Defended His Thesis
The modeling approach chosen is of the direct type ; it consists of representing the electromechanical chain in the common mode base by two ports networks.
This generic model allows us to estimate common mode currents directly in the frequency domain at different locations.
Stream re-partitioning is used to reconfigure execution while processing takes place, and previous techniques did not utilize window semantics.
In this dissertation, we put stream processing in a procrustean bed, in terms of the manner and the degree of processing.
In the past, three techniques have been developed for improving an SPE’s ability to adapt.
Generative Modelling and Machine Learning Methods for Travel Behaviour Analysis Professor Ricardo Daziano, Cornell University Professor Eric J.
Committee Alexandros Labrinidis, Department of Computer Science, University of Pittsburgh (co-chair) Panos Chrysanthis, Department of Computer Science, University of Pittsburgh (co-chair) Jack Lange, Department of Computer Science, University of Pittsburgh (member) Andy Pavlo, Department of Computer Science, Carnegie Mellon University (member) TITLE: A Procrustean Approach to Stream Processing Abstract: === START === The increasing demand for real-time data processing and the constantly growing data volume have contributed to the rapid evolution of Stream Processing Engines (SPEs), which are designed to continuously process data.
Low operational cost and timely delivery of results are both objectives of paramount importance for SPEs.
To this end, we present new approaches, which are applicable to both exact and approximate stream processing in modern SPEs.
Our solutions offer improvements in performance and accuracy on real-world data.