Parallel operation flow anomaly detection method oriented to data model

Abstract

The invention provides a parallel operation flow anomaly detection method oriented to a data model, and the method comprises the following steps: 1) carrying out division on an operation flow through structural validation, and distributing nodes in the same branch under the same gateway into a block; 2) establishing a data manipulation algebra system on a data manipulation set, and establishing a task data existence matrix according to the data manipulation algebra system, wherein each line of the matrix is a data existence state vector corresponding to each task in a procedural model, and each row of the matrix is corresponding to a data object in the data model; 3) based on the task state matrix, carrying out detection on the operation of the data through the task and the anomaly of the operation flow through the existence state of the data. The parallel operation flow anomaly detection method can be used for efficiently processing the procedural model containing a plurality of parallel branches, and the operation flow anomaly detection efficiency is high.

Description

technical field [0001] The invention belongs to the field of information technology, and in particular relates to a data model-oriented parallel business process abnormality detection method, which can efficiently perform abnormality detection on large-scale parallel business processes. Background technique [0002] A business process is "a collection of logically related activities performed to achieve a certain business purpose". The output of a business process is a product or service that meets the needs of the market. The correctness of a business process is an important prerequisite for achieving the established goals of an enterprise. Abnormal business processes will lead to business losses, such as lower user evaluation, waste of resources, lower efficiency, and so on. Business process anomaly detection is to check the errors in the business process model through an automated method. As an important means to improve the quality of the process model, it has become an ...

AI Technical Summary

Problems solved by technology

However, there are currently few methods for processing optimization for parallel processes.

2) The exception is due to an inconsistency between the parallel process model and the data model

These methods can detect errors in the control structure, such as deadlocks, unreachable tasks, etc., but cannot detect the exceptions in the above example

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