Jens Leveling, Luise Weickhmann, Christian Nissen, Christopher Kirsch
{"title":"Event-driven Architecture for Sensor Data Integration for Logistics Services","authors":"Jens Leveling, Luise Weickhmann, Christian Nissen, Christopher Kirsch","doi":"10.1109/IEEM.2018.8607460","DOIUrl":null,"url":null,"abstract":"Sensor data offers a massive potential for the logistics sector. To achieve an optimal, effective and productive supply chain, operators and manufacturers are challenged to use this information and extract value from it. They have to comply with the main task of efficiently managing logistics processes as well as fulfilling requirements and guidelines. To do so, it is necessary to monitor all processes and understand exceptions and anomalies. Sensor and Internet of Things (IoT) data is the key for these tasks. Currently, the data is available but not (sufficiently) used. The heterogeneity of sensor data is a major obstacle for the usage. Therefore, we present an architecture, which addresses these challenges by integrating heterogenic data in well-formed data sets.","PeriodicalId":119238,"journal":{"name":"2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IEEM.2018.8607460","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Sensor data offers a massive potential for the logistics sector. To achieve an optimal, effective and productive supply chain, operators and manufacturers are challenged to use this information and extract value from it. They have to comply with the main task of efficiently managing logistics processes as well as fulfilling requirements and guidelines. To do so, it is necessary to monitor all processes and understand exceptions and anomalies. Sensor and Internet of Things (IoT) data is the key for these tasks. Currently, the data is available but not (sufficiently) used. The heterogeneity of sensor data is a major obstacle for the usage. Therefore, we present an architecture, which addresses these challenges by integrating heterogenic data in well-formed data sets.