Top PDF A Framework for Focus+Context Visualization

A Framework for Focus+Context Visualization

A Framework for Focus+Context Visualization

RELATED WORK Although the origin of focus+context visualization can be traced back to non-interactive distortion-based techniques for visualization of map data [14], the first computer-based interactive method was introduced with the FISHEYE View [8], more known as the Generalized Fisheye View [9]. This original fisheye notion was in fact a general interaction framework for information filtering according to the user’s current point of interest in the material, rather than a spe- cific visualization technique, and was shown to be applica- ble to various types of data, notably structured programs and tree structures. (Some confusion has been the result of several other techniques using the term “fisheye”, and cur- rently fisheye visualization is often more closely associated with distortion-based techniques that give the graphical impression of the fisheye-lens of a camera.) In connection with the Generalized Fisheye View, important concepts such as the Degree of Interest (DoI) function and the Level of Detail (LoD) were introduced.
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Interactive Feature Specification for Focus+Context Visualization of Complex Simulation Data

Interactive Feature Specification for Focus+Context Visualization of Complex Simulation Data

Complex and high-dimensional feature definition – when analyzing simulation data, one very often encountered prob- lem is the limited flexibility of current brushing and inter- action techniques. Brushing is usually restricted to simple combinations of individual brushes, as well as missing sup- port of high-dimensional brushes due to the tight coupling of GUI interactions and the representation of the brush data itself. For fast and flexible analysis of the usually large and high-dimensional simulation data, complex and also high- dimensional brushes are necessary. In this paper, we present a formal framework, that is very closely coupled to the data, allowing to define and handle such brushes interactively. Linking multiple views – the combination of InfoViz and SciViz methods 7 4 , especially for the interactive visualiza- tion and analysis of simulation data, improves the under- standing of the data in terms of their high-dimensional char- acter as well as the data relation to the spatial layout. Link- ing several views 2 to interactively update all changes of the data analysis process in all views simultaneously is a cru- cial property for making optimal use of multiple (different) visualization views.
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Lived experience and discursive context: A twin focus

Lived experience and discursive context: A twin focus

Overlapping with the process of articulating the two strands of research questions for the empirical project are considerations and articulations at the level of epistemology. Here, the researcher needs to remain acutely aware of the epistemological assumptions of IPA and FDA and how these inform their respective analytic foci (see earlier discussions for outline). A third procedural milestone requires the researcher to establish the direction in which they wish to go in preparing to collect data. As with any qualitative work, the epistemological position and theoretical framework adopted influences the formulation of the interview questions as well as the interview style. In terms of interview structure, in the case of the first author’s PhD, the selection of questions emerged from a commitment to remain open and curious towards the participants’ voice and aware of the researcher’s own intellectual and personal assumptions, and, as much as is possible, keeping these bracketed. The set of questions devised to guide but not restrict the interview conversations thus featured open-ended questions which tried to tap into the analytic foci of both FDA and IPA. For example questions such as ‘What do you understand by the term relationship satisfaction?’ were followed by ‘How do you know when you’re satisfied? What does it feel like?’. The aim was to encourage as much narrative, and as much reflection as possible, and to prompt the participants to describe their internal life-world, whilst noting points of tension in their accounts. This process required active engagement with the interview process and was challenging because of the simultaneous needs to be relaxed enough to allow free narrative, whilst also remaining curious and aware of potential moments of segueing into novel areas, or contradiction with what had already been said, as these moments hold high potential for both IPA and FDA interests. It required constant reflection by the interviewer on what had been covered, and the extent to which the analytic foci and questions had been addressed.
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Adaptive Mobile Visualization – The Chameleon Framework

Adaptive Mobile Visualization – The Chameleon Framework

The Context Information Retrieval Modules are responsible to define the access to the different sources of contextual information. They communicate directly with these sources and, for this reason, consist mainly on low level contexts. Since the types of communication can differ greatly, we divided these modules into five groups, each of which communicate with different types of sources of contextual information: internal databases in which historical records are stored, a set of dynamic files that contain personalization information and information about the user’s current task, fairly static files created in installation time and configuration, a set of hardware sensors integrated with the mobile device and, a set of context servers available by using a wireless data connection. These modules are also responsible for communicating updates of the context, either at specified intervals, or whenever the context is changed.
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Mondrian: An Agile Information Visualization Framework

Mondrian: An Agile Information Visualization Framework

mentation to a particular interface. That is why, the Figure talks to the Object through the Shape which acts like a translator between the visualization model and the data model. We can have several shapes (e.g., Rectangle, Line), and depending on the Shape we can specify how to compute a certain visual characteristic via a closure. For example, to a Rectangle we can specify how to compute the width, height and color. As mentioned in the previous section, in Smalltalk, closures are first class objects that can be passed around and get executed with a specified context. Closures can be simu- lated in Java using anonymous classes that implement a command. A similar problem was addressed and solved in SWT.
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A Framework for Creative Visualization-Opportunities Workshops

A Framework for Creative Visualization-Opportunities Workshops

domain knowledge needed to effectively design the workshop. Also, our collaborators were too busy to meet with us before the workshop, which should have been a warning about the nature of the project. Ac- cordingly, we recommend researchers evaluate the preconditions of design studies [81] in projects where they are considering workshops. We also recognize that workshops may not be well received by all stakeholders. In a full day workshop [P4], one participant reported that “Overall, it was good, but a bit long and slightly repetitive.” Similarly, after another full day workshop [P5], one participant said “there was too much time spent expanding and not enough focus ... discussions were too shallow and non-specific.” However, we can improve work- shops based on this feedback, perhaps by ensuring that the methods are closely related to the topic, and that we facilitate workshops in a way that provides appropriate agency. Nevertheless, both of these workshops were generally well received by stakeholders, allowed us to explore a wide space of visualization opportunities, and to moved the collaborations forward in new and interesting ways.
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A Framework for Creative Visualization-Opportunities Workshops

A Framework for Creative Visualization-Opportunities Workshops

domain knowledge needed to effectively design the workshop. Also, our collaborators were too busy to meet with us before the workshop, which should have been a warning about the nature of the project. Ac- cordingly, we recommend researchers evaluate the preconditions of design studies [81] in projects where they are considering workshops. We also recognize that workshops may not be well received by all stakeholders. In a full day workshop [P4], one participant reported that “Overall, it was good, but a bit long and slightly repetitive.” Similarly, after another full day workshop [P5], one participant said “there was too much time spent expanding and not enough focus ... discussions were too shallow and non-specific.” However, we can improve work- shops based on this feedback, perhaps by ensuring that the methods are closely related to the topic, and that we facilitate workshops in a way that provides appropriate agency. Nevertheless, both of these workshops were generally well received by stakeholders, allowed us to explore a wide space of visualization opportunities, and to moved the collaborations forward in new and interesting ways.
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Focus and Context Methods for Particle-Based Data

Focus and Context Methods for Particle-Based Data

Using Focus+Context together with particle visualization has several advantages. First, the overall visual complexity is lowered. By allowing the user to visually focus on a manageable subset of the data, the visualization as a whole is effective. Second, complex and hidden structures become visible. Especially in particle data, dense structures cause occlusion or completely envelope relevant structures. For example, focussing on inner, otherwise occluded structures, makes them, by definition of a Focus+Context visualization, visible which allows gaining new insights. Third, since the context has looser constraints on expressiveness and accuracy, the design space for new visualizations is enlarged. The context needs to convey the information of its represented data only qualitatively. Note that of course a context visualization must remain meaningful, that means, it may not represent the data in a way that is implausible in the domain of the data’s origin. For example, reconstructing a Metaball surface from particles that repre- sent grains of sand might be a doubtable choice. Fourth, Focus+Context can be adapted to existing visualization approaches to lift their scalability. In essence, Focus+Context means two visualizations in one image. Although both visualiza- tions are ideally carried out using the same scheme, it is not mandatory. This means, existing methods can be declared to be either a focus or a context method, and only need a complementing second visualization. For example, instead of being forced to give up on a complex and visually demanding visualization for a large dataset, it can still be used to only show a selection of the data, while a perceptually simpler accompanying visualization shows the rest.
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An interactive visualization framework for performance analysis

An interactive visualization framework for performance analysis

Notice that the rms definition, which is based on tracing low-level memory accesses made by the program, supports memory dereferencing and pointers in a natural way. How- ever, the rms metric ignores any communication between threads and data received via system calls from the OS ker- nel, failing to accurately characterize the behavior of rou- tines executed in the context of modern concurrent and in- teractive applications. A more recent work [6] has extended the rms metric in order to include dynamic input sources such as communication between threads and I/O. For the sake of presentation, in this paper we refer to the original metric, but any consideration can be naturally applied to the latter extension.
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Interactive Context-Aware Visualization for Mobile Devices

Interactive Context-Aware Visualization for Mobile Devices

3.3 Context-Aware Visualization and Real-Time Adaptation For the selection process of appropriate visualization techniques the amount of data and possibly other aspects, e.g. spatial distribution of the individual data elements, might also be relevant. To consider these aspects in the selection process, all techniques in the list of possible visualizations are provided with raw data from the stream of data objects, generated by a query to the AWM. Afterwards, a combination of per-client context information and properties of each technique—e.g. the amount of data objects the method can visualize simultaneously—are used to decide which visualization is fi- nally selected as most appropriate for the current situation. This process is summarized as Context-Based Technique Selection in Figure 2. The necessary per-client context is made available via an aggregation of data provided by the framework (tracking system, web services, etc.) and data from local sensors (camera image, GPS location, etc.) of the connected client. In order to support real-time context information, both sources are synchronized to build the per-client context that is consistently available on the server and client. The following Preprocessing step (see Figure 2) executes the selected tem- plate’s function to prepare the data and, dependent on the context, transmits optimized intermediate visualization data or an image stream to the client.
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A Visualization Framework for Designing Process Mining Diagrams

A Visualization Framework for Designing Process Mining Diagrams

Munzner presents data visualization process as a nested model, where the output of one layer is an input to another [37]. The four layers are domain situation, data/task abstraction, visual encoding/interaction idiom and algorithm [37]. The question “how to visualize?”, which is the interest of the framework of this thesis, lays in the third layer – visual encoding and interaction idiom. Munzner breaks that question down to several sub-questions and each of those sub-questions have their own sub-questions forming an hierarchical tree of design decisions [37]. Some, but not all questions are answered with possible solutions, theory behind the preferred options and specific examples from the data visualization field. The theory provides a method to analyze any data visualization with any scope and content. This ambition makes it a good guidance for designers, who want to expand their awareness of different visualization possibilities for various purposes and fields. It is not a good source for developers, who want to design a diagram for a specific purpose and context, such as process mining. The spectrum is wide and there is too much irrelevant information if one is searching help for a specific task. Going through all the material before composing a single
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DEMONSTRATION OF THE SOFTVISION SOFTWARE VISUALIZATION FRAMEWORK

DEMONSTRATION OF THE SOFTVISION SOFTWARE VISUALIZATION FRAMEWORK

the user to customize and extend its functionality to produce effective visualizations for specific domains and data sets. So far, SoftVision has been used to visualize reverse engineering data (Telea et al., 2002a,b; Telea, 2004), the Internet resource metadata language RDF (Resource Description Framework) (Telea et al., 2003), and component-based software systems (Voinea and Telea, 2004). SoftVision’s architecture and design philosophy are described in detail by Telea et al. (2002a,b). The toolkit is also available for download with its manual at a website (Voinea, 2004). In this paper, we focus on demonstrating the flexibility of SoftVision for building visualizations for reverse engineering applications. First, we give a short description of the main concepts SoftVision is based on, i.e. its data and operation model (Section 2). In Section 3, we present a number of reverse engineering visualization scenarios we have constructed using SoftVision and detail the steps we took to customize the generic framework provided by SoftVision to achieve our specific goals. Our presentation of these scenarios has two aims. First, we illustrate and advocate the effectiveness of a visual exploration tool for understanding architectural data. Second, by detailing the steps we took in constructing our visualizations, we give an indication of the (small) amount of effort needed to achieve such results when using the SoftVision toolkit. Finally, we draw out conclusions in Section 4.
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An Operator Interaction Framework for Visualization Systems

An Operator Interaction Framework for Visualization Systems

2 Related Work Many have observed the intricate relationship between the view and the value associated with that view. In particular, a good observa- tion was made in [3] that when using a brushing technique with a group of scatterplots, the effect of an operation on a data point ap- pears simultaneously on all scatterplots in the other views. They termed this “coordinated”. This is a simple, yet–powerful, notion of view and value, where the view is always tied to the underlying value. This binding is never broken. While this model does not take multiple data sources into account, the advantage is that the user has a very concise and clear model of how the system works. The dis- advantage is that the other opposing semantic is impossible, which is that the user may just want to temporarily change one view, but not all of them. For example, the user may simply want to select a group of data points in one particular view to highlight it to discuss it outside the context of other scatter plots.
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Framework for software architecture visualization assessment.

Framework for software architecture visualization assessment.

Context should also be maintained when switching between views so as to reduce disorientation. Along with data-space navigation, the movement within a view is also important. Schneiderman’s mantra for vi- sualisation is overview first, zoom and filter, and then show details on demand [12]. A visualisation system should support this strategy. Also, the visualisation should allow the user to move around so as to focus on and see the information they are looking for. 3.2.5 Task Support (TS)

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A framework for software architecture visualization assessment.

A framework for software architecture visualization assessment.

Context should also be maintained when switching between views so as to reduce disorientation. Along with data-space navigation, the movement within a view is also important. Schneiderman’s mantra for vi- sualisation is overview first, zoom and filter, and then show details on demand [12]. A visualisation system should support this strategy. Also, the visualisation should allow the user to move around so as to focus on and see the information they are looking for. 3.2.5 Task Support (TS)

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Representing Context: Presupposition Triggers and Focus-sensitivity

Representing Context: Presupposition Triggers and Focus-sensitivity

tions and shrink the Context Set by eliminating any possible worlds in which this proposition was not true. A well-known formalization of these ideas based on Stalnaker ( 1978 ) comes from Heim ( 1982 , 1983 , 1992 ). In her framework, the meaning of a sentence is its Context Change Potential (CCP), namely a function from contexts to contexts, with a context being a set of possible worlds. To illustrate how a context gets updated, consider someone asserting the proposition It is raining. On the assumption that the initial context is empty - the Common Ground is the empty set and the Con- text Set is the set of all possible worlds W - the context would be updated with this assertion to contain only those worlds in which it rains, as in (2.12) . That is, the con- text gets intersected with the asserted proposition. Presuppositions then serve as requirements on the context, allowing an update to proceed only in those contexts that entail the presupposition. An example for the presupposition of a possessive - that Emma owns a cat - is given in (2.13) . Frameworks of this sort - as the one by van der Sandt ( 1992 ) discussed further below - belong to the class of dynamic semantics - in contrast to the static one seen earlier - since they are concerned with how information accumulates as a discourse progresses.
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How To Create A Visualization Framework For Qualitative Researchers

How To Create A Visualization Framework For Qualitative Researchers

Qualitative research has always been an important mode of enquiry for social and human science exploration. There is also an increasing focus on qualitative methods of inquiry in multidisciplinary and cross disciplinary research areas in order to capture broader aspects of human and social behavior. Qualitative research also has a very broad range of enquiry methods, huge quantities of data, complex relationships between the researchers and participants, intricate theories and reflexive accounts. Exploring, analyzing, synthesizing these intricate maze of data requires understanding these complexities in detail and the multiple perspectives from which it evolves. To facilitate some of these complexities, certain computer assisted qualitative data analysis software (CAQDAS) have evolved from the early 1990s which enable qualitative
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INTERACTIVE FOCUS+CONTEXT GLYPH AND STREAMLINE VECTOR VISUALIZATION

INTERACTIVE FOCUS+CONTEXT GLYPH AND STREAMLINE VECTOR VISUALIZATION

Focus points can be generated and placed automatically based on the data by pre- computing possible critical points using a method presented by Effenberger et al. [6] By analyzing each cell of the vector field, and determining in what way the sign of the vector field in each component (x or y) changes along the edges of the cells, possible critical points can be found. As long as the focus point’s radius is larger than the size of the cell, simply determining if a cell can have one or more critical points is sufficient and placing a focus point in the center of the cell will ensure that the critical points are all contained within a focus point. No further computation to determine the number or type of critical points is needed. These checks can be optimized by creating a key for each possible configuration case and performing a lookup in a pre-computed table with flags that state if a cell holds at least one critical point or not.
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Illustrative Focus+Context Approaches in Interactive Volume Visualization

Illustrative Focus+Context Approaches in Interactive Volume Visualization

A recent trend in volume visualization is that researchers tend to use traditional illus- trations as an inspiration for their work. As the domain of scientific illustration is based on centuries of experience in the depiction of complex volumetric structures, it represents a valuable source for visualization researchers. A common technique found in many traditional illustrations (see Figure 1) is referred to as focus+context in visualization literature. As there is often not enough space available to display all information in sufficient detail, the general idea is to emphasize regions of particular interest (focus) without completely removing other information important for orientation (context). Moreover, focus+context visualizations are not only motivated by space limitations but also by human visual perception. People are capable of simultaneously perceiving both local detail and global context [46]. Focus+context methods make it possible to show more detailed or targeted information and at the same
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Focus and context for volume visualization

Focus and context for volume visualization

Chapter 2 25 Volume Visualization 2.8 Splatting Splatting is a novel object-order algorithm proposed in 1990 by Westover [168], based on the idea of computing the contribution of each voxel to the final image, and traversing the dataset in a front to back order - hence the algorithm starts from the face of the volume cube which is closest to the observer, and works with parallel slices to that face. The name “splatting” comes from the fact that the procedure can be compared to throwing a snow ball at a glass pane - the ball will spread from the point of impact, where the contribution is highest. Each scalar value (voxel) is first classified through application of the transfer function - the resulting colour and opacity can be shaded by e.g. gradient shading (see section 2.10.3). The next step is the splatting itself: the voxel is projected onto the image plane and a reconstruction kernel (a circular Gaussian) is used to calculate the magnitude of the contribution. This projection is called a footprint, and it is scaled proportionally to the size of the volume and to the size of the image plane, and then placed at the centre of the projected voxel in the image plane. This footprint can be pre- calculated and stored in a table for lookup during the actual rendering - this allows the method to be faster than this description suggests. The final step is to blend the current voxel with the image plane at every pixel within the footprint area, using the colour and opacity (from classification) attenuated by the value of the Gaussian at that specific pixel centre. However, this algorithm can produce bleeding artifacts from hidden objects, as the volume rendering integral has been essentially reordered.
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