The outbreak of the COVID-19 pandemic led policy makers and public health officials around the world to implement non-pharmaceutical interventions to suppress the spread of the virus. The goal was to reduce human mobility and social contacts, considered as the main factors of virus diffusion. However, these containment measures needed to be revised on a frequent basis to avoid serious economic and social costs. Moreover, spatial disparities needed to be taken into consideration, since the behavior of the virus was different according to the geographical context. Therefore, a frequent update on the short-term forecasts of the epidemic course, which considered spatial heterogeneities, was crucial for planning appropriate mitigation strategies. In this article, we present a simple epidemiological model based on Cellular Automata, that takes into account human mobility and produces short-term forecasts of daily virus infections. Cellular Automata allow the discretization of time and space and thus, the spatio-temporal dynamics of the disease can be explored at the desired scale. We apply our model on real daily infection and mobility data from Spain and show that it is reliable in predicting the short-term daily infections trajectory both at the country level and at the regional level of Autonomous Communities. Furthermore, compared against four state-of-the-art methods, the proposed method achieves comparable forecasting performance with significantly lower computational resources.

Complex Event Recognition (CER) systems receive as input a stream of time-stamped events and identify situations of interest that satisfy a given pattern. Streaming environments are characterized by the high rate and volume of input data, and thus, scalability is of crucial importance. At the same time, noise and uncertainty are ubiquitous in temporal data, and not considering them, leads to erroneous detections. To confront these challenges, we present a tensor-based formalization of the Event Calculus (EC) for probabilistic inference, and demonstrate the scalability of our approach with the use of CER datasets from two real-world application domains. Moreover, we demonstrate the benefits of our approach, in terms of processing time, by comparing it against a probabilistic logic programming implementation of EC.

We present a system for online, incremental composite event recognition. In streaming environments, the usual case is for data to arrive with a (variable) delay from, and to be revised by, the underlying sources. We propose RTEC_inc, an incremental version of RTEC, a composite event recognition engine with formal, declarative semantics, that has been shown to scale to several real-world data streams. RTEC deals with delayed arrival and revision of events by computing all queries from scratch. This is often inefficient since it results in redundant computations. Instead, RTEC_inc deals with delays and revisions in a more efficient way, by updating only the affected queries. We compare RTEC_inc and RTEC experimentally using real-world and synthetic datasets. The results are compatible with our complexity analysis and show that RTEC_inc outperforms RTEC in many practical cases.

We present a system for online, incremental composite event recognition. In streaming environments, the usual case is for data to arrive with a (variable) delay from, and to be revised by, the underlying sources. We propose RTECinc, an incremental version of RTEC, a composite event recognition engine with formal, declarative semantics, that has been shown to scale to several real-world data streams. RTEC deals with delayed arrival and revision of events by computing all queries from scratch. This is often inefficient since it results in redundant computations. Instead, RTECinc deals with delays and revisions in a more efficient way, by updating only the affected queries. We examine RTECinc theoretically, presenting a complexity analysis, and show the conditions in which it outperforms RTEC. Moreover, we compare RTECinc and RTEC experimentally using real-world and synthetic datasets. The results are compatible with our theoretical analysis and show that RTECinc outperforms RTEC in many practical cases.

Abstract We present a system for online composite event recognition over streaming positions of commercial vehicles. Our system employs a data enrichment module, augmenting the mobility data with external information, such as weather data and proximity to points of interest. In addition, the composite event recognition module, based on a highly optimised logic programming implementation of the Event Calculus, consumes the enriched data and identifies activities that are beneficial in fleet management applications. We evaluate our system on large, real-world data from commercial vehicles, and illustrate its efficiency.