Applications of dynamic data flow programming to real-time interactive simulations

Author(s):  
S.A. Morrison
2010 ◽  
Vol 69 (1) ◽  
pp. 12-37 ◽  
Author(s):  
Diane Favro ◽  
Christopher Johanson

Scientifically accurate, three-dimensional digital representations of historical environments allow architectural historians to explore viewsheds, movement, sequencing, and other factors. Using real-time interactive simulations of the Roman Forum during the mid-Republic and the early third century CE, Diane Favro and Christopher Johanson examine the visual and sequential interrelationships among audience, actors, and monuments during funeral rituals. Death in Motion: Funeral Processions in the Roman Forum presents a hypothetical reconstruction of the funeral of the Cornelii family in the early second century BCE and argues that the conventional understanding of the staging of the funeral oration may be incorrect. It then reviews the imperial funerals of the emperors Pertinax and Septimius Severus to compare the ways that later building in the Roman Forum altered the ritual experience, controlled participant motion, and compelled the audience to submit to an imperial program of viewing.


2013 ◽  
Author(s):  
Abdulrahman A. Al-Amer ◽  
Muhammad Al-Gosayir ◽  
Naser Al-Naser ◽  
Hussain Al-Towaileb

2019 ◽  
Vol 1 ◽  
pp. 1-1
Author(s):  
Rex G. Cammack ◽  
Paul Hunt

<p><strong>Abstract.</strong> In many modern sports, athlete tracking for athlete performance analysis is a common practice. Most of the time this athlete tracking is done during training sessions. At some World Tour cycling races the broadcasting company and race organizers use athlete tracking data during race events for various graphical for fans of the sport. This research attempt to use the race real time broadcast of data to produce a web mapping application that will show detailed cycling race tactics and other mapping forms in near real time. This research focuses on data flow and processing for dynamic mapping of complex point data patterns.</p>


The rise of social media platforms like Twitter and the increasing adoption by people in order to stay connected provide a large source of data to perform analysis based on the various trends, events and even various personalities. Such analysis also provides insight into a person’s likes and inclinations in real time independent of the data size. Several techniques have been created to retrieve such data however the most efficient technique is clustering. This paper provides an overview of the algorithms of the various clustering methods as well as looking at their efficiency in determining trending information. The clustered data may be further classified by topics for real time analysis on a large dynamic data set. In this paper, data classification is performed and analyzed for flaws followed by another classification on the same data set.


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