We present a system for Complex Event Recognition (CER) based on automata. While multiple such systems have been described in the literature, they typically suffer from a lack of clear and denotational semantics, a limitation which often leads to confusion with respect to their expressive power. In order to address this issue, our system is based on an automaton model which is a combination of symbolic and register automata. We extend previous work on these types of automata, in order to construct a formalism with clear semantics and a corresponding automaton model whose properties can be formally investigated. We call such automata Symbolic Register Transducers (SRT). The distinctive feature of SRT , compared to previous automaton models used in CER, is that they can encode patterns relating multiple input events from an event stream, without sacrificing rigor and clarity. We show how SRT can be used in CER in order to detect patterns upon streams of events, using our framework that provides declarative and compositional semantics. We also compare our SRT -based CER engine against other state-of-the-art CER systems and show that it is both more expressive and more efficient.

Proactive streaming analytics continuously extract real-time business value from massive data that stream in data centers or clouds. This requires (a) to process the data while they are still in motion; (b) to scale the processing to multiple machines, often over various, dispersed computer clusters, with diverse Big Data technologies; and (c) to forecast complex business events for proactive decision-making. Combining the necessary facilities for proactive streaming analytics at scale entails: (I) deep knowledge of the relevant state-of-the-art, (II) cherry-picking cutting edge research outcomes based on desired features and with the prospect of building interoperable components, and (III) building components and deploying them into a holistic architecture within a real-world platform. In this tutorial, we drive the audience through the whole journey from (I) to (III), delivering cutting edge research into a commercial analytics platform, for which we provide a hands-on experience.

We present a Maritime Situational Awareness (MSA) framework for detecting and forecasting maritime events (e.g., illegal fishing) over streams of Big maritime Data. The architecture of the MSA framework relies on the following state-of-the-art components: (i) the Maritime Event Detector which uses data-driven distributed techniques deployed on a computer cluster to detect maritime events of interest in an online, real-time fashion, (ii) the Complex Event Forecasting module, which implements state-of-the-art distributed Complex Event Forecasting techniques for maritime data, (iii) the Synopses Data Engine component, that creates synopses of maritime data improving the scalability of the framework and (iv) the streaming extension of a popular data science platform, namely RapidMiner Studio, that integrates all the above, allowing users to graphically design and rapidly implement Big Data analytics pipelines which can be deployed transparently on top of distributed architectures.

Complex Event Processing (CEP) 's main purpose is recognizing interesting phenomena upon streams of data. So its only natural that it would find applications in the maritime domain, where detecting vessel activity plays an important role in monitoring movement at sea. In this study we briefly examine the field of Complex Event Processing; we present two CEP implementations, one based on machine learning techniques and a rule-based system modeled with Event Calculus. Finally, we evaluate their ability in modeling activities that involve multiple vessels, by comparing their results on real-life examples.

Maritime monitoring is a typical Big Data problem where hundreds of thousands of vessels across the globe transmit messages about their location, speed and other information. We have developed a system for online vessel tracking that performs, as a first step, a high-rate but accurate trajectory compression. Subsequently, the compressed trajectories are analyzed by a complex event recognition engine, promptly reporting alerts to maritime authorities. To deal with realistic maritime event patterns, we seamlessly integrated spatial and temporal reasoning for online event recognition. The system is evaluated on real data from the Greek seas.