@inproceedings{poppe2017caesar,
author = {Poppe, Olga and Lei, Chuan and Rundensteiner, Elke A. and Dougherty, Daniel J. and Deva,  Goutham and Fajardo, Nicholas and Owens, James and Schweich,  Thomas and Valkenburg,  MaryAnn Van and Paisarnsrisomsuk, Sarun and Wiratchotisatian, Pitchaya and Gettel, George and Hollinger, Robert and Roberts,  Devin and Tocco, Daniel},
title = {CAESAR: Context-Aware Event Stream Analytics for Urban Transportation Services},
booktitle = {EDBT},
year = {2017},
month = {March},
abstract = {We demonstrate the first full-fledged context-aware event processing solution, called CAESAR, that supports application contexts as first class citizens. CAESAR offers human readable 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 context-driven 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.},
url = {http://approjects.co.za/?big=en-us/research/publication/caesar-2/},
pages = {590-593},
}