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.

Abstract Computational systems and methods are often being used in biological research, including the understanding of cancer and the development of treatments. Simulations of tumor growth and its response to different drugs are of particular importance, but also challenging complexity. The main challenges are first to calibrate the simulators so as to reproduce real‐world cases, and second, to search for specific values of the parameter space concerning effective drug treatments. In this work, we combine a multi‐scale simulator for tumor cell growth and a genetic algorithm (GA) as a heuristic search method for finding good parameter configurations in reasonable time. The two modules are integrated into a single workflow that can be executed in parallel on high performance computing infrastructures. In effect, the GA is used to calibrate the simulator, and then to explore different drug delivery schemes. Among these schemes, we aim to find those that minimize tumor cell size and the probability of emergence of drug resistant cells in the future. Experimental results illustrate the effectiveness and computational efficiency of the approach.

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.

Complex Event Processing (CEP) systems have appeared in abundance during the last two decades. Their purpose is to detect in real–time interesting patterns upon a stream of events and to inform an analyst for the occurrence of such patterns in a timely manner. However, there is a lack of methods for forecasting when a pattern might occur before such an occurrence is actually detected by a CEP engine. We present Wayeb, a tool that attempts to address the issue of Complex Event Forecasting. Wayeb employs symbolic automata as a computational model for pattern detection and Markov chains for deriving a probabilistic description of a symbolic automaton.

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.