Presence monitoring or intrusion detection in a location/area are examples of decision-support applications. Decision-support applications are applications where monitoring is used to collect (heterogeneous) data and create situational awareness, which further requires decisions and/or actions. As such, decision-support software consists of different interconnected components with very diverse roles, whose communication and synchronization are essential for the application functionality and performance. Despite this complexity, software design for decision-support is often driven by short-term functional requirements and only supported by designers’ previous experience. In the current non-systematic approach, mistakes can be easily made, and can be very difficult to repair.

Many situations in the security domain require decision-making based on complex data, i.e., many variables which need to be taken into account before adequate decisions can be made. For example, in a surveillance scenario, the size and complexity of the area of interest, the mix of objects, and the unexpected behavior of suspects are just a few examples of complex variables to be analyzed in the process. Existing decision support systems provide some analysis, but are typically limited in the complexity they can handle. Therefore, users end up with simplified models which often suffer in the accuracy of their decisions and, ultimately, may lead to incorrect decisions. In this work, we present a framework that can scale to cope with the complexity and time requirements of real-world scenarios, while remaining flexible to handle the ad-hoc adaptation to the situation. We discuss the challenges and solutions for such a scalable and flexible system, and validate it using a target tracking scenario in urban environments of different sizes.

Due to increasingly large datasets, graph analytics - traversals, all-pairs shortest path computations, centrality measures, etc. - are becoming the focus of high-performance computing (HPC). Because HPC is currently dominated by many-core architectures (both CPUs and GPUs), new graph processing solutions have to be defined to efficiently use such computing resources. Prior work focuses on platform-specific performance studies and on platform-specific algorithm development, successfully proving that algorithms highly tuned to GPUs or multi-core CPUs can provide high performance graph analytics. However, the portability of such algorithms remains an important concern for many users, especially the many companies without the resources to invest in HPC or concerned about lock-in in single-use parallel techniques.