I-Light Symposium 2005 Proceedings

I-Light was made possible by a special appropriation by the State of Indiana. The research described at the I-Light Symposium has been supported by numerous grants from several sources. Any opinions, findings and conclusions, or recommendations expressed in the 2005 I-Light Symposium Proceedings are those of the researchers and authors and do not necessarily reflect the views of the granting agencies.Indiana University Office of the Vice President for Research and Information Technology, Purdue University Office of the Vice President for Information Technology and CI

Optimization of Display-Wall Aware Applications on Cluster Based Systems

Actualment, els sistemes d'informació i comunicació que treballen amb grans volums de dades requereixen l'ús de plataformes que permetin una representació entenible des del punt de vista de l'usuari. En aquesta tesi s'analitzen les plataformes Cluster Display Wall, usades per a la visualització de dades massives, i es treballa concretament amb la plataforma Liquid Galaxy, desenvolupada per Google. Mitjançant la plataforma Liquid Galaxy, es realitza un estudi de rendiment d'aplicacions de visualització representatives, identificant els aspectes de rendiment més rellevants i els possibles colls d'ampolla. De forma específica, s'estudia amb major profunditat un cas representatiu d'aplicació de visualització, el Google Earth. El comportament del sistema executant Google Earth s'analitza mitjançant diferents tipus de test amb usuaris reals. Per a aquest fi, es defineix una nova mètrica de rendiment, basada en la ratio de visualització, i es valora la usabilitat del sistema mitjançant els atributs tradicionals d'efectivitat, eficiència i satisfacció. Adicionalment, el rendiment del sistema es modela analíticament i es prova la precisió del model comparant-ho amb resultats reals.Nowadays, information and communication systems that work with a high volume of data require infrastructures that allow an understandable representation of it from the user's point of view. This thesis analyzes the Cluster Display Wall platforms, used to visualized massive amounts of data, and specifically studies the Liquid Galaxy platform, developed by Google. Using the Liquid Galaxy platform, a performance study of representative visualization applications was performed, identifying the most relevant aspects of performance and possible bottlenecks. Specifically, we study in greater depth a representative case of visualization application, Google Earth. The system behavior while running Google Earth was analyzed through different kinds of tests with real users. For this, a new performance metric was defined, based on the visualization ratio, and the usability of the system was assessed through the traditional attributes of effectiveness, efficiency and satisfaction. Additionally, the system performance was analytically modeled and the accuracy of the model was tested by comparing it with actual results.Actualmente, los sistemas de información y comunicación que trabajan con grandes volúmenes de datos requieren el uso de plataformas que permitan una representación entendible desde el punto de vista del usuario. En esta tesis se analizan las plataformas Cluster Display Wall, usadas para la visualización de datos masivos, y se trabaja en concreto con la plataforma Liquid Galaxy, desarrollada por Google. Mediante la plataforma Liquid Galaxy, se realiza un estudio de rendimiento de aplicaciones de visualización representativas, identificando los aspectos de rendimiento más relevantes y los posibles cuellos de botella. De forma específica, se estudia en mayor profundidad un caso representativo de aplicación de visualización, el Google Earth. El comportamiento del sistema ejecutando Google Earth se analiza mediante diferentes tipos de test con usuarios reales. Para ello se define una nueva métrica de rendimiento, basada en el ratio de visualización, y se valora la usabilidad del sistema mediante los atributos tradicionales de efectividad, eficiencia y satisfacción. Adicionalmente, el rendimiento del sistema se modela analíticamente y se prueba la precisión del modelo comparándolo con resultados reales

Scientific Analysis by the Crowd: A System for Implicit Collaboration between Experts, Algorithms, and Novices in Distributed Work.

Crowd sourced strategies have the potential to increase the throughput of tasks historically constrained by the performance of individual experts. A critical open question is how to configure crowd-based mechanisms, such as online micro-task markets, to accomplish work normally done by experts. In the context of one kind of expert work, feature extraction from electron microscope images, this thesis describes three experiments conducted with Amazon’s Mechanical Turk to explore the feasibility of crowdsourcing for tasks that traditionally rely on experts. The first experiment combined the output from learning algorithms with judgments made by non-experts to see whether the crowd could efficiently and accurately detect the best algorithmic performance for image segmentation. Image segmentation is an important but rate limiting step in analyzing biological imagery. Current best practice relies on extracting features by hand. Results showed that crowd workers were able to match the results of expert workers in 87.5% of the cases given the same task and that they did so with very little training. The second experiment used crowd responses to progressively refine task instructions. Results showed that crowd workers were able to consistently add information to the instructions and produced results the crowd perceived as more clear by an average of 8.7%. Finally, the third experiment mapped images to abstract representations to see whether the crowd could efficiently and accurately identify target structures. Results showed that crowd workers were able to find 100% of known structures with an 82% decrease in false positives compared to conventional automated image processing. This thesis makes a number of contributions. First, the work demonstrates that tasks previously performed by highly-trained experts, such as image extraction, can be accomplished by non-experts in less time and with comparable accuracy when organized through a micro-task market. Second, the work shows that engaging crowd workers to reflect on the description of tasks can be used to have them refine tasks to produce increased engagement by subsequent crowd workers. Finally, the work shows that abstract representations perform nearly as well as actual images in terms of using a crowd of non-experts to locate targeted features.PHDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/102368/1/dlzz_1.pd