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"abstract": "In this paper we describe a novel method to integrate interactive visual analysis and machine learning to support the insight generation of the user. The suggested approach combines the vast search and processing power of the computer with the superior reasoning and pattern recognition capabilities of the human user. An evolutionary search algorithm has been adapted to assist in the fuzzy logic formalization of hypotheses that aim at explaining features inside multivariate, volumetric data. Up to now, users solely rely on their knowledge and expertise when looking for explanatory theories. However, it often remains unclear whether the selected attribute ranges represent the real explanation for the feature of interest. Other selections hidden in the large number of data variables could potentially lead to similar features. Moreover, as simulation complexity grows, users are confronted with huge multidimensional data sets making it almost impossible to find meaningful hypotheses at all. We propose an interactive cycle of knowledge-based analysis and automatic hypothesis generation. Starting from initial hypotheses, created with linking and brushing, the user steers a heuristic search algorithm to look for alternative or related hypotheses. The results are analyzed in information visualization views that are linked to the volume rendering. Individual properties as well as global aggregates are visually presented to provide insight into the most relevant aspects of the generated hypotheses. This novel approach becomes computationally feasible due to a GPU implementation of the time-critical parts in the algorithm. A thorough evaluation of search times and noise sensitivity as well as a case study on data from the automotive domain substantiate the usefulness of the suggested approach.",
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"description": "Interaction example. (1) The user has the idea that attributes $a_8$ and $a_9$ describe a feature of interest. The resulting feature is shown in a rendering to the right. (2) The user runs the machine learning algorithm to search for hypotheses. The best resulting hypothesis has 65\\% fitness. The coloring (dark green) shows that attributes $a_0$ and $a_1$ are important in the entire population. (3) The user deactivates genes on other attributes and the fitness increases. (4) Via interactive visual analysis, the user improves the hypothesis. It is still unclear whether there are alternative hypotheses. (5) A larger search has found an alternative hypothesis. (6) The alternative explanation consists of two clauses combined by conjunction (AND). If one of the two clauses is missing, the feature vanishes. (7) The first clause contains the disjunction (OR) of multiple selections. Attributes $a_2$, $a_3$ select the shape \"Vis\" only. (8) Automatic local optimization via hill climbing finds the optimal selections.",
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"abstract": "Weninger et al. [25] developed a novel methodology for rapid 2D and 3D computer\r\nanalysis and visualization of gene expression patterns. The data is generated\r\nby staining a specimen followed by an iterating process of cutting thin slices and\r\ncapturing them with an episcopic microscope. The result is an high resolution\r\n3D dataset. One channel contains anatomical information and a second channel\r\ncontains the gene expression patterns.\r\nIn this thesis we examine methods for enhancing, registrating and visualizing\r\nthis novel kind of data. We address the uneven illumination of slices that are\r\nintroduced by the methodology. We developed an algorithm to fit a quadric surface\r\nthrough the background pixels to estimate the illumination situation over the\r\nwhole slice. This estimate is used to correct the slices of one dataset.\r\nFurther, an extension of this methodology was researched. Recycling the already\r\ncut sections for staining them a second time allows the medical domain\r\nscientists to augment their technique with additional information. The result of\r\nthe second data generation phase is a stack of unaligned slices. The manual processing\r\nof the sections introduces non-linear deformations. We explored several\r\nregistration algorithms to align the two image stacks. We found a two step registration\r\napproach to yield the best results. In the first step a coarse affine registration\r\nis used to approximately align the datasets. The result of the first step is inspected\r\nand if necessary corrected by the user. In the second step a b-spline registration is\r\nused that compensates for the non-linear deformations of the 2D slices.\r\nFor the visual inspection of the registration results and to present an overview\r\nof the datasets we implemented two visualization approaches. A checkerboard\r\nview is used to compare 2D slices, and a three dimensional approach based on\r\ndirect volume rendering incorporates surface enhancement by gradient magnitude\r\nopacity modulation to emphasize the alignment of tissue boundaries.",
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"title": "Interactive Visual Analysis of Relational Data and Applications in Event-Based Business Analytics",
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"abstract": "In this work, a framework for interactive visual analysis of attributed graphs has been\r\ndeveloped. An attributed graph is an extension of the standard graph of a binary\r\nrelation, which attaches a set of attributes to the nodes and edges. The implemented\r\nvisual analysis techniques aim at the local level at enabling an intuitive navigation in the\r\ngraph which reveals both the structure of the selected part of the graph and the\r\nattributes of the nodes and edges in this part. At the global level these techniques aim at\r\nunderstanding the distributions of the attributes in the graph as a whole or in specific\r\nparts in it and at spotting meaningful associations between the attributes and the\r\nrelations.\r\nThe work presents several extensions to the attributes such as graph‐theoretic features,\r\nvalues aggregated over the relations, and hierarchical grouping. All attributes are\r\ntreated in a unified manner which helps performing elaborate analysis tasks using the\r\nexisting tools.\r\nAdditionally, novel graph drawing techniques are proposed. They are designed to\r\nunderstand attribute distributions and associations in the graph. These techniques can\r\nbe additionally used to visualize results of queries in the data, which can be also visually\r\ndefined using the attribute analysis tools.\r\nFinally, the work addresses several types of association analysis in relational data, along\r\nwith visual analysis methods for them. It presents a perceptual enhancement for the\r\nwell‐known parallel sets technique for association analysis in categorical data, and\r\nproposes extensions for employing it in relational data. Also, novels methods for other\r\ntypes of association analysis are introduced.\r\nThe relational data in this work were defined upon typed events in an event‐based\r\nsystem, which offers a flexible architecture for real‐time analysis. Nevertheless, the\r\npresented analysis methods are generic and have been tested on two real‐world\r\ndatasets. In the first dataset, entities for customers and products are derived from the\r\npurchase events, and various meaningful associations were found between the attributes\r\nand the relation (for example, which types of products the female customers bought\r\nmore frequently, or at which age customers have higher interest for books). In the\r\nsecond dataset, events in an issue‐tracking system are analyzed to find out ticket\r\nassignment patterns and forwarding patterns between the support offices.",
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"id": "piringer-2009-hds",
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"title": "Hierarchical Difference Scatterplots - Interactive Visual Analysis of Data Cubes",
"date": "2009-06",
"abstract": "Data cubes as employed by On-Line Analytical Processing (OLAP) play a key role in many application domains. The analysis typically involves to compare categories of different hierarchy levels with respect to size and pivoted values. Most existing visualization methods for pivoted values, however, are limited to single hierarchy levels. The main contribution of this paper is an approach called Hierarchical Difference Scatterplot (HDS). A HDS allows for relating multiple hierarchy levels and explicitly visualizes differences between them in the context of the absolute position of pivoted values. We discuss concepts of tightly coupling HDS to other types of tree visualizations and propose the integration in a setup of multiple views, which are linked by interactive queries on the data. We evaluate our approaches by analyzing social survey data in collaboration with a domain expert.",
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