Method and system for realizing non-business process irregularity detection in large data environment

Abstract

The invention relates to a method for realizing non-business process irregularity detection in a large data environment. The method comprises steps: a system introduces a standardization process model and builds a business object type; the system associates the business object type and the standardization process model; the system acquires standard data of a non-business process; the system carries out process recovery on the data of the standard data; and the system carries out irregularity analysis on the standard data after recovery. The invention also relates to a system for realizing non-business process irregularity detection in a large data environment, which comprises a standardization process definition module, a process association module, a process recovery and irregularity definition module, a data extraction module and a process recovery and irregularity analysis module. by adopting the method and the system of the invention, irregularities in the non-business handling process by an enterprise can be detected in real time, the business activity monitoring field is further expanded, the structure is simple, expansibility is good, the detection efficiency is high, portability is strong, and a wider application range is provided.

Description

technical field [0001] The present invention relates to the field of business process monitoring, in particular to the field of process violation detection, and specifically refers to a method and system for detecting non-business process violations in a big data environment. Background technique [0002] With the continuous advancement of informatization in various industries, the degree of automation of industrial enterprises has deepened, most of the business has achieved information management, and various business process platforms are providing support for the daily business of enterprises. Due to the extremely large and complex businesses in industries such as banking and telecommunications, a large number of independent business systems in the enterprise and various complicated applications isolated from each other in the same business have brought a lot of pressure to daily operations and IT departments. Many questions: [0003] (1) The input and output volumes of ...

AI Technical Summary

Problems solved by technology

[0003] (1) The input and output volumes of business processes across branch centers or even different links of the same center do not match, but it is difficult to locate the abnormal operations that cause these data differences, and it is also impossible to continuously track the stock operations;

[0004] (2) A production capacity analysis report needs several hours of work to extract business data from multiple systems, and the business status has changed during this period;

[0005] (3) After two or three hours of hard investigation, it was found that the cause of the system failure was that the disk space was insufficient and the log could not be written;

[0006] (4) The pressure on the system increases sharply, but it is not known which link the application consumes resources

However, traditional business activity monitoring technologies generally only support the collection and analysis of business process application data, and cannot monitor process-related activities for a large number of legacy applications that are not business process application types, and legacy applications are often important in the process of enterprise informatization. Assets, if the process activities in legacy applications cannot be effectively monitored, the significance of traditional business activity monitoring technologies is also very limited

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