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: 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: 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.