Ruben Mayer

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Reliability is of critical importance to many applications involving distributed event processing systems. Especially the use of stateful operators makes it challenging to provide efficient recovery from failures and to ensure consistent event streams. Even during failure-free execution, state-of-the-art methods for achieving reliability incur significant(More)
The tremendous number of sensors and smart objects being deployed in the Internet of Things (IoT) pose the potential for IT systems to detect and react to live-situations. For using this hidden potential, complex event processing (CEP) systems offer means to efficiently detect event patterns (complex events) in the sensor streams and therefore, help in(More)
Complex Event Processing (CEP) systems enable applications to react to live-situations by detecting event patterns (complex events) in data streams. With the increasing number of data sources and the increasing volume at which data is produced, parallelization of event detection is becoming of tremendous importance to limit the time events need to be(More)
In recent years, the proliferation of highly dynamic graph-structured data streams fueled the demand for real-time data analytics. For instance, detecting recent trends in social networks enables new applications in areas such as disaster detection, business analytics or health-care. Parallel Complex Event Processing has evolved as the paradigm of choice to(More)
A recent trend in communication networks---sometimes referred to as fog computing---offers to execute computational tasks close to the access points of the networks. This enables mobile Complex Event Processing (CEP) middlewares to significantly reduce end-to-end latencies and bandwidth usage by migrating operators when event sources and consumers change(More)
Social sensing services use humans as sensor carriers, sensor operators and sensors themselves in order to provide situation-awareness to applications. This promises to provide a multitude of benefits to the users, for example in the management of natural disasters or in community empowerment. However, current social sensing services depend on Internet(More)
Distributed Complex Event Processing has emerged as a well-established paradigm to detect situations of interest from basic sensor streams, building an operator graph between sensors and applications. In order to detect event patterns that correspond to situations of interest, each operator correlates events on its incoming streams according to a sliding(More)
Today, massive amounts of streaming data from smart devices need to be analyzed automatically to realize the Internet of Things. The Complex Event Processing (CEP) paradigm promises low-latency pattern detection on event streams. However, CEP systems need to be extended with Machine Learning (ML) capabilities such as online training and inference in order(More)
New generations of cloud applications are increasingly complex and pose lower latency requirements. The latter is forcing the industry to reduce network latency by adding computation nodes near the edge of the network, also known as Fog Computing. To utilize the Fog nodes efficiently, the dynamic placement and migration of application components must be(More)