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2006
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| |  | Andrienko, Natalia | Exploratory Analysis of Spatial and Temporal Data read moreAbstract: Exploratory data analysis (EDA) is about detecting and describing patterns, trends, and relations in data, motivated by certain purposes of investigation. As something relevant is detected in data, new questions arise, causing specific parts to be viewed in more detail. So EDA has a significant appeal: it involves hypothesis generation rather than mere hypothesis testing. The authors describe in detail and systemize approaches, techniques, and methods for exploring spatial and temporal data in particular. They start by developing a general view of data structures and characteristics and then build on top of this a general task typology, distinguishing between elementary and synoptic tasks. This typology is then applied to the description of existing approaches and technologies, resulting not just in recommendations for choosing methods but in a set of generic procedures for data exploration. Professionals practicing analysis will profit from tested solutions – illustrated in many examples – for reuse in the catalogue of techniques presented. Students and researchers will appreciate the detailed description and classification of exploration techniques, which are not limited to spatial data only. In addition, the general principles and approaches described will be useful for designers of new methods for EDA. | 2006 |
2005
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| | | Amar, Robert m. | Low-Level Components of Analytic Activity in Information Visualization read moreAbstract: Existing system-level taxonomies of visualization tasks are geared more towards the design of particular representations than the facilitation of user analytic activity. We present a set of ten low-level analysis tasks that largely capture people��?s activities while employing information visualization tools for understanding data. To help develop these tasks, we collected nearly 200 sample questions from students about how they would analyze five particular data sets from different domains. The questions, while not being totally comprehensive, illustrated the sheer variety of analytic questions typically posed by users when employing information visualization systems. We hope that the presented set of tasks is useful for information visualization system designers as a kind of common substrate to discuss the relative analytic capabilities of the systems. Further, the tasks may provide a form of checklist for system designers | 2005 |
2003
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| | | P, Gatalsky | Exploratory spatio-temporal visualization: an analytical review read moreAbstract: Current software tools for visualization of spatio-temporal data, on the one hand, utilize the opportunities provided by modern computer technologies, on the other hand, incorporate the legacy from the conventional cartography. We have considered existing visualization-based techniques for exploratory analysis of spatio-temporal data from two perspectives: (1) what types of spatio-temporal data they are applicable to; (2) what exploratory tasks they can potentially support.The technique investigation has been based on an operational typology of spatio-temporal data and analytical tasks we specially devised for this purpose. The result of the study is a structured inventory of existing exploratory techniques related to the types of data and tasks they are appropriate for. This result is potentially helpful for data analysts-users of geovisualization tools: it provides guidelines for selection of proper exploratory techniques depending on the characteristics of data to analyze and the goals of analysis. At the same time the inventory as well as the suggested typology of tasks could be useful for tool designers and developers of various domain-specific geovisualization applications. The designers can, on the one hand, see what task types are insufficiently supported by the existing tools and direct their creative activities towards filling the gaps, on the other hand, use the techniques described as basic elements for building new, more sophisticated ones. The application developers can, on the one hand, use the task and data typology in the analysis of potential user needs, on the other hand, appropriately select and combine existing tools in order to satisfy these needs. | 2003 |
2000
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| | | Amar, Rober | Low-Level Components of Analytic Activity in Information Visualization read moreAbstract: Existing system-level taxonomies of visualization tasks are geared more towards the design of particular representations than the facilitation of user analytic activity. We present a set of ten low-level analysis tasks that largely capture peoples activities while employing information visualization tools for understanding data. To help develop these tasks, we collected nearly 200 sample questions from students about how they would analyze five particular data sets from different domains. The questions, while not being totally comprehensive, illustrated the sheer variety of analytic questions typically posed by users when employing information visualization systems. We hope that the presented set of tasks is useful for information visualization system designers as a kind of common substrate to discuss the relative analytic capabilities of the systems. Further, the tasks may provide a form of checklist for system designers. | 2000 |
1998
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| | | Chi, Huai-hsin | An Operator Interaction Framework for Visualization Systems read moreAbstract: Information visualization encounters a wide variety of different
data domains. The visualization community has developed representation
methods and interactive techniques. As a community,
we have realized that the requirements in each domain are often
dramatically different. In order to easily apply existing methods,
researchers have developed a semiology of graphic representations.
We have extended this research into a framework that includes
operators and interactions in visualization systems, such as a
visualization spreadsheet. We discuss properties of this framework
and use it to characterize operations spanning a variety of different
visualization techniques. The framework developed in this paper
enables a new way of exploring and evaluating the design space of
visualization operators, and helps end--users in their analysis tasks.
Keywords: information visualization, operators, user interactions,
view/value, framework, spreadsheet, design, extensibility,
visualization systems.
1... | 1998 |
1996
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| | | Shneiderman, B. | The eyes have it: a task by data type taxonomy for informationvisualizations read moreAbstract: Sorry no abstract available for this article | 1996 |
| | | Shneiderman, B. | The eyes have it: a task by data type taxonomy for information read moreAbstract: A useful starting point for designing advanced graphical user interfaces is the visual information seeking Mantra: overview first, zoom and filter, then details on demand. But this is only a starting point in trying to understand the rich and varied set of information visualizations that have been proposed in recent years. The paper offers a task by data type taxonomy with seven data types (one, two, three dimensional data, temporal and multi dimensional data, and tree and network data) and seven tasks (overview, zoom, filter, details-on-demand, relate, history, and extracts) | 1996 |
1993
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| | | Keller, Peter R. | Visual cues: practical data visualization read moreAbstract: Sorry no abstract available for this article | 1993 |
1992
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| | | Springmeyer, Rebecca R. | A characterization of the scientific data analysis process read moreAbstract: Extensible scientific visualization tools are often offered as data analysis tools. While images may be the goal of visualization, insight is the goal of analysis. Visualization tools often fail to reflect this fact both in functionality and in their user interfaces, which typically focus on graphics and programming concepts rather than on concepts more meaningful to end-user scientists. This paper presents a characterization which shows how data visualization fits into the broader process of scientific data analysis. We conducted an empirical study, observing scientists from several disciplines while they analyzed their own data. Examination of the observations exposed process elements outside conventional image viewing. For example, analysts queried for quantitative information, made a variety of comparisons, applied math, managed data, and kept records. The characterization of scientific data analysis reveals activity beyond that traditionally supported by computer. It offers an understanding which has the potential to be applied to many future designs, and suggests specific recommendations for improving the support of this important aspect of scientific computing | 1992 |
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