CAESAR: Context-Aware Event Stream Analytics for Urban Transportation Services
نویسندگان
چکیده
We demonstrate the first full-fledged context-aware event processing solution, called CAESAR, that supports application contexts as first class citizens. CAESAR offers humanreadable specification of context-aware application semantics composed of context derivation and context processing. Both classes of queries are only relevant during their respective contexts. They are suspended otherwise to save resources and to speed up the system responsiveness to the current situation. Furthermore, we demonstrate the contextdriven optimization techniques including context window push-down and query workload sharing among overlapping context windows. We illustrate the usability and performance gain of our CAESAR system by a use case scenario for urban transportation services using real data sets [2, 1].
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