Information-Aware 2^n-Tree for Efficient Out-of-Core Indexing of Very Large Multidimensional Volumetric Data

Kim, Jusub; JáJá, Joseph F.

doi:10.1109/ssdbm.2007.15

Cited by 2 publications

(2 citation statements)

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“…The use of informationtheoretic measures is increasingly popular in visualization [2]. Kim and JaJa [21] build information-aware octrees to extract isosurfaces. Jänicke et.…”

Section: Information Contentmentioning

confidence: 99%

Hierarchical Exploration of Volumes Using Multilevel Segmentation of the Intensity-Gradient Histograms

Varshney

JáJá

2012

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. We explore a volume dataset with an intensity-gradient histogram segmentation hierarchy. 1. We compute a 2D intensitygradient histogram from a volume dataset. 2. We mimic the user visual search of shapes in the histogram by recursively segmenting the histogram image using normalized cuts. 3. We construct a multi-resolution hierarchy for interactive exploration. 4. Users traverse this hierarchy to discover features in the volume data and compose meaningful visualizations.Abstract-Visual exploration of volumetric datasets to discover the embedded features and spatial structures is a challenging and tedious task. In this paper we present a semi-automatic approach to this problem that works by visually segmenting the intensitygradient 2D histogram of a volumetric dataset into an exploration hierarchy. Our approach mimics user exploration behavior by analyzing the histogram with the normalized-cut multilevel segmentation technique. Unlike previous work in this area, our technique segments the histogram into a reasonable set of intuitive components that are mutually exclusive and collectively exhaustive. We use information-theoretic measures of the volumetric data segments to guide the exploration. This provides a data-driven coarse-to-fine hierarchy for a user to interactively navigate the volume in a meaningful manner.

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“…The use of informationtheoretic measures is increasingly popular in visualization [2]. Kim and JaJa [21] build information-aware octrees to extract isosurfaces. Jänicke et.…”

Section: Information Contentmentioning

confidence: 99%

Hierarchical Exploration of Volumes Using Multilevel Segmentation of the Intensity-Gradient Histograms

Varshney

JáJá

2012

IEEE Trans. Visual. Comput. Graphics

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“…As mentioned above, higher EEI gives higher number of options for a decision maker to choose from, which can be seen as a higher amount of information that should be dealt with. Such higher amount of information is a measure of higher entropy of a system (Mitchell 1997; Khemani, D., personal communication), and ‘the more the variability of x [such as the number of options available to a manager], the more unpredictable x is, and the higher the entropy’ (Kim & JaJa 2007). On the other hand, one of the common definitions of entropy states that ‘the greater the disorder the higher the entropy’ (The Essential Dictionary of Science 2004), stating that entropy is a measure of disorder and vice versa.…”

Section: Eementioning

confidence: 99%

Effective efficiency in water resources management using efficiency elasticity index

Haie

Machado

Pereira

et al. 2011

Water & Environment J

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Effective efficiency (EE) is a water use performance indicator of a system, integrating net evapotranspiration, volumes of water in and out of the system (including reused water) and their qualities. In order to analyse the nature of management and its options in promoting efficiencies, an efficiency elasticity index (EEI) is developed using EE. The maximum value of EEI for a given net evapotranspiration sets a threshold value for EE under which the management of the system should be improved. This makes it possible to evaluate how good the management of a system is and allows the decision makers to analyse efficiency targets using a graphical approach (assuming that interventions have the same costs). Hypothetical examples and real irrigation cases from United States and Egypt show the practical utility of EE and the methodology developed through the use of EEI. It is also shown that classical efficiency should be used with caution.

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Information-Aware 2^n-Tree for Efficient Out-of-Core Indexing of Very Large Multidimensional Volumetric Data

Cited by 2 publications

References 10 publications

Hierarchical Exploration of Volumes Using Multilevel Segmentation of the Intensity-Gradient Histograms

Hierarchical Exploration of Volumes Using Multilevel Segmentation of the Intensity-Gradient Histograms

Effective efficiency in water resources management using efficiency elasticity index

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