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Double-granularity noise log filtering method based on incidence relation

A technology of association relationship and filtering method, which is applied in the direction of instruments, electrical digital data processing, hardware monitoring, etc., and can solve problems such as unguaranteed, model structure changes, and inability to solve missing noise events

Active Publication Date: 2019-07-19
HANGZHOU DIANZI UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Among them, coarse-grained filtering will directly remove the original log from the track containing noise events, but for small-scale log data, removing the entire track may have a large change in the mined model structure
Fine-grained filtering only removes noise events and retains other events on the track, but it cannot guarantee that this behavior will not bring new noise to the track while removing noise events, and this type of algorithm cannot solve the problem of missing events. Problems arising from noise events

Method used

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  • Double-granularity noise log filtering method based on incidence relation
  • Double-granularity noise log filtering method based on incidence relation
  • Double-granularity noise log filtering method based on incidence relation

Examples

Experimental program
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Embodiment

[0092] The steps in this embodiment are the same as those described above in the specific implementation mode, and will not be repeated here. Part of the implementation process and results are shown below:

[0093] Data source acquisition: the original log file used in this embodiment uses the java toolkit JDOM to read the log file, obtains the root node root of the log file, obtains the child node element named Process under the root node, and further obtains the child node element named Process under the Process node. All child node elements of ProcessInstance. A ProcessInstance node contains all the information of an execution instance of the process. It usually has multiple node elements named AuditTrailEntry. The detailed information of each event that occurs in the process instance is recorded in an AuditTrailEntry node element. These AuditTrailEntry nodes include Many event attributes, such as timestamp attributes, event name attributes, resource attributes, etc. Afte...

example 1

[0095] Example 1 track σ 1 =

[0096] 1) Get σ 1 start event A with and add it to the sequence of empty trajectories σ f middle;

[0097] 2) Take out the next event B of event A, and calculate the mixed correlation degree Dep of event AB mixed (A,B)=0.80, which is greater than the mixture threshold of 0.5, so event B is a normal event (non-noise event), and it is added to the sequence σ f middle;

[0098] 3) Take out the next event C of event B, and calculate the mixed correlation degree Dep of event BC mixed (B,C)=0.75, which is greater than the mixture threshold of 0.5, so event C is a normal event (non-noise event), and it is added to the sequence σ f middle;

[0099] 4) Take out the next event D of event C, and calculate the mixed correlation degree Dep of event CD mixed (C,D)=0.85, which is greater than the mixture threshold of 0.5, so event D is a normal event (non-noise event), and it is added to the sequence σ f middle;

[0100] 5) Take out...

example 2

[0105] Example 2 track σ 2 =

[0106] 1) Get σ 2 start event A with and add it to the sequence of empty trajectories σ f middle;

[0107] 2) Take out the next event B of event A, and calculate the mixed correlation degree Dep of event AB mixed (A,B)=0.80, which is greater than the mixture threshold of 0.5, so event B is a normal event (non-noise event), and it is added to the sequence σ f middle;

[0108] 3) Take out the next event C of event B, and calculate the mixed correlation degree Dep of event BC mixed (B,C)=0.75, which is greater than the mixture threshold of 0.5, so event C is a normal event (non-noise event), and it is added to the sequence σ f middle;

[0109] 4) Take out the next event E of event C, and calculate the mixed correlation degree Dep of event CE mixed (C,E)=0.26, which is less than the mixture threshold of 0.5, so the event E is a noise event and will not be added to the sequence σ f Medium; use a penalty function to modify ...

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Abstract

The invention discloses a double-granularity noise log filtering method based on an incidence relation. According to the method, the mixed dependency is obtained through calculation based on the localdependency and the global dependency, and the fine-grained filtering of the noise events in logs and the coarse-grained filtering of the noise tracks can be achieved at the same time through the method. Compared with a traditional log filtering method, the log filtering method has the advantages that a double-granularity filtering mechanism is adopted, different filtering mechanisms are used fordifferent noise situations, and therefore an excellent filtering effect is achieved under the condition that the original log data is reserved as much as possible; 2, the filtered log file is used forprocess mining, so that the precision of a process discovery model can be greatly improved, and the understandability of the model is enhanced.

Description

technical field [0001] The invention relates to the field of process mining, in particular to a method for filtering double-granularity noise logs based on association relationships. Background technique [0002] Process mining aims to extract useful information from event logs recorded by process-aware information systems to help stakeholders understand the actual execution of processes. As an important part of process mining, process discovery is to build a process model that can reproduce the behavior of event logging. High-precision models can intuitively show the actual execution of business processes. [0003] In a business process management system, business process activities are executed according to a well-designed process model, and the execution of these activities will be recorded in logs to help stakeholders analyze and monitor the execution of the process. In real life, most business processes do not have a standardized process model, or as the business proc...

Claims

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Application Information

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IPC IPC(8): G06F11/30
CPCG06F11/3072
Inventor 孙笑笑俞东进侯文杰潘建梁
Owner HANGZHOU DIANZI UNIV
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