Oracle International Corporation

Dhileeban Kumaresan

Sreeji Krishnan Das

Jae Young Yoon

Venktesh Alvenkar

Harish Akali

Adrienne Wong

Hari Krishna Galla

Chandra Sekhar Komali

Rodrigo Reyes

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.

Systems and methods for identifying process flows from log files and visualizing the flow

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.

Clustering using natural language processing

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.

Multi-dimensional clustering and correlation with interactive user interface design

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.

Systems and methods for displaying representative samples of tabular data

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.

Extracting and labeling custom information from log messages

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.

Querying distributed log data using virtual fields defined in query strings

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).

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Hyderabad Telangana, India

Dhileeban Kumaresan