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Scalable Natural User Interfaces for...

Coffey, Dane M.

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Scalable Natural User Interfaces for Data-Intensive Exploratory Visualization: Designing in the Context of Big Data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Scalable Natural User Interfaces for Data-Intensive Exploratory Visualization: Designing in the Context of Big Data./
作者:	Coffey, Dane M.
面頁冊數:	214 p.
附註:	Source: Dissertation Abstracts International, Volume: 76-01(E), Section: B.
Contained By:	Dissertation Abstracts International76-01B(E).
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3635113
ISBN:	9781321160819

Scalable Natural User Interfaces for Data-Intensive Exploratory Visualization: Designing in the Context of Big Data.
Coffey, Dane M.

Scalable Natural User Interfaces for Data-Intensive Exploratory Visualization: Designing in the Context of Big Data. - 214 p.

Source: Dissertation Abstracts International, Volume: 76-01(E), Section: B.

Thesis (Ph.D.)--University of Minnesota, 2014.

This item must not be sold to any third party vendors.

This dissertation investigates new exploratory visualization tools in data-intensive domains that are built upon natural user interface technology, including multi-touch surfaces and virtual reality. Scalable interactions are developed and evaluated in several software systems that are targeted toward supporting design workflows that make use of big data.

ISBN: 9781321160819Subjects--Topical Terms:

626642
Computer Science.

Scalable Natural User Interfaces for Data-Intensive Exploratory Visualization: Designing in the Context of Big Data.
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245 1 0 $a Scalable Natural User Interfaces for Data-Intensive Exploratory Visualization: Designing in the Context of Big Data.
300 $a 214 p.
500 $a Source: Dissertation Abstracts International, Volume: 76-01(E), Section: B.
500 $a Adviser: Daniel F. Keefe.
502 $a Thesis (Ph.D.)--University of Minnesota, 2014.
506 $a This item must not be sold to any third party vendors.
520 $a This dissertation investigates new exploratory visualization tools in data-intensive domains that are built upon natural user interface technology, including multi-touch surfaces and virtual reality. Scalable interactions are developed and evaluated in several software systems that are targeted toward supporting design workflows that make use of big data.
520 $a A new immersive and highly-interactive multi-touch workbench is presented, along with a theoretical framework and evaluation of how visualizations may be developed on it. Building upon this foundation, two different exploratory visualization software systems are presented that address distinct challenges faced by designers working in data-intensive domains. The first of these systems is called Slice World-In-Miniature (WIM), which is designed to overcome the difficulty associated with exploring large-volume data, where the complexity of the data often leads to the designers becoming disoriented. Using Overview+Detail techniques to provide context, the designer navigates inside of complex volumes using multi-touch gestures. This Slice WIM system is applied to a number of medical device design applications and evaluated by domain experts in this field. The second system is called Design by Dragging, which addresses the information overload associated with comparing and navigating many sets of interelated simulations. Design by Dragging gives the designer the power to explore high-dimensional simulation design spaces by using natural direct manipulation interactions. This system is applied to several problems in medical device design and in visual effects simulation, and a domain expert evaluation is presented.
520 $a The big data paradigm is integrally tied to the future of computing. The major contribution of this dissertation is its investigation into the effectiveness of natural user interfaces as a means of working in this paradigm. Although natural user interfaces have become ubiquitous in our daily lives, they are typically used only for simple interactions. This dissertation demonstrates that these technologies can also be effective in aiding design work in the context of big data, a result that could shape the future of computing and change the way designers work with computers.
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