Extending Event-Driven Architecture for Proactive Systems

ABSTRACT Proactive Event-Driven Computing is a new paradigm, in which a decision is not made due to explicit users' requests nor is it made as a response to past events. Rather, the decision is autonomously triggered by forecasting future states. Proactive event-driven computing requires a departure from current event-driven architectures to ones capable of handling uncertainty and future events, and real-time decision making. We present a proactive event-driven architecture for Scalable Proactive Event-Driven Decision-making (SPEEDD), which combines these capabilities. The proposed architecture is composed of three main components: complex event processing, real-time decision making, and visualization. This architecture is instantiated by a real use case from the traffic management domain. In the future, the results of actual implementations of the use case will help us revise and refine the proposed architecture

A Probabilistic Logic Programming Event Calculus

We present a system for recognising human activity given a symbolic representation of video content. The input of our system is a set of time-stamped short-term activities (STA) detected on video frames. The output is a set of recognised long-term activities (LTA), which are pre-defined temporal combinations of STA. The constraints on the STA that, if satisfied, lead to the recognition of a LTA, have been expressed using a dialect of the Event Calculus. In order to handle the uncertainty that naturally occurs in human activity recognition, we adapted this dialect to a state-of-the-art probabilistic logic programming framework. We present a detailed evaluation and comparison of the crisp and probabilistic approaches through experimentation on a benchmark dataset of human surveillance videos.Comment: Accepted for publication in the Theory and Practice of Logic Programming (TPLP) journa

Αναγνώριση συμβάντων σε απρόβλεπτα και μερικώς παρατηρήσιμα περιβάλλοντα

Symbolic event recognition systems have been successfully applied to a variety of application domains, extracting useful information in the form of events, allowing experts or other systems to monitor and respond when significant events are recognised. In a typical event recognition application, however, these systems often have to deal with a significant amount of uncertainty. In this thesis, we address the issue of uncertainty in logic-based event recognition by extending the Event Calculus with probabilistic reasoning. The temporal semantics of the Event Calculus introduce a number of challenges for the proposed model. We show how and under what assumptions we can overcome these problems. Additionally, we study how probabilistic modelling changes the behaviour of the formalism, affecting its key property, the inertia of fluents. Furthermore, we demonstrate the advantages of the probabilistic Event Calculus through examples and experiments in the domain of activity recognition, using a publicly available dataset for video surveillance.Τα συμβολικά συστήματα αναγνώρισης γεγονότων έχουν χρησιμοποιηθεί επιτυχώς σε μία ποικιλία εφαρμογών. Τα συστήματα αυτά εξάγουν χρήσιμη πληροφορία υπό την μορφή γεγονότων, που δίνουν τη δυνατότητα σε ειδικούς, ή σε άλλα συστήματα, να παρακολουθούν και να ανταποκρίνονται στην παρουσία γεγονότων σημαντικού ενδιαφέροντος. Ωστόσο είναι πολύ συχνό σε μία τυπική εφαρμογή αναγνώρισης γεγονότων να παρουσιάζεται σημαντική αβεβαιότητα. Σε αυτή την διατριβή, εστιάζουμε στα προβλήματα που προκύπτουν από την παρουσία της αβεβαιότητας στην αναγνώριση γεγονότων. Επεκτείνουμε ένα φορμαλισμό λογικής άλγεβρας γεγονότων με πιθανοτικό συμπερασμό. Η χρονικές σχέσεις του λογικού φορμαλισμού εισάγουν ένα πλήθος δυσκολιών στα πιθανοτικά μοντέλα και παρουσιάζουμε τον τρόπο και τις προϋποθέσεις με τις οποίες μπορούμε να ξεπεράσουμε αυτές τις δυσκολίες. Παράλληλα, μελετάμε τον τρόπο με τον οποίο η πιθανοτική μοντελοποίηση επηρεάζει την συμπεριφορά του φορμαλισμού. Επιπλέον, παρουσιάζουμε τις δυνατότητες και τα προτερήματα των πιθανοτικών μεθόδων που αναπτύξαμε με εκτενή πειραματισμό και ανάλυση στον τομέα της αναγνώρισης συμπεριφορών από βίντεο

Logic-based representation, reasoning and machine learning for event recognition

Today’s organisations require techniques for automated transformation of the large data volumes they collect during their operations into operational knowledge. This requirement may be addressed by employing event recognition systems that detect activities/events of special significance within an organisation, given streams of ‘low-level’ information that is very difficult to be utilised by humans. Numerous event recognition systems have been proposed in the literature. Recognition systems with a logic-based representation of event structures, in particular, have been attracting considerable attention because, among others, they exhibit a formal, declarative semantics, they haven proven to be efficient and scalable, and they are supported by machine learning tools automating the construction and refinement of event structures. In this paper we review representative approaches of logic-based event recognition, and discuss open research issues of this field

Logic-Based Event Recognition

Today’s organisations require techniques for automated transformation of their large data volumes into operational knowledge. This requirement may be addressed by employing event recognition systems that detect events/activities of special significance within an organisation, given streams of ‘low-level ’ information that is very difficult to be utilised by humans. Consider, for example, the recognition of attacks on nodes of a computer network given the TCP/IP messages, the recognition of suspicious trader behaviour given the transactions in a financial market, and the recognition of whale songs given a symbolic representation of whale sounds. Various event recognition systems have been proposed in the literature. Recognition systems with a logic-based representation of event structures, in particular, have been attracting considerable attention, because, among others, they exhibit a formal, declarative semantics, they have proven to be efficient and scalable, and they are supported by machine learning tools automating the construction and refinement of event structures. In this paper we review representative approaches of logic-based event recognition and discuss open research issues of this field. We illustrate the reviewed approaches with the use of a real-world case study: event recognition for city transport management