80,824 research outputs found
AN EXTENDABLE VISUALIZATION AND USER INTERFACE DESIGN FOR TIME-VARYING MULTIVARIATE GEOSCIENCE DATA
Geoscience data has unique and complex data structures, and its visualization has been challenging due to a lack of effective data models and visual representations to tackle the heterogeneity of geoscience data. In today’s big data era, the needs of visualizing geoscience data become urgent, especially driven by its potential value to human societies, such as environmental disaster prediction, urban growth simulation, and so on. In this thesis, I created a novel geoscience data visualization framework and applied interface automata theory to geoscience data visualization tasks. The framework can support heterogeneous geoscience data and facilitate data operations. The interface automata can generate a series of interactions that can efficiently impress users, which also provides an intuitive method for visualizing and analysis geoscience data. Except clearly guided users to the specific visualization, interface automata can also enhance user experience by eliminating automation surprising, and the maintenance overhead is also reduced. The new framework was applied to INSIGHT, a scientific hydrology visualization and analysis system that was developed by the Nebraska Department of Natural Resources (NDNR). Compared to the existing INSIGHT solution, the new framework has brought many advantages that do not exist in the existing solution, which proved that the framework is efficient and extendable for visualizing geoscience data.
Adviser: Hongfeng Y
AN EXTENDABLE VISUALIZATION AND USER INTERFACE DESIGN FOR TIME-VARYING MULTIVARIATE GEOSCIENCE DATA
Geoscience data has unique and complex data structures, and its visualization has been challenging due to a lack of effective data models and visual representations to tackle the heterogeneity of geoscience data. In today’s big data era, the needs of visualizing geoscience data become urgent, especially driven by its potential value to human societies, such as environmental disaster prediction, urban growth simulation, and so on. In this thesis, I created a novel geoscience data visualization framework and applied interface automata theory to geoscience data visualization tasks. The framework can support heterogeneous geoscience data and facilitate data operations. The interface automata can generate a series of interactions that can efficiently impress users, which also provides an intuitive method for visualizing and analysis geoscience data. Except clearly guided users to the specific visualization, interface automata can also enhance user experience by eliminating automation surprising, and the maintenance overhead is also reduced. The new framework was applied to INSIGHT, a scientific hydrology visualization and analysis system that was developed by the Nebraska Department of Natural Resources (NDNR). Compared to the existing INSIGHT solution, the new framework has brought many advantages that do not exist in the existing solution, which proved that the framework is efficient and extendable for visualizing geoscience data.
Adviser: Hongfeng Y
Researching Framework for Simulating/Implementating P Systems
Researching simulation/implementation of membranes systems is very recent. Present literature
gathers new publications frequently about software/hardware, data structures and algorithms for implementing P
system evolution.
In this context, this work presents a framework which goal is to make tasks of researchers of this field easier.
Hence, it establishes the set of cooperating classes that form a reusable and flexible design for the customizable
evaluation with new data structures and algorithms. Moreover, it includes customizable services for correcting,
monitoring and logging the evolution and edition, recovering, automatic generating, persistence and visualizing P
systems
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Visualizing latent domain knowledge
Knowledge discovery and data mining commonly rely on finding salient patterns of association from a vast amount of data. Traditional citation analysis of scientific literature draws insights from strong citation patterns. Latent domain knowledge, in contrast to the mainstream domain knowledge, often consists of highly relevant but relatively infrequently cited scientific works. Visualizing latent domain knowledge presents a significant challenge to knowledge discovery and quantitative studies of science. We build upon a citation-based knowledge visualization procedure and develop an approach that not only captures knowledge structures from prominent and highly cited works, but also traces latent domain knowledge through low-frequency citation chains. We apply this approach to two cases: (1) identifying cross-domain applications of Pathfinder networks (PFNETs) and (2) clarifying the current status of scientific inquiry of a possible link between Bovine spongiform encephalopathy (BSE), also known as mad cow disease, and a new variant Creutzfeldt-Jakob disease (vCJD), a type of brain disease in human
The boundary coefficient : a vertex measure for visualizing and finding structure in weighted graphs
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