KEYWORDS: Visualization, Information visualization, Visual analytics, Chemical elements, Associative arrays, 3D displays, Visual system, Databases, Control systems, Human-machine interfaces
Effective representation of large, complex collections of information (datasets) presents a difficult challenge. Visualization
is a solution that uses a visual interface to support efficient analysis and discovery within the data. Our primary goal
in this paper is a technique that allows viewers to compare multiple query results representing user-selected subsets of
a multidimensional dataset. We present an algorithm that visualizes multidimensional information along a space-filling
spiral. Graphical glyphs that vary their position, color, and texture appearance are used to represent attribute values for the
data elements in each query result. Guidelines from human perception allow us to construct glyphs that are specifically
designed to support exploration, facilitate the discovery of trends and relationships both within and between data elements,
and highlight exceptions. A clustering algorithm applied to a user-chosen ranking attribute bundles together similar data
elements. This encapsulation is used to show relationships across different queries via animations that morph between
query results. We apply our techniques to the MovieLens recommender system, to demonstrate their applicability in a
real-world environment, and then conclude with a simple validation experiment to identify the strengths and limitations of
our design, compared to a traditional side-by-side visualization.
KEYWORDS: Visualization, Data storage, Visual analytics, Information visualization, Curium, Chemical elements, Associative arrays, Analytical research, 3D displays, Graphic design
This paper presents a technique that allows viewers to visually analyze, explore, and compare a storage controller's performance. We present an algorithm that visualizes storage controller's performance metrics along a traditional 2D grid or a linear space-filling spiral. We use graphical "glyphs" (simple geometric objects) that vary in color, spatial placement and texture properties to represent the attribute values contained in a data element. When shown together, the glyphs form visual patterns that support exploration, facilitate discovery of data characteristics, relationships, and highlight trends and exceptions. We identified four important goals for our project: 1. Design a graphical glyph that supports flexibility in its placement, and in its ability to represent multidimensional data elements. 2. Build an effective visualization technique that uses glyphs to represent the results gathered from running different tests on the storage controllers by varying their performance parameters. 3. Build an effective representation to compare the performance of storage controller(s) during different time intervals. 4. Work with domain experts to select properties of storage controller performance data that are most useful to visualize.
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