{"title":"应用数据挖掘技术发现业务流程环境中的kpi关系","authors":"Emna Ammar El Hadj Amour, Sonia Ayachi Ghannouchi","doi":"10.1109/PDCAT.2017.00045","DOIUrl":null,"url":null,"abstract":"Organizations need to continually improve and review their critical business processes. In addition, it is crucial not only to track the business process (BP) behavior and to derive key performance indicators (KPIs) but also to understand all necessary concepts and incorporate domain knowledge of the field. The purpose of this paper is to gain a deeper understanding of the interrelationships between all concepts and performance measurement raw data to extract their real meaning. In order to meet these challenges, first, we explore several qualitative and quantitative indicators for measuring the performance of BPs. Second, we develop a new ontology for the representation of these performance indicators. Then, we are based on data mining techniques to extract the most important information from data measurement and to discover all necessary relationships between indicators.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Applying Data Mining Techniques to Discover KPIs Relationships in Business Process Context\",\"authors\":\"Emna Ammar El Hadj Amour, Sonia Ayachi Ghannouchi\",\"doi\":\"10.1109/PDCAT.2017.00045\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Organizations need to continually improve and review their critical business processes. In addition, it is crucial not only to track the business process (BP) behavior and to derive key performance indicators (KPIs) but also to understand all necessary concepts and incorporate domain knowledge of the field. The purpose of this paper is to gain a deeper understanding of the interrelationships between all concepts and performance measurement raw data to extract their real meaning. In order to meet these challenges, first, we explore several qualitative and quantitative indicators for measuring the performance of BPs. Second, we develop a new ontology for the representation of these performance indicators. Then, we are based on data mining techniques to extract the most important information from data measurement and to discover all necessary relationships between indicators.\",\"PeriodicalId\":119197,\"journal\":{\"name\":\"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)\",\"volume\":\"25 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PDCAT.2017.00045\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PDCAT.2017.00045","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Applying Data Mining Techniques to Discover KPIs Relationships in Business Process Context
Organizations need to continually improve and review their critical business processes. In addition, it is crucial not only to track the business process (BP) behavior and to derive key performance indicators (KPIs) but also to understand all necessary concepts and incorporate domain knowledge of the field. The purpose of this paper is to gain a deeper understanding of the interrelationships between all concepts and performance measurement raw data to extract their real meaning. In order to meet these challenges, first, we explore several qualitative and quantitative indicators for measuring the performance of BPs. Second, we develop a new ontology for the representation of these performance indicators. Then, we are based on data mining techniques to extract the most important information from data measurement and to discover all necessary relationships between indicators.