Towards a reactive system for managing big trajectory data

Spatio-temporal events often describe the movements of an object in terms of space, time, and potential other attributes. Significant knowledge can be inferred by analysing them, either individually or atomically in form of trajectories. The trajectories can abstract additional properties and lead to deeper value. Moreover, external contextual information can be attributed to them to change their structure and lead to different perspectives. Because of this potentially valuable knowledge, nowadays indoor and outdoor tracking devices are used everywhere; generating countless events instantaneously. However, the extraction of knowledge from such heterogeneous, massive data is not a trivial task. In other terms, there is a need for a sophisticated system that is efficient in terms of distributed computing, failure handling, responsiveness, and abstraction. To answer this need, our study incorporates a fully fledged, reactive system for big trajectory data management. The system is unique of its kind because it is actor-based and features responsiveness, resiliency, and elasticity. Furthermore, our system is implemented using Scala; hence, we have the expressive power of both the Object-Oriented (OO) and Functional Programming (FP) paradigms. Allowing us to reach a higher level of abstraction to be able to process any trajectory type. The scope of this paper is to detail our system and discuss elasticity, routing strategies, load balancing, and our proper fault-tolerance mechanism. To fulfill this study, we consider the Geolife project’s GPS trajectory dataset.