IBRIDIA: A hybrid solution for processing big logistics data - AlShaer, Mohammed; Taher, Yehia; Haque, Rafiqul; Hacid, Mohand-Saïd; Dbouk, Mohamed (Future generation computer systems, 2019)
Internet of Things (IoT) is leading to a paradigm shift within the logistics industry. Logistics services providers use sensor technologies such as GPS or telemetry to track and manage their shipment processes. Additionally, they use external data that contain critical information about events such as traffic, accidents, and natural disasters. Correlating data from different sensors and social media and performing analysis in real-time provide opportunities to predict events and prevent unexpected delivery delay at run-time. However, collecting and processing data from heterogeneous sources foster problems due to the variety and velocity of data. In addition, processing data in real-time is heavily challenging that it cannot be dealt with using conventional logistics information systems. In this paper, we present a hybrid framework for processing massive volume of data in batch style and real-time. Our framework is built upon Johnson’s hierarchical clustering (HCL) algorithm which produces a dendrogram that represents different clusters of data objects.