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dc.contributor.advisorAnshus, Otto
dc.contributor.advisorHa, Phuong H
dc.contributor.authorHolsbø, Einar
dc.date.accessioned2014-06-16T10:45:53Z
dc.date.available2014-06-16T10:45:53Z
dc.date.issued2014-05-15
dc.description.abstractWe create 2.5 quintillion bytes of data every day. A whole 90% of the world’s data was created in the last two years.1 One contribution to this massive bulk of data is Twitter: Twitter users create 500 million tweets a day,2 which fact has greatly impacted social science [24] and journalism [39]. Network analysis is important in social science [6], but with so much data there is a real danger of information overload, and there is a general need for tools that help users navigate and make sense of this. Data exploration is one way of analyzing a data set. Exploration-based analysis is to let the data suggest hypotheses, as opposed to starting out with a hypothesis to either confirm or refute. Visualization is an important exploration tool. Given the ready availability of large-scale displays [1], we believe that an ideal visual exploration system would leverage these, and leverage the fact that there are many different ways to visualize something. We propose to use wall- sized displays to provide many different views of the same data set and as such let the user explore the data by exploring visualizations. Our thesis is that a display wall architecture [1, 42] is an ideal platform for such a scheme, providing both the resolution and the compute power required. Proper utilization of this would allow for useful sensemaking and storytelling. To evaluate our thesis we have built a system for gathering and analyzing Twitter data, and exploring it through multiple visualizations. Our evaluation of the prototype has provided us with insights that will allow us to create a practicable system, and demonstrations of the prototype has uncovered interesting stories in our case study data set. We find that it is strictly necessary to use clever pre-computation, or pipelining, or streaming to meet the strict latency requirements of providing visualization interactively fast. Our further experiments with the system have led to new discoveries in streaming graph processing.en
dc.identifier.urihttp://hdl.handle.net/10037/6383
dc.identifier.urnURN:NBN:no-uit_munin_6007
dc.language.isoengen
dc.publisherUiT Norges arktiske universiteten
dc.publisherUiT The Arctic University of Norwayen
dc.rights.accessRightsopenAccess
dc.subject.courseIDINF-3990en
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Kommunikasjon og distribuerte systemer: 423en
dc.subjectVDP::Mathematics and natural science: 400::Information and communication science: 420::Communication and distributed systems: 423en
dc.titleLarge Multiples : exploring the large-scale scattergun approach to visualization and analysisen
dc.typeMaster thesisen
dc.typeMastergradsoppgaveen


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