| 摘要 |
A system and method are disclosed for providing metric recommendations by a cloud event log analytics system. The log analytics system includes a user interface which allows users to view metric recommendations, view, modify, annotate, delete, or create log metrics. In a first embodiment, centroid vectors are created from metadata associated with user access of log metrics. The centroid vectors are compared to metrics vectors created from log metrics and the results are ranked and provided to users as metric recommendations. In a second embodiment, classification rules are inferred for metric matrix tables containing metadata about log metric usage. Classification rules are assigned to a decision tree used to calculate composite probabilities of interest of log metrics. A recommendation matrix incorporate the composite probabilities of interest to predict the degree of interest an analytics user may have in a log metric for a given role. |
| 主权项 |
1. A computer executed method for generating log metric recommendations for a user of log analytics system, the method comprising:
storing in a database a plurality of log metrics, each log metric defining a query on a database of log events in an enterprise system, each log metric having a metric name, metric description, and metric parameters; storing in the database metric usage data indicating usage of the log metrics by users, each user having role; for each log metric in the database, automatically generating a metric vector comprising a term vector having plurality of term weights, wherein the terms of the term vectors are selected from terms used in the metric names, metric descriptions, and metric parameters, and the term weights corresponds to a measure of frequency of the terms appearing in the log metric; for the user, querying the database metric usage data to select metric vectors used by the user, and generating a user vector for the user as a centroid of the selected metric vectors; selecting a target set of metrics from the database, and obtaining a corresponding set of metric vectors; and for each metric vector in the target set, generating a similarity score between the metric vector and the user vector; ranking the metric vectors in the target set based on their similarity scores, to obtain one or more highest ranking metric vectors; and displaying an output in the log analytics system of at least one of metric corresponding to one of highest ranking metric vectors. |
