Now showing items 1-6 of 6

    • 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 ...
    • Experimental evaluation of polycrystalline diamond tool geometries while drilling carbon fiber-reinforced plastics 

      Karpat, Y.; Deǧer, B.; Bahtiyar, O. (Springer, 2014)
      Polycrystalline diamond (PCD) drills are commonly employed in carbon fiber-reinforced plastic (CFRP) drilling to satisfy hole quality conditions with an acceptable tool life and productivity. Despite their common use in ...
    • Multi-resolution social network community identification and maintenance on big data platform 

      Aksu, Hidayet; Canım, M.; Chang, Y.-C.; Körpeoğlu, İbrahim; Ulusoy, Özgür (IEEE, 2013-06-07)
      Community identification in social networks is of great interest and with dynamic changes to its graph representation and content, the incremental maintenance of community poses significant challenges in computation. ...
    • ON two-dimensional sparse matrix partitioning: models, methods, and a recipe 

      Çatalyürek, U. V.; Aykanat, Cevdet; Uçar, A. (Society for Industrial and Applied Mathematics, 2010)
      We consider two-dimensional partitioning of general sparse matrices for parallel sparse matrix-vector multiply operation. We present three hypergraph-partitioning-based methods, each having unique advantages. The first one ...
    • RailwayDB: adaptive storage of interaction graphs 

      Soulé R.; Gedik, B. (Association for Computing Machinery, 2016)
      We are living in an ever more connected world, where data recording the interactions between people, software systems, and the physical world is becoming increasingly prevalent. These data often take the form of a temporally ...
    • Weakly supervised object localization with multi-fold multiple instance learning 

      Cinbis, R. G.; Verbeek, J.; Schmid, C. (IEEE Computer Society, 2017)
      Object category localization is a challenging problem in computer vision. Standard supervised training requires bounding box annotations of object instances. This time-consuming annotation process is sidestepped in weakly ...