that indicates the first field set associated with the first source type is related to the second field set associated with a second source type. Further, a third portion of the GUI is displayed that includes an explanation or recommendation associated with the relationship indication.

Abstract: Disclosed is a system and method for cross-silo acquisition, reporting and analysis of enterprise data. A computer system receives enterprise data related to various vertical units of an enterprise, including machine-generated data and human-generated data. The computer system stores the machine-generated data with associations to at least some of the human generated data, and associates persona data representing a plurality of personas with the plurality of vertical units of the enterprise, such that at least one persona is associated with each of the vertical units. The computer system further associates a plurality of user-defined key performance indicators (KPIs) with the personas, and associates each of a plurality of users with at least one of the personas. The computer system computes the KPIs based on the enterprise data, and controls access by the users to the computed KPIs, based on personas to which the users are assigned.

Abstract: Embodiments of the present invention are directed to identifying related data, in particular, data associated with different source types. In embodiments, a first source type related to a second source type associated with a search query is identified. Field set pairs are identified from a first data set associated with the first source type and a second data set associated with the second source type. Each field set pair can include one field set associated with the first source type and another field set associated with the second source type. For each field set pair, an extent of similarity is determined between the corresponding field sets. Based on the extent of similarities between the corresponding field sets, at least one pair of related field sets is identified. An indication of the at least one pair of related field sets is provided, for example, for presentation to a user.

Abstract: Embodiments of the present invention are directed to identifying related data, in particular, data associated with different source types. In embodiments, a first source type related to a second source type associated with a search query is identified. Field set pairs are identified from a first data set associated with the first source type and a second data set associated with the second source type. Each field set pair can include one field set associated with the first source type and another field set associated with the second source type. For each field set pair, an extent of similarity is determined between the corresponding field sets. Based on the extent of similarities between the corresponding field sets, at least one pair of related field sets is identified. An indication of the at least one pair of related field sets is provided, for example, for presentation to a user.

Abstract: Disclosed is a guidance technique that can be applied to guide search and analysis of stored data by a user. The technique can include inputting from a user a portion of a search query expressed in a pipelined search language, at a system for indexing and searching machine data. The system generates and outputs search guidance for the user as the user builds the search query, by applying the portion of the query to an operation flow model, where the operation flow model represents a plurality of searches performable by the system. The operation flow model has been generated based on multi-user historical search data and includes a plurality of states, each representing a different group of related commands of the pipelined search language.

Abstract: Disclosed is a system and method for cross-silo acquisition, reporting and analysis of enterprise data. A computer system receives enterprise data related to various vertical units of an enterprise, including machine-generated data and human-generated data. The computer system stores the machine-generated data with associations to at least some of the human generated data, and associates persona data representing a plurality of personas with the plurality of vertical units of the enterprise, such that at least one persona is associated with each of the vertical units. The computer system further associates a plurality of user-defined key performance indicators (KPIs) with the personas, and associates each of a plurality of users with at least one of the personas. The computer system computes the KPIs based on the enterprise data, and controls access by the users to the computed KPIs, based on personas to which the users are assigned.

Abstract: Disclosed is a system and method for cross-silo acquisition, reporting and analysis of enterprise data. A computer system receives enterprise data related to various vertical units of an enterprise, including machine-generated data and human-generated data. The computer system stores the machine-generated data with associations to at least some of the human generated data, and associates persona data representing a plurality of personas with the plurality of vertical units of the enterprise, such that at least one persona is associated with each of the vertical units. The computer system further associates a plurality of user-defined key performance indicators (KPIs) with the personas, and associates each of a plurality of users with at least one of the personas. The computer system computes the KPIs based on the enterprise data, and controls access by the users to the computed KPIs, based on personas to which the users are assigned.

Abstract: Embodiments of the present invention are directed to identifying and providing related data field sets. In one embodiment, a first portion of a graphical user interface (GUI) configured to receive a search query is displayed. The GUI enables user interaction to specify a source type in association with the search query. In accordance with a first source type specified in the search query, a first field set associated with the first source type is identified as related to a second field set associated with a second source type. A second portion of the GUI is displayed that includes a relationship indication that indicates the first field set associated with the first source type is related to the second field set associated with a second source type. Further, a third portion of the GUI is displayed that includes an explanation or recommendation associated with the relationship indication.

Abstract: Embodiments of the present invention are directed to identifying related data, in particular, data associated with different source types. In embodiments, a first source type related to a second source type associated with a search query is identified. Field set pairs are identified from a first data set associated with the first source type and a second data set associated with the second source type. Each field set pair can include one field set associated with the first source type and another field set associated with the second source type. For each field set pair, an extent of similarity is determined between the corresponding field sets. Based on the extent of similarities between the corresponding field sets, at least one pair of related field sets is identified. An indication of the at least one pair of related field sets is provided, for example, for presentation to a user.

Abstract: Embodiments include generating data models that may give semantic meaning for unstructured or structured data that may include data generated and/or received by search engines, including a time series engine. A method includes generating a data model for data stored in a repository. Generating the data model includes generating an initial query string, executing the initial query string on the data, generating an initial result set based on the initial query string being executed on the data, determining one or more candidate fields from one or results of the initial result set, generating a candidate data model based on the one or more candidate fields, iteratively modifying the candidate data model until the candidate data model models the data, and using the candidate data model as the data model.

Abstract: Embodiments include generating data models that may give semantic meaning for unstructured or structured data that may include data generated and/or received by search engines, including a time series engine. A method includes generating a data model for data stored in a repository. Generating the data model includes generating an initial query string, executing the initial query string on the data, generating an initial result set based on the initial query string being executed on the data, determining one or more candidate fields from one or results of the initial result set, generating a candidate data model based on the one or more candidate fields, iteratively modifying the candidate data model until the candidate data model models the data, and using the candidate data model as the data model.

Abstract: Embodiments are directed towards determining and tracking metadata for the generation of visualizations of requested data. A user may request data by providing a query that may be employed to search for the requested data. The query may include a plurality of commands, which may be employed in a pipeline to perform the search and to generate a table of the requested data. In some embodiments, each command may be executed to perform an action on a set of data. The execution of a command may generate one or more columns to append and/or insert into the table of requested data. Metadata for each generated column may be determined based on the actions performed by executing the commands. The table of requested data and the column metadata may be employed to generate and display a visualization of at least a portion of the requested data to a user.

Abstract: Disclosed is a guidance technique that can be applied to guide search and analysis of stored data by a user. The technique can include inputting from a user a portion of a search query expressed in a pipelined search language, at a system for indexing and searching machine data. The system generates and outputs search guidance for the user as the user builds the search query, by applying the portion of the query to an operation flow model, where the operation flow model represents a plurality of searches performable by the system. The operation flow model has been generated based on multi-user historical search data and includes a plurality of states, each representing a different group of related commands of the pipelined search language.

Abstract: Embodiments include generating data models that may give semantic meaning for unstructured or structured data that may include data generated and/or received by search engines, including a time series engine. A method includes generating a data model for data stored in a repository. Generating the data model includes generating an initial query string, executing the initial query string on the data, generating an initial result set based on the initial query string being executed on the data, determining one or more candidate fields from one or results of the initial result set, generating a candidate data model based on the one or more candidate fields, iteratively modifying the candidate data model until the candidate data model models the data, and using the candidate data model as the data model.

Abstract: Embodiments include generating data models that may give semantic meaning for unstructured or structured data that may include data generated and/or received by search engines, including a time series engine. A method includes generating a data model for data stored in a repository. Generating the data model includes generating an initial query string, executing the initial query string on the data, generating an initial result set based on the initial query string being executed on the data, determining one or more candidate fields from one or results of the initial result set, generating a candidate data model based on the one or more candidate fields, iteratively modifying the candidate data model until the candidate data model models the data, and using the candidate data model as the data model.

Abstract: Embodiments are directed towards determining and tracking metadata for the generation of visualizations of requested data. A user may request data by providing a query that may be employed to search for the requested data. The query may include a plurality of commands, which may be employed in a pipeline to perform the search and to generate a table of the requested data. In some embodiments, each command may be executed to perform an action on a set of data. The execution of a command may generate one or more columns to append and/or insert into the table of requested data. Metadata for each generated column may be determined based on the actions performed by executing the commands. The table of requested data and the column metadata may be employed to generate and display a visualization of at least a portion of the requested data to a user.