分解过程模型线性时间逻辑公式的确定

Maryamah, R. Sarno, Afina Lina Nurlaili
{"title":"分解过程模型线性时间逻辑公式的确定","authors":"Maryamah, R. Sarno, Afina Lina Nurlaili","doi":"10.1109/ICOIACT.2018.8350722","DOIUrl":null,"url":null,"abstract":"Process discovery is a process to observe behaviour in the event log and to build a model for the next process. In addition, it is an important process because its strength to predict the time, and cost. After constructing the model, the model has to be set into some parts by using decomposed process algorithm. So that, the result will be easier to be analyzed. A decomposed process can implementation inductive miner algorithm. However, decomposed using inductive miner have limited relation in process. To overcome this problem, this paper proposed decomposed model by using Linear Temporal Logic (LTL) to find the rule and build the process model automatically without constructing from the first step and also have many notations to formalize relation of activity. In addition, LTL is a method to build the rule and check the workflow of the process logs whether the logs have the parallel process. So that, by using the proposed method LTL will be used for getting a process model in less time with average time 1 second and more accurate result.","PeriodicalId":6660,"journal":{"name":"2018 International Conference on Information and Communications Technology (ICOIACT)","volume":"25 1","pages":"466-470"},"PeriodicalIF":0.0000,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Determining linear temporal logic formula for decomposed process model\",\"authors\":\"Maryamah, R. Sarno, Afina Lina Nurlaili\",\"doi\":\"10.1109/ICOIACT.2018.8350722\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Process discovery is a process to observe behaviour in the event log and to build a model for the next process. In addition, it is an important process because its strength to predict the time, and cost. After constructing the model, the model has to be set into some parts by using decomposed process algorithm. So that, the result will be easier to be analyzed. A decomposed process can implementation inductive miner algorithm. However, decomposed using inductive miner have limited relation in process. To overcome this problem, this paper proposed decomposed model by using Linear Temporal Logic (LTL) to find the rule and build the process model automatically without constructing from the first step and also have many notations to formalize relation of activity. In addition, LTL is a method to build the rule and check the workflow of the process logs whether the logs have the parallel process. So that, by using the proposed method LTL will be used for getting a process model in less time with average time 1 second and more accurate result.\",\"PeriodicalId\":6660,\"journal\":{\"name\":\"2018 International Conference on Information and Communications Technology (ICOIACT)\",\"volume\":\"25 1\",\"pages\":\"466-470\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 International Conference on Information and Communications Technology (ICOIACT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICOIACT.2018.8350722\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 International Conference on Information and Communications Technology (ICOIACT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICOIACT.2018.8350722","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

流程发现是一个过程,用于观察事件日志中的行为,并为下一个流程构建模型。此外,它是一个重要的过程,因为它的强度预测的时间,和成本。模型构建完成后,需要使用分解过程算法将模型分解成若干部分。这样,结果就更容易分析了。分解过程可以实现归纳挖掘算法。然而,用归纳挖掘法进行分解在过程中存在一定的局限性。为了克服这一问题,本文提出了采用线性时间逻辑(LTL)分解模型,无需从头构建,即可自动发现规则并建立过程模型,并具有许多形式化活动关系的符号。另外,LTL是一种建立规则,检查流程日志的工作流是否具有并行进程的方法。采用该方法,可以在较短的时间内(平均时间为1秒)得到一个过程模型,结果更加准确。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Determining linear temporal logic formula for decomposed process model
Process discovery is a process to observe behaviour in the event log and to build a model for the next process. In addition, it is an important process because its strength to predict the time, and cost. After constructing the model, the model has to be set into some parts by using decomposed process algorithm. So that, the result will be easier to be analyzed. A decomposed process can implementation inductive miner algorithm. However, decomposed using inductive miner have limited relation in process. To overcome this problem, this paper proposed decomposed model by using Linear Temporal Logic (LTL) to find the rule and build the process model automatically without constructing from the first step and also have many notations to formalize relation of activity. In addition, LTL is a method to build the rule and check the workflow of the process logs whether the logs have the parallel process. So that, by using the proposed method LTL will be used for getting a process model in less time with average time 1 second and more accurate result.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信