SPADE: the system s declarative stream processing engine
By Bugra Gedik, H. Andrade, Kun-Lung Wu, Philip S. Yu, Myungcheol Doo. SIGMOD Conference, 2008
486 citations recorded by the source corpus.
Subfields: Computer Networks and Communications
View Paper: https://www.semanticscholar.org/paper/00c5d5189e29dba8f2729929476b739a5c35bc02
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In Computer Networks and Communications
- John Wilkes
- Xiaohui Gu
- Martin Strohbach
- Sethuraman Subbiah
- Holger Ziekow
- Navot Akiva
- Zhiming Shen
- Vangelis Gazis
Citations in the corpus
- Towards a Big Data Analytics Framework for IoT and Smart City Applications (2015, methodology): “The actual processing of data uses different computational frameworks such as Hadoop’s MapReduce [ 63], Hama’s Bulk Synchronous Processing framework [ 53], or graph processing frameworks [ 24].”
- CloudScale: elastic resource scaling for multi-tenant cloud systems (2011, methodology): “We implemented CloudScale on top of the Xen virtualization platform and conducted extensive evaluation studies using the RUBiS [4] online auction benchmark (PhP version), Hadoop MapReduce systems [2, 15], and IBM System S data stream processing system [21].”
- A catalog of stream processing optimizations (2014, background): “Compilers for databases typically first translate an application (or query) into a graph (or plan) of logical operators, and then translate that to a graph (or plan) of physical operators [Garcia-Molina et al. 2008].”