TY - GEN
T1 - Processing big events with showers and streams
AU - Doblander, Christoph
AU - Rabl, Tilmann
AU - Jacobsen, Hans Arno
PY - 2014
Y1 - 2014
N2 - Emerging use cases derived from the area of cloud computing, smart power grids, and business process management require a set of capabilities not met by traditional event processing systems. These use cases were chosen to illustrate the capabilities required from systems that are able to process what we refer to as Big Events, that is Big Data in motion. To further illustrate Big Events, we identify three use cases and analyze the characteristics of the events involved. Based on this analysis, we specify requirements regarding the event schema, event query language, historic event processing needs, event timing, and result accuracy. Collectively, we refer to the constellation of state changes in a given system that exhibits these characteristics as event showers, referring to the collective of these events, similar to the notion of an event stream in the context of event stream processing. We call systems that offer capabilities for meeting these requirements event shower processing systems in contrast to traditional event (stream) processing systems. The use cases we picked, demonstrate that additional value can be captured by having shower processing systems in place. The benefits lie in the new possibilities to gain additional insights, increase observability, and to further exert control and opportunities for optimizations in the given applications.
AB - Emerging use cases derived from the area of cloud computing, smart power grids, and business process management require a set of capabilities not met by traditional event processing systems. These use cases were chosen to illustrate the capabilities required from systems that are able to process what we refer to as Big Events, that is Big Data in motion. To further illustrate Big Events, we identify three use cases and analyze the characteristics of the events involved. Based on this analysis, we specify requirements regarding the event schema, event query language, historic event processing needs, event timing, and result accuracy. Collectively, we refer to the constellation of state changes in a given system that exhibits these characteristics as event showers, referring to the collective of these events, similar to the notion of an event stream in the context of event stream processing. We call systems that offer capabilities for meeting these requirements event shower processing systems in contrast to traditional event (stream) processing systems. The use cases we picked, demonstrate that additional value can be captured by having shower processing systems in place. The benefits lie in the new possibilities to gain additional insights, increase observability, and to further exert control and opportunities for optimizations in the given applications.
UR - http://www.scopus.com/inward/record.url?scp=84958536893&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-53974-9_6
DO - 10.1007/978-3-642-53974-9_6
M3 - Conference contribution
AN - SCOPUS:84958536893
SN - 9783642539732
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 60
EP - 71
BT - Specifying Big Data Benchmarks - First Workshop, WBDB 2012, and Second Workshop, WBDB 2012, Revised Selected Papers
PB - Springer Verlag
T2 - 2nd Workshop on Specifying Big Data Benchmarks, WBDB 2012
Y2 - 17 December 2012 through 18 December 2012
ER -