Learn More
Event processing systems in general and data stream processing systems in particular focus on processing of queries over unbounded event streams. The goal of the DEBS 2014 Grand Challenge is to provide a specific problem, originating from the domain of energy data management, which can be leveraged by both commercial and academic event processing systems.(More)
The DEBS Grand Challenge is a series of challenges which address problems in event stream processing. The focus of the Grand Challenge in 2016 is on processing of data streams that originate from social networks. Hence, the data represents an evolving graph structure. With this challenge we take up the general scenario and data source from the 2014 SIGMOD(More)
The ACM DEBS 2017 Grand Challenge is the seventh in a series of challenges which seek to provide a common ground and evaluation criteria for a competition aimed at both research and industrial event-based systems. The focus of the 2017 Grand Challenge is on the analysis of the RDF streaming data generated by digital and analogue sensors embedded within(More)
The goal of the DEBS Grand Challenge series is to contribute to the Event Processing Grand Challenge, that serves as a common goal and mechanism for coordinating research focusing on event processing. DEBS Grand Challenge series provides a common ground and evaluation criteria for a competition aimed at both research and industrial event-based systems. The(More)
In this tutorial we present the results of recent research about the cloud enablement of data streaming systems. We illustrate, based on both industrial as well as academic prototypes, new emerging uses cases and research trends. Specifically, we focus on novel approaches for (1) scalability and (2) fault tolerance in large scale distributed streaming(More)
One fundamental challenge in data stream processing is to cope with the ubiquity of disorder of tuples within a stream caused by network latency, operator parallelization, merging of asynchronous streams, etc. High result accuracy and low result latency are two conflicting goals in out-of-order stream processing. Different applications may prefer different(More)
A major challenge for cloud-based systems is to be fault tolerant to cope with an increasing probability of faults in cloud environments. This is especially true for in-memory computing solutions like data stream processing systems, where a single host failure might result in an unrecoverable information loss. In state of the art data streaming systems(More)
Sliding window join is one of the most important operators for stream applications. To produce high quality join results, a stream processing system must deal with the ubiquitous disorder within input streams which is caused by network delay, parallel processing, etc. Disorder handling involves an inevitable tradeoff between the latency and the quality of(More)
Achieving expressive and efficient content-based routing in publish/subscribe systems is a difficult problem. Traditional approaches prove to be either inefficient or severely limited in their expressiveness and flexibility. We present a novel routing method, based on Bloom filters, which shows high efficiency while simultaneously preserving the flexibility(More)