Now showing items 1-14 of 14

    • 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 ...
    • Autopipelining for data stream processing 

      Tang, Y.; Gedik, B. (Institute of Electrical and Electronics Engineers, 2013)
      Stream processing applications use online analytics to ingest high-rate data sources, process them on-the-fly, and generate live results in a timely manner. The data flow graph representation of these applications facilitates ...
    • Building user-defined runtime adaptation routines for stream processing applications 

      Jacques-Silva, G.; Gedik, B.; Wagle, R.; Wu, Kun-Lung; Kumar, V. (VLDB Endowment, 2012)
      Stream processing applications are deployed as continuous queries that run from the time of their submission until their cancellation. This deployment mode limits developers who need their applications to perform runtime ...
    • C-Stream: A coroutine-based elastic stream processing engine 

      Şahin, Semih (Bilkent University, 2015)
      Stream processing is a computational paradigm for on-the-fly processing of live data. This paradigm lends itself to implementations that can provide high throughput and low latency, by taking advantage of various forms ...
    • CAPSULE: Language and system support for efficient state sharing in distributed stream processing systems 

      Losa, G.; Kumar, V.; Andrade, H.; Gedik, Buğra; Hirzel, M.; Soulé, R.; Wu, K. -L. (ACM, 2012)
      Data stream processing applications are often expressed as data flow graphs, composed of operators connected via streams. This structured representation provides a simple yet powerful paradigm for building large-scale, ...
    • A catalog of stream processing optimizations 

      Hirzel M.; Soulé R.; Schneider S.; Gedik, B.; Grimm, R. (Association for Computing Machinery, 2014)
      Various research communities have independently arrived at stream processing as a programming model for efficient and parallel computing. These communities include digital signal processing, databases, operating systems, ...
    • From a calculus to an execution environment for stream processing 

      Soulé, R.; Hirzel, M.; Gedik, Buğra; Grimm, R. (ACM, 2012)
      At one level, this paper is about River, a virtual execution environment for stream processing. Stream processing is a paradigm well-suited for many modern data processing systems that ingest high-volume data streams from ...
    • Online nonlinear modeling for big data applications 

      Khan, Farhan (Bilkent University, 2017-12)
      We investigate online nonlinear learning for several real life, adaptive signal processing and machine learning applications involving big data, and introduce algorithms that are both e cient and e ective. We present ...
    • Partitioning functions for steteful data parallelism in stream processing 

      Gedik, B. (Association for Computing Machinery, 2014)
      In this paper we study partitioning functions for stream processing systems that employ stateful data parallelism to improve application throughput. In particular, we develop partitioning functions that are effective ...
    • River: an intermediate language for stream processing 

      Soulé R.; Hirzel M.; Gedik, B.; Grimm, R. (John Wiley & Sons Ltd., 2016)
      Summary This paper presents both a calculus for stream processing, named Brooklet, and its realization as an intermediate language, named River. Because River is based on Brooklet, it has a formal semantics that enables ...
    • S3-TM: scalable streaming short text matching 

      Basık F.; Gedik, B.; Ferhatosmanoğlu, H.; Kalender, M. E. (Association for Computing Machinery, 2015)
      Micro-blogging services have become major venues for information creation, as well as channels of information dissemination. Accordingly, monitoring them for relevant information is a critical capability. This is typically ...
    • 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 ...