Now showing items 1-5 of 5

    • Auto-parallelizing stateful distributed streaming applications 

      Schneider, S.; Hirzel, M.; Gedik, Buğra; Wu, K. -L. (2012)
      Streaming applications transform possibly infinite streams of data and often have both high throughput and low latency requirements. They are comprised of operator graphs that produce and consume data tuples. The streaming ...
    • Big-data streaming applications scheduling based on staged multi-armed bandits 

      Kanoun, K.; Tekin, C.; Atienza, D.; Van Der Schaar, M. (Institute of Electrical and Electronics Engineers, 2016)
      Several techniques have been recently proposed to adapt Big-Data streaming applications to existing many core platforms. Among these techniques, online reinforcement learning methods have been proposed that learn how to ...
    • Safe data parallelism for general streaming 

      Schneider S.; Hirzel M.; Gedik, B.; Wu, Kun-Lung (Institute of Electrical and Electronics Engineers, 2015)
      Streaming applications process possibly infinite streams of data and often have both high throughput and low latency requirements. They are comprised of operator graphs that produce and consume data tuples. General streaming ...
    • SPL: an extensible language for distributed stream processing 

      Hirzel M.; Schneider S.; Gedik, B. (Association for Computing Machinery, 2017)
      Big data is revolutionizing how all sectors of our economy do business, including telecommunication, transportation, medical, and finance. Big data comes in two flavors: data at rest and data in motion. Processing data in ...
    • Tutorial: Stream processing optimizations 

      Schneider, S.; Hirzel, M.; Gedik, Buğra (ACM, 2013)
      This tutorial starts with a survey of optimizations for streaming applications. The survey is organized as a catalog that introduces uniform terminology and a common categorization of optimizations across disciplines, such ...