{"title":"利用直觉模糊集指导知识密集型流程实现目标","authors":"","doi":"10.1016/j.eswa.2024.125417","DOIUrl":null,"url":null,"abstract":"<div><div>Throughout the execution of a knowledge-intensive process (KiP), knowledge workers need to make critical decisions such as skipping a task or canceling a process instance. These decisions significantly impact the efficiency and effectiveness of KiP execution and should, therefore, be made in a well-informed manner. When historical data, such as event logs, is available, it can be leveraged to support knowledge workers in making these decisions. However, KiPs often lack useful historical data, as each KiP instance is unique and hardly repeatable. To address this issue, this paper proposes the novel concept of <em>potential goal achievement, i.e., the extent to which a goal can be achieved at the end of the process, considering the collected (but incomplete) data</em>, to support knowledge workers in efficiently executing KiPs. An approach based on Intuitionistic Fuzzy Sets (IFSs) is introduced to calculate the potential goal achievement without relying on historical data. The use of potential goal achievement in supporting knowledge workers’ decisions is demonstrated, and the effectiveness of the approach is evaluated through simulations. The results demonstrate that modeling and calculating potential goal achievement support knowledge workers in achieving goals more efficiently.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":null,"pages":null},"PeriodicalIF":7.5000,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Guidance for goal achievement in knowledge-intensive processes using intuitionistic fuzzy sets\",\"authors\":\"\",\"doi\":\"10.1016/j.eswa.2024.125417\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Throughout the execution of a knowledge-intensive process (KiP), knowledge workers need to make critical decisions such as skipping a task or canceling a process instance. These decisions significantly impact the efficiency and effectiveness of KiP execution and should, therefore, be made in a well-informed manner. When historical data, such as event logs, is available, it can be leveraged to support knowledge workers in making these decisions. However, KiPs often lack useful historical data, as each KiP instance is unique and hardly repeatable. To address this issue, this paper proposes the novel concept of <em>potential goal achievement, i.e., the extent to which a goal can be achieved at the end of the process, considering the collected (but incomplete) data</em>, to support knowledge workers in efficiently executing KiPs. An approach based on Intuitionistic Fuzzy Sets (IFSs) is introduced to calculate the potential goal achievement without relying on historical data. The use of potential goal achievement in supporting knowledge workers’ decisions is demonstrated, and the effectiveness of the approach is evaluated through simulations. The results demonstrate that modeling and calculating potential goal achievement support knowledge workers in achieving goals more efficiently.</div></div>\",\"PeriodicalId\":50461,\"journal\":{\"name\":\"Expert Systems with Applications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.5000,\"publicationDate\":\"2024-09-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Expert Systems with Applications\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S095741742402284X\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Expert Systems with Applications","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S095741742402284X","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Guidance for goal achievement in knowledge-intensive processes using intuitionistic fuzzy sets
Throughout the execution of a knowledge-intensive process (KiP), knowledge workers need to make critical decisions such as skipping a task or canceling a process instance. These decisions significantly impact the efficiency and effectiveness of KiP execution and should, therefore, be made in a well-informed manner. When historical data, such as event logs, is available, it can be leveraged to support knowledge workers in making these decisions. However, KiPs often lack useful historical data, as each KiP instance is unique and hardly repeatable. To address this issue, this paper proposes the novel concept of potential goal achievement, i.e., the extent to which a goal can be achieved at the end of the process, considering the collected (but incomplete) data, to support knowledge workers in efficiently executing KiPs. An approach based on Intuitionistic Fuzzy Sets (IFSs) is introduced to calculate the potential goal achievement without relying on historical data. The use of potential goal achievement in supporting knowledge workers’ decisions is demonstrated, and the effectiveness of the approach is evaluated through simulations. The results demonstrate that modeling and calculating potential goal achievement support knowledge workers in achieving goals more efficiently.
期刊介绍:
Expert Systems With Applications is an international journal dedicated to the exchange of information on expert and intelligent systems used globally in industry, government, and universities. The journal emphasizes original papers covering the design, development, testing, implementation, and management of these systems, offering practical guidelines. It spans various sectors such as finance, engineering, marketing, law, project management, information management, medicine, and more. The journal also welcomes papers on multi-agent systems, knowledge management, neural networks, knowledge discovery, data mining, and other related areas, excluding applications to military/defense systems.