Abstract: Techniques for implementing user interfaces, systems, and processes for multidimensional clustering and analysis are described herein. In one aspect, an application or cloud service receives a request to cluster a set of records where the request identifies a first set of one or more dimensions to use for clustering and a second set of one or more dimensions to analyze for correlation patterns. Responsive to receiving the request to cluster the set of records, the system generates clusters based at least in part on variances in the first set of one or more dimensions, wherein each cluster includes at least one record from the set of records. The system may generate, for each respective cluster, an analytic result that identifies how strongly the second set of one or more dimensions correlate to the respective cluster. The system may present the clusters and analytic results for further processing.

Abstract: Techniques for performing analytics using automatically generated labels for time series data and numerical lists are disclosed. In some embodiments, a system loads a set of one or more time series datasets. A respective time series dataset may include a set of data points based on varying values of a metric of one or more computing resources over a window of time. The system assigns labels to a subset of the data points in the time series datasets. The label assigned to a given data point may be descriptive of a pattern reflected by the data point relative to other data points in the time series. The system further identifies a pattern of automatically assigned labels that is indicative of an event affecting the one or more computing resources. Responsive to identifying the pattern of labels, the system may trigger a responsive action.

Abstract: In one aspect, a system receives a request to cluster a set of log records. Responsive to receiving the request, the system identifies at least one dictionary that defines a set of tokens and corresponding token weights and generates, based at least in part on the set of tokens and corresponding token weights, a set of clusters such that each cluster in the set of clusters represents a unique combination of two or more tokens from the dictionary and groups a subset of log records mapped to the unique combination of two or more tokens. The system may then perform one or more automated actions based on at least one cluster in the set of clusters.

Abstract: Techniques for performing analytics using automatically generated labels for time series data and numerical lists are disclosed. In some embodiments, a system loads a set of one or more time series datasets. A respective time series dataset may include a set of data points based on varying values of a metric of one or more computing resources over a window of time. The system assigns labels to a subset of the data points in the time series datasets. The label assigned to a given data point may be descriptive of a pattern reflected by the data point relative to other data points in the time series. The system further identifies a pattern of automatically assigned labels that is indicative of an event affecting the one or more computing resources. Responsive to identifying the pattern of labels, the system may trigger a responsive action.

Abstract: In one aspect, a system receives a request to cluster a set of log records. Responsive to receiving the request, the system identifies at least one dictionary that defines a set of tokens and corresponding token weights and generates, based at least in part on the set of tokens and corresponding token weights, a set of clusters such that each cluster in the set of clusters represents a unique combination of two or more tokens from the dictionary and groups a subset of log records mapped to the unique combination of two or more tokens. The system may then perform one or more automated actions based on at least one cluster in the set of clusters.

Abstract: Techniques for implementing user interfaces, systems, and processes for multidimensional clustering and analysis are described herein. In one aspect, an application or cloud service receives a request to cluster a set of records where the request identifies a first set of one or more dimensions to use for clustering and a second set of one or more dimensions to analyze for correlation patterns. Responsive to receiving the request to cluster the set of records, the system generates clusters based at least in part on variances in the first set of one or more dimensions, wherein each cluster includes at least one record from the set of records. The system may generate, for each respective cluster, an analytic result that identifies how strongly the second set of one or more dimensions correlate to the respective cluster. The system may present the clusters and analytic results for further processing.

Abstract: Techniques for implementing user interfaces, systems, and processes for multidimensional clustering and analysis are described herein. In one aspect, an application or cloud service receives a request to cluster a set of records where the request identifies a first set of one or more dimensions to use for clustering and a second set of one or more dimensions to analyze for correlation patterns. Responsive to receiving the request to cluster the set of records, the system generates clusters based at least in part on variances in the first set of one or more dimensions, wherein each cluster includes at least one record from the set of records. The system may generate, for each respective cluster, an analytic result that identifies how strongly the second set of one or more dimensions correlate to the respective cluster. The system may present the clusters and analytic results for further processing.

Abstract: The present disclosure generally relates to systems and methods that efficiently display tabular data (e.g., a large data set of a million or more rows of data with multiple data fields). More particularly, the present disclosure relates to systems and methods that compress the tabular data to a representative data set that maintains the data density and data variation of the original tabular data, and that display the representative data set with respect to clusters formed.

Abstract: In one aspect, a system receives a request to cluster a set of log records. Responsive to receiving the request, the system identifies at least one dictionary that defines a set of tokens and corresponding token weights and generates, based at least in part on the set of tokens and corresponding token weights, a set of clusters such that each cluster in the set of clusters represents a unique combination of two or more tokens from the dictionary and groups a subset of log records mapped to the unique combination of two or more tokens. The system may then perform one or more automated actions based on at least one cluster in the set of clusters.

Abstract: Techniques for implementing user interfaces, systems, and processes for multidimensional clustering and analysis are described herein. In one aspect, an application or cloud service receives a request to cluster a set of records where the request identifies a first set of one or more dimensions to use for clustering and a second set of one or more dimensions to analyze for correlation patterns. Responsive to receiving the request to cluster the set of records, the system generates clusters based at least in part on variances in the first set of one or more dimensions, wherein each cluster includes at least one record from the set of records. The system may generate, for each respective cluster, an analytic result that identifies how strongly the second set of one or more dimensions correlate to the respective cluster. The system may present the clusters and analytic results for further processing.

Abstract: A set of field values corresponding to a set of underlying fields are extracted from individual log messages. A space of potential values for underlying field(s) is identified. The space of potential values is segmented into value subspaces. Each value subspace is automatically associated with a category name. A definition for the new categorical field is generated, which indicates how a categorical value of the new categorical field depends on value(s) of the underlying field(s). For each log message, a categorical value is determined for the new categorical field based on the definition and the one or more values of the one or more underlying fields extracted from the log message. A presentation is generated that represents, for each log message, the particular category name corresponding to the categorical value determined for the log message.

Abstract: Techniques for performing analytics using automatically generated labels for time series data and numerical lists are disclosed. In some embodiments, a system loads a set of one or more time series datasets. A respective time series dataset may include a set of data points based on varying values of a metric of one or more computing resources over a window of time. The system assigns labels to a subset of the data points in the time series datasets. The label assigned to a given data point may be descriptive of a pattern reflected by the data point relative to other data points in the time series. The system further identifies a pattern of automatically assigned labels that is indicative of an event affecting the one or more computing resources. Responsive to identifying the pattern of labels, the system may trigger a responsive action.

Abstract: The present disclosure generally relates to systems and methods that efficiently display tabular data (e.g., a large data set of a million or more rows of data with multiple data fields). More particularly, the present disclosure relates to systems and methods that compress the tabular data to a representative data set that maintains the data density and data variation of the original tabular data, and that display the representative data set with respect to clusters formed.

Abstract: The present disclosure relates to querying log messages stored at log endpoints (e.g., log cores) using query strings that reference virtual fields, and generating log field views presenting aggregate information for groups of log messages that are grouped based on criteria defined in the query string.

Abstract: Systems, methods, and other embodiments associated with a synchronized test master are described. In one embodiment, a method periodically synchronizing test master data to a source database by, at a synchronization time: modifying the test master data to reflect changes to the source database since a last synchronization time; populating a transaction log with data manipulation operations performed on the source database since a last transaction time; and taking a snapshot of the test master data. Synchronization data is derived from the transaction log that includes a snapshot time for the snapshot and a modification time for each data manipulation operation in the transaction log. The method includes storing the synchronization data and snapshot for use in generating a test database that represents a point-in-time version of the source database.

Abstract: A set of field values corresponding to a set of underlying fields are extracted from individual log messages. A space of potential values for underlying field(s) is identified. The space of potential values is segmented into value subspaces. Each value subspace is automatically associated with a category name. A definition for the new categorical field is generated, which indicates how a categorical value of the new categorical field depends on value(s) of the underlying field(s). For each log message, a categorical value is determined for the new categorical field based on the definition and the one or more values of the one or more underlying fields extracted from the log message. A presentation is generated that represents, for each log message, the particular category name corresponding to the categorical value determined for the log message.

Abstract: The present disclosure relates to querying log messages stored at log endpoints (e.g., log cores) using query strings that reference virtual fields, and generating log field views presenting aggregate information for groups of log messages that are grouped based on criteria defined in the query string.

Abstract: Systems, methods, and other embodiments associated with a synchronized test master are described. In one embodiment, a method periodically synchronizing test master data to a source database by, at a synchronization time: modifying the test master data to reflect changes to the source database since a last synchronization time; populating a transaction log with data manipulation operations performed on the source database since a last transaction time; and taking a snapshot of the test master data. Synchronization data is derived from the transaction log that includes a snapshot time for the snapshot and a modification time for each data manipulation operation in the transaction log. The method includes storing the synchronization data and snapshot for use in generating a test database that represents a point-in-time version of the source database.

Abstract: An Environment Management System (EMS) provides on-demand instances of environments for testing hardware and/or software configurations. In various embodiments, the EMS includes a processing engine that handles parsing and execution of commands in a definition file that express the lifecycle of a testing environment. Accordingly, the EMS provides flexibility to end users in the way that environments are configured and deployed. An environment definition file is provided that may contain a standard set of scripts for implementing the lifecycle of the environment, along with any custom logic specific to a user or customer.

Abstract: An Environment Management System (EMS) provides on-demand instances of environments for testing hardware and/or software configurations. In various embodiments, the EMS includes a processing engine that handles parsing and execution of commands in a definition file that express the lifecycle of a testing environment. Accordingly, the EMS provides flexibility to end users in the way that environments are configured and deployed. An environment definition file is provided that may contain a standard set of scripts for implementing the lifecycle of the environment, along with any custom logic specific to a user or customer.