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 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)
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)
Typical use cases like financial trading or monitoring of manufacturing equipment pose huge challenges regarding end to end latency as well as throughput towards existing data stream processing systems. Established solutions like Apache S4 or Storm need to scale out to a large set of hosts to meet these challenges. An ideal system can react to workload(More)
Visual analysis of high-volume time series data is ubiquitous in many industries, including finance, banking, and discrete manufacturing. Contemporary, RDBMS-based systems for visualization of high-volume time series data have difficulty to cope with the hard latency requirements and high inges-tion rates of interactive visualizations. Existing solutions(More)
Elastic scaling allows a data stream processing system to react to a dynamically changing query or event workload by automatically scaling in or out. Thereby, both unpredictable load peaks as well as underload situations can be handled. However, each scaling decision comes with a latency penalty due to the required operator movements. Therefore, in practice(More)
The focus of the DEBS 2015 Grand Challenge is on processing of data streams originating from the New York City Taxi and Limousine Commission. The data is made available under the Freedom of Information Law and provides information pickup, drop off, and payments made in New York City medallion taxis. The goal of the DEBS 2015 Grand Challenge is to process(More)