Term | Description |
time series | During the configuration of an inspection job for time series, standard time series must be provided for algorithms. Each time series includes UNIX time-stamped metric values that are recorded at equally spaced periods of time. |
entity | An entity is an observed object in an intelligent inspection job. For example, anomaly detection is performed on a service that runs on a machine, and the entity description is "192.0.2.0": Machine IP address,"80": Service port. In this example, you can uniquely identify the entity by using the machine IP address and service port. |
golden metric | A golden metric accurately describes the quality of a service or the stability of an entity. Examples: If you want to describe the request quality of a domain name, you can use the following golden metrics: average response latency per minute, number of requests per minute, number of failed requests per minute, and volume of write traffic per minute. If you want to describe the status of a machine, you can use the following golden metrics: CPU utilization in user mode per minute, CPU utilization in kernel mode per minute, size of resident memory per minute, number of disk I/Os per minute, and average system load per minute. If you want to describe the status of an Object Storage Service (OSS) bucket, you can use the following golden metrics: number of write operations in the bucket per minute, number of read operations in the bucket per minute, and volume of write traffic in the bucket per minute.
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anomaly type | Intelligent Anomaly Analysis provides seven built-in anomaly types, which are commonly used. The anomaly types can be used for filtering. For more information, see Anomaly types in intelligent inspection and Anomaly types in text analysis. |
normalization method | The normalization method is used to simplify calculation. The method converts a dimensional expression into a dimensionless expression, which is equivalent to a scalar. This improves the performance of anomaly detection. |
filtering method | The filtering method filters out signals at unwanted frequencies in a specified band. The method is commonly used for inhibition and interference prevention. Filtering can smooth curves. This helps improve the performance of anomaly detection. |
evaluate | You can evaluate the results of intelligent inspection to report your feedback on intelligent inspection. The Intelligent Anomaly Analysis application can receive your feedback. |
false positive | During time series inspection, an algorithm model detects anomalies and notifies you of the anomalies by using alert notification methods. If the anomalies are not as expected, you can evaluate the anomalies and report your feedback to the Intelligent Inspection Analysis application. The application performs machine learning based on your feedback. |
false negative | During time series inspection, if an algorithm model detects no anomalies, you can evaluate the inspection result of each data point and report your feedback. |
pattern extraction | This method extracts patterns from text objects by using analysis, distillation, and induction. A pattern can describe a class of similar text. |
clustering | In the clustering process, a set of physical or abstract objects are divided into multiple classes that consist of similar objects. A cluster is generated after clustering. A cluster is a set of data objects that are similar to each other but are different from the objects in other clusters. |
unsupervised | Unlabeled training samples are used to resolve issues that occur during pattern recognition. |
supervised | Supervised learning refers to machine learning tasks that train functions or models from labeled training datasets. |
log constant | In most cases, logs are generated by running the logging or print command in programs. For example, the log connect mysql server, latency 212ms may be generated by running logging.info("connect mysql server, latency %dms"). Log constants are always contained in the output of log commands. In this example, the log constant is connect mysql server, latency ms. |
log variable | In most cases, logs are generated by running the logging or print command in programs. For example, the log connect mysql server, latency 212ms may be generated by running logging.info("connect mysql server, latency %dms"). Log variables always change in the output of log commands. In this example, the log variable is 212. |
log template | A log template consists of log constants and the wildcard characters for log variables. A log template is in the text format. For example, the log template for the log connect mysql server, latency 212ms is connect mysql server, latency *ms. The asterisk (*) is used to replace the numeric variable 212. You can use different wildcard characters based on the types of log variables. For example, you can use NUM to represent numeric variables. In this example, the log template is connect mysql server, latency NUMms. |
log category | A log category is represented by a log template. If a log matches a log template, the log belongs to the category that is represented by the template. |
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