Visualizing the Effects of Logically Combined Filters

Geymayer, Thomas; Lex, Alexander; Streit, Marc; Schmalstieg, Dieter

doi:10.1109/iv.2011.52

Cited by 3 publications

(1 citation statement)

References 14 publications

(15 reference statements)

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“…Meta search combines these results using set union, and allows exploring which elements were retrieved by which engines [Spo04]. ● Faceted search : this search paradigm allows defining queries over data items based on a number of search facets [Tun09]. Each facet can be modelled as a set of the data item that satisfy its criterion [BKP12, AMR14, GLSS11]. ● Formal Concept Analysis (FCA) [GWW99] makes use of concepts and their relations to reason about problems. These relations are analogous to set relations, when treating concepts as sets [BKP12]. ● Genomics : to analyse how genes belong to individuals, each individual can be modelled as a set that contains specific genes.…”

Section: Sets and Set‐typed Datamentioning

confidence: 99%

The State‐of‐the‐Art of Set Visualization

Alsallakh

Micallef

Aigner

et al. 2015

Computer Graphics Forum

111

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Sets comprise a generic data model that has been used in a variety of data analysis problems. Such problems involve analysing and visualizing set relations between multiple sets defined over the same collection of elements. However, visualizing sets is a non-trivial problem due to the large number of possible relations between them. We provide a systematic overview of state-of-the-art techniques for visualizing different kinds of set relations. We classify these techniques into six main categories according to the visual representations they use and the tasks they support. We compare the categories to provide guidance for choosing an appropriate technique for a given problem. Finally, we identify challenges in this area that need further research and propose possible directions to address these challenges. Further resources on set visualization are available at http://www.setviz.net.

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Section: Sets and Set‐typed Datamentioning

confidence: 99%

The State‐of‐the‐Art of Set Visualization

Alsallakh

Micallef

Aigner

et al. 2015

Computer Graphics Forum

111

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Visualizing uncertainty in biological expression data

Holzhüter

Lex

Schmalstieg

et al. 2012

Visualization and Data Analysis 2012

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Expression analysis of ∼omics data using microarrays has become a standard procedure in the life sciences. However, microarrays are subject to technical limitations and errors, which render the data gathered likely to be uncertain. While a number of approaches exist to target this uncertainty statistically, it is hardly ever even shown when the data is visualized using for example clustered heatmaps. Yet, this is highly useful when trying not to omit data that is "good enough" for an analysis, which otherwise would be discarded as too unreliable by established conservative thresholds. Our approach addresses this shortcoming by first identifying the margin above the error threshold of uncertain, yet possibly still useful data. It then displays this uncertain data in the context of the valid data by enhancing a clustered heatmap. We employ different visual representations for the different kinds of uncertainty involved. Finally, it lets the user interactively adjust the thresholds, giving visual feedback in the heatmap representation, so that an informed choice on which thresholds to use can be made instead of applying the usual rule-of-thumb cut-offs. We exemplify the usefulness of our concept by giving details for a concrete use case from our partners at the Medical University of Graz, thereby demonstrating our implementation of the general approach.

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Tendency on the Application of Drill-Down Analysis in Scientific Studies: A Systematic Review

et al. 2023

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With the fact that new server technologies are coming to market, it is necessary to update or create new methodologies for data analysis and exploitation. Applied methodologies go from decision tree categorization to artificial neural networks (ANN) usage, which implement artificial intelligence (AI) for decision making. One of the least used strategies is drill-down analysis (DD), belonging to the decision trees subcategory, which because of not having AI resources has lost interest among researchers. However, its easy implementation makes it a suitable tool for database processing systems. This research has developed a systematic review to understand the prospective of DD analysis on scientific literature in order to establish a knowledge platform and establish if it is convenient to drive it to integration with superior methodologies, as it would be those based on ANN, and produce a better diagnosis in future works. A total of 80 scientific articles were reviewed from 1997 to 2023, showing a high frequency in 2021 and experimental as the predominant methodology. From a total of 100 problems solved, 42% were using the experimental methodology, 34% descriptive, 17% comparative, and just 7% post facto. We detected 14 unsolved problems, from which 50% fall in the experimental area. At the same time, by study type, methodologies included correlation studies, processes, decision trees, plain queries, granularity, and labeling. It was observed that just one work focuses on mathematics, which reduces new knowledge production expectations. Additionally, just one work manifested ANN usage.

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Visualizing the Effects of Logically Combined Filters

Cited by 3 publications

References 14 publications

The State‐of‐the‐Art of Set Visualization

The State‐of‐the‐Art of Set Visualization

Visualizing uncertainty in biological expression data

Tendency on the Application of Drill-Down Analysis in Scientific Studies: A Systematic Review

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