In this thesis, we developed and experimented two data processing solutions: SANA and IBRIDIA. SANA is built on Multinomial Naïve Bayes classifier whereas IBRIDIA relies on Johnson's hierarchical clustering (HCL) algorithm which is hybrid technology that enables data collection and processing in batch style and realtime.  SANA is a service-based solution which deals with unstructured data. It serves as a multi-purpose system to extract the relevant events including the context of the event (such as place, location, time, etc.). In addition, it can be used to perform text analysis over the targeted events. IBRIDIA was designed to process unknown data stemming from external sources and cluster them on-the-fly in order to gain knowledge/understanding of data which assist in extracting events which may lead to delivery delay. According to our experiments, both of these approaches show a unique ability to process logistics data. However, SANA is found more promising since the underlying technology (Naïve Bayes classifier) out-performed IBRIDIA from performance measuring perspectives. It is clearly said that SANA was meant to generate a graph knowledge from the events collected immediately in realtime without any need to wait, thus reaching maximum benefit from these events. Whereas, IBRIDIA have an important influence within the logistics domain for identifying the most influential category of events that are affecting the delivery. Unfortunately, in IBRIRDIA, we should wait for a minimum number of events to arrive and always we have a cold start. Due to the fact that we are interested in re-optimizing the route on the fly, we adopted SANA as our data processing framework. We implemented a route optimization application to demonstrate how our solution is used in extracting information of events that may lead to delivery delay and how the routes can be optimized to prevent the delay.