Abstract: Some embodiments relate to analyzing log records. A method may include determining a first shape identification value that characterizes a shape described by data points of a first time series signal that represents a time distribution of timestamps of a first plurality of messages. For each message among a second plurality of messages, the method may also include determining a shape identification value for the message that characterizes a shape described by data points of a corresponding time series signal that represents a time distribution of timestamps of a plurality of instances of the message. The method may further include determining that a shape identification value, from among the shape identification values for the second plurality of messages, is the same as the first shape identification value and, in response to the determining, providing information identifying the corresponding message to a user interface.

Abstract: The present disclosure relates to identifying process flows from log sources (e.g., log files), and generating visual representations (e.g., flow diagrams, Sankey diagrams, etc.) of the identified process flows. In addition, the present disclosure relates to clustering of tree structures based on the shape of the tree structure using one or more hashing algorithms. The present disclosure also relates to a user interface that presents a query builder for efficiently querying a log analytics system for tree structures that satisfy a user-defined range.

Abstract: The present disclosure relates to identifying process flows from log sources (e.g., log files), and generating visual representations (e.g., flow diagrams, Sankey diagrams, etc.) of the identified process flows. In addition, the present disclosure relates to clustering of tree structures based on the shape of the tree structure using one or more hashing algorithms. The present disclosure also relates to a user interface that presents a query builder for efficiently querying a log analytics system for tree structures that satisfy a user-defined range.

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: 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: 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: 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: Some embodiments relate to assigning individual log messages to clusters. An initial cluster assignment may be performed by applying a hash function to one or more non-variable components of the message to generate an initial cluster identifier. Subsequently, clustering may be further refined (e.g., by determining whether to merge clusters based on similarity values). An interface can present a representative message of each cluster and indicate which portions of the message correspond to a variable component. Particular inputs detected at the input corresponding to one of these components can cause other values for the component to be presented. For a given cluster, timestamps of assigned messages can be used to generate a time series, which can facilitate grouping of clusters (with similar or complementary shapes) and/or triggering alerts (with a condition corresponding to a temporal aspect).

Abstract: Some embodiments relate to assigning individual log messages to clusters. An initial cluster assignment may be performed by applying a hash function to one or more non-variable components of the message to generate an initial cluster identifier. Subsequently, clustering may be further refined (e.g., by determining whether to merge clusters based on similarity values). An interface can present a representative message of each cluster and indicate which portions of the message correspond to a variable component. Particular inputs detected at the input corresponding to one of these components can cause other values for the component to be presented. For a given cluster, timestamps of assigned messages can be used to generate a time series, which can facilitate grouping of clusters (with similar or complementary shapes) and/or triggering alerts (with a condition corresponding to a temporal aspect).

Abstract: Some embodiments relate to assigning individual log messages to clusters. An initial cluster assignment may be performed by applying a hash function to one or more non-variable components of the message to generate an initial cluster identifier. Subsequently, clustering may be further refined (e.g., by determining whether to merge clusters based on similarity values). An interface can present a representative message of each cluster and indicate which portions of the message correspond to a variable component. Particular inputs detected at the input corresponding to one of these components can cause other values for the component to be presented. For a given cluster, timestamps of assigned messages can be used to generate a time series, which can facilitate grouping of clusters (with similar or complementary shapes) and/or triggering alerts (with a condition corresponding to a temporal aspect).