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检索条件"主题词=Exploratory data visualization"

共 4 条记录，以下是1-10 订阅

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Explorative visualization of Food data to Raise Awareness of Nutritional Value 21st

Explorative Visualization of Food Data to Raise Awareness of...

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21st International Conference on Human-Computer Interaction (HCII)

作者： Lunterova, Anna Spetko, Ondrej Palamas, George Aalborg Univ AC Meyers Vaenge 15 DK-2450 Copenhagen Denmark

ISBN: (纸本)9783030300333;9783030300326

The following research proposes a web based, interactive data visualization of a large food dataset, with an aim to raise awareness of their nutritional value. The system consists of an automatic dimensionality reduction scheme, based on t-SNE algorithm, and an exploratory user interface based on dynamic views of the data. Our approach allows users to effectively compare and relate visual representations of nutritional value in both micro and macro levels. The communicativeness of the application was evaluated regarding user experience and the storytelling functionality. Results show that the participants experienced a shift in perception from one mainly focused on taste to one aware of nutritional aspects. Additionally, the results suggest that a narrative experience can be generated, along with an effective long-term engagement.

关键词： Big data visualization Dimensionality reduction exploratory data visualization Interactive data storytelling

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Visual exploration to support the identification of relevant attributes in time-varying multivariate data

Visual exploration to support the identification of relevant...

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作者： Aurea Rossy Soriano Vargas UNIVERSIDADE DE SAO PAULO

学位级别：博士

Ionospheric scintillation is a rapid variation in the amplitude and/or phase of radio signals traveling through the ionosphere. This spatial and time-varying phenomenon is of interest because its occurrence may affect the reception quality of satellite signals. Specialized receivers at strategic regions can track multiple variables related to the phenomenon, generating a database of historical observations on the regional behavior of ionospheric scintillation. The analysis of such data is very challenging, since it consists of time-varying measurements of many variables which are heterogeneous in nature and with possibly many missing values, recorded over extensive time periods. There is a need to introduce alternative intuitive strategies that contribute to experts acquiring further knowledge from the ionospheric scintillation data. Such challenges motivated a study on the applicability of visualization techniques to support tasks of identification of relevant attributes in the study of the behavior of phenomena described by multiple time-varying variables, of which the ionospheric scintillation is a good example. In particular, this thesis introduces a visual analytics framework, named TV-MV Analytics, that supports exploratory tasks on time-varying multivariate data and was developed following the requirements of experts on ionospheric scintillation from the Faculty of Science and Technology of UNESP at Presidente Prudente, Brazil. TV-MV Analytics provides an interactive visual explo- ration loop to analysts inspecting the behavior of multiple variables at different temporal scales, through temporal representations associated with clustering and multidimensional projection techniques. Analysts can also assess how different feature sub-spaces contribute to character- izing a certain behavior, where they may direct the analysis process and include their domain knowledge in the exploratory analysis. We also illustrate the application of TV-MV Analytics on multivaria

关键词： Visual Analytics Time-varying Multivariate data Visual Feature Selection Feature Space Analysis exploratory data visualization

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A Need for exploratory Visual Analytics in Big data Research and for Open Science 20

A Need for Exploratory Visual Analytics in Big Data Research...

引用

20th International Conference Information Visualisation (IV)

作者： Tanaka, Yuzuru Sjobergh, Jonas Takahashi, Keisuke Hokkaido Univ Sapporo Hokkaido Japan

ISBN: (纸本)9781467389426

We argue that exploratory visual analytics frameworks are needed for efficient big data research and data-driven research, and exemplify with experiences from our research. Such frameworks can be used for iterative hypothesis generation and hypothesis verification, and for exploratory creation of appropriate explanatory variables to use in data acquisition and analysis. We discuss how complex analysis tools, e.g. data mining tools, can be integrated with the coordinated multiple views framework and we briefly present a framework that can support such extended coordinated multiple views frameworks and that can be used for "open science", i.e. making scientific research, methods, data, etc. reusable and more accessible to everyone.

关键词： visualization visual analytics exploratory data visualization coordinated multiple views direct manipulation big data open science Meme Media

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Histologic characterization of DCE-MRI breast tumors with dimensional data reduction

Histologic characterization of DCE-MRI breast tumors with di...

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Medical Imaging 2006 Conference

作者： Varini, Claudio Degenhard, Andreas Nattkemper, Tim W. Univ Bielefeld Fak Phys Condensed Matter Theory Grp Univ Str 25E5 D-33615 Bielefeld Germany

ISBN: (纸本)0819464236

The assessment of similarities of breast tumors in DCE-MRI is an important step to improving diagnostic accuracy. A comparison of a breast lesion with different histologic types of tumors can in addition provide further clinical information on the nature of the lesion itself. We present an approach to the visual comparison of different histologic types of breast tumor utilizing Locally Linear Embedding (LLE), an algorithm for dimensional data reduction. The experimental dataset contains the time-series of seven benign and seven malignant breast tumors of various histologic types that were manually labeled by an expert physician from a sequence of DCE-MRI volumes. The adopted DCE-MRI protocol involves six consecutive images of the female breast, yielding to a six-dimensional time-series of MR intensity values for each voxel. The set of all time-series from the 14 tumors constitutes a six-dimensional signal space where similar time-series exhibit locality. This high-dimensional dataset is projected into two dimensions by LLE while preserving the local space topology. In this way similar time-series are mapped onto neighboring data points in the LLE projection. Its visualization with customized colors encoding the histologic information provides a convenient interface for interactive comparison of various breast tumors belonging to different histologic families.

关键词： dimensional data reduction LLE exploratory data visualization breast cancer DCE-MRI

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