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Kundenbewertungen stellen vielfach eine entscheidende Messgröße zur Beurteilung der Qualität eines Dienstes dar. Entsprechend groß ist die Versuchung für Anbieter, sich mit selbst verfassten Bewertungen ins rechte Licht zu rücken.
The most critical intervention point to affect design at and across all physical scales (see graphic below) is to pay attention to the processes and patterns underlying their physical manifestations…
Hardware performance monitoring counters have recently received a lot of attention. They have been used by diverse communities to understand and improve the quality of computing systems: for example, architects use them to extract application characteristics and propose new hardware mechanisms; compiler writers study how generated code behaves on particular hardware; software developers identify critical regions of their applications and evaluate design choices to select the best performing implementation. In this paper, we propose that counters be used by all categories of users, in particular non-experts, and we advocate that a few simple metrics derived from these counters are relevant and useful. For example, a low IPC (number of executed instructions per cycle) indicates that the hardware is not performing at its best; a high cache miss ratio can suggest several causes, such as conflicts between processes in a multicore environment. We also introduce a new simple and flexible user-level tool that collects these data on Linux platforms, and we illustrate its practical benefits through several use cases.
Hal Varian, What econometrics can learn from machine learning and vice versa. http://www.stanford.edu/class/ee380/winter-schedule-20142015.html http://www.stanford.edu/class/ee380/Abstracts/140129.html http://ee380.stanford.edu/cgi-bin/videologger.php?target=140129-ee380-300.asx 140129-ee380-500.wmv 140129-slides-Machine-Learning-and-Econometrics.pdf