Predicting knowledge in an ontology stream
Freddy Lécué, Jeff Z. Pan
IJCAI 2013
High-performance stream processing is critical in many sense-and-respond application domainsfrom environmental monitoring to algorithmic trading. In this paper, we focus on language and runtime support for improving the performance of sense-and-respond applications in processing data from high-rate live streams. The central tenets of this work are the programming model, the workload splitting mechanisms, the code generation framework, and the underlying System S middleware and Spade programming model. We demonstrate considerable scalability behavior coupled with low processing latency in a real-world financial trading application. © 2010 Elsevier Inc. All rights reserved.
Freddy Lécué, Jeff Z. Pan
IJCAI 2013
Arnold.L. Rosenberg
Journal of the ACM
Hagen Soltau, Lidia Mangu, et al.
ASRU 2011
Arnold L. Rosenberg
Journal of the ACM