基于RankClus算法的機(jī)場(chǎng)流程日志活動(dòng)挖掘

徐濤; 孟野; 盧敏

doi:10.11999/JEIT151137

基于RankClus算法的機(jī)場(chǎng)流程日志活動(dòng)挖掘

doi: 10.11999/JEIT151137

徐濤¹,
孟野²,
盧敏¹

2.
(中國(guó)民航大學(xué)計(jì)算機(jī)科學(xué)與技術(shù)學(xué)院天津 300300) ②(中國(guó)民航信息技術(shù)科研基地天津 300300)

基金項(xiàng)目:

國(guó)家自然科學(xué)基金(61502499)，中國(guó)民航科技創(chuàng)新引導(dǎo)資金項(xiàng)目重大專項(xiàng)(MHRD20140105)，中央高?？蒲袠I(yè)務(wù)費(fèi)專項(xiàng)資金(3122013C005, 3122014D032, 3122015D015)，中國(guó)民航大學(xué)科研基金(2013QD18X)，中國(guó)民航信息技術(shù)科研基地開放課題基金(CAAC-ITRB-201401)

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出版歷程
- 收稿日期: 2015-10-10
- 修回日期: 2016-04-15
- 刊出日期: 2016-08-19

Activity Mining for Airport Event Logs Based on RankClus Algorithm

XU Tao¹,
MENG Ye²,
LU Min ¹

2.
(College of Computer Science and Technology, Civil Aviation University of China, Tianjin 300300, China)

Funds:

The National Natural Science Foundation of China (61502499), The Civil Aviation Key Technologies RD Program of China (MHRD20140105), The Fundamental Research Funds for the Central Universities of China (3122013C005, 3122014D032, 3122015D015), The Scientific Research Foundation from Civil Aviation University of China (2013QD18X), The Open Project Foundation of Information Technology Research Base of Civil Aviation Administration of China (CAAC-ITRB-201401)

摘要

摘要: 流程挖掘技術(shù)可以提取機(jī)場(chǎng)流程日志中的有用信息用于流程分析。但機(jī)場(chǎng)流程日志處于細(xì)節(jié)化的低抽象層次，不符合分析者的預(yù)期。對(duì)機(jī)場(chǎng)流程日志挖掘得到的流程模型呈現(xiàn)意面狀的復(fù)雜結(jié)構(gòu)，流程模型的含義難于理解。解決該問(wèn)題的一種方法是通過(guò)活動(dòng)挖掘，將低抽象層次活動(dòng)聚類為流程模型中表征高抽象層次活動(dòng)的活動(dòng)類簇。為此提出了一種基于RankClus算法的活動(dòng)挖掘方法，將機(jī)場(chǎng)流程日志的活動(dòng)聚類與活動(dòng)排序評(píng)分計(jì)算相結(jié)合，從而構(gòu)建更易理解的活動(dòng)聚類流程模型。實(shí)驗(yàn)結(jié)果表明，RankClus活動(dòng)聚類流程模型的日志回放一致性與原生日志流程模型大致相當(dāng)，但在結(jié)構(gòu)復(fù)雜度上要顯著低于原生日志流程模型。
- 流程挖掘 /
- 活動(dòng)挖掘 /
- RankClus /
- 蹤跡聚類
Abstract: Process mining is a technology which can extract non-trivial and useful information from airport event logs. However, the airport event logs are always on a detailed level of abstraction, which may not be in line with the expected abstract level of an analyst. Process models generated by these event logs are always spaghetti-like and too hard to comprehend. An approach to overcome this issue is to group low-level events into clusters, which represent the execution of a higher-level activity in the process model. Therefore, this paper presents a new activity mining method which is based on RankClus algorithm to generate activity clusters integrated with ranking. On this basis, the activity-clustered model which is easier to comprehend can be constructed. The experiment results show that this activity-clustered model, which shares a similar level of conformance with the meta model, is significantly less complex.
- Process mining /
- Activity mining /
- RankClus /
- Trace clustering

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