Zbigniew Jerzak

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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)
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)
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)
Elastic scaling allows data stream processing systems to dynamically scale in and out to react to workload changes. As a consequence, unexpected load peaks can be handled and the extent of the overprovisioning can be reduced. However, the strategies used for elastic scaling of such systems need to be tuned manually by the user. This is an error prone and(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)
State-of-the-art visual data analysis tools ignore bandwidth limitations. They fetch millions of records of high-volume time series data from an underlying RDBMS to eventually draw only a few thousand pixels on the screen. In this work, we demonstrate a pixel-aware big data visualization system that dynamically adapts the number of data points transmitted(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)
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)