Knowledge-assisted Visualization Research Articles

Knowledge Rocks: Adding Knowledge Assistance to Visualization Systems.

We present Knowledge Rocks, an implementation strategy and guideline for augmenting visualization systems to knowledge-assisted visualization systems, as defined by the KAVA model. Visualization systems become more and more sophisticated. Hence, it is increasingly important to support users with an integrated knowledge base in making constructive choices and drawing the right conclusions. We support the effective reactivation of visualization software resources by augmenting them with knowledge-assistance. To provide a general and yet supportive implementation strategy, we propose an implementation process that bases on an application-agnostic architecture. This architecture is derived from existing knowledge-assisted visualization systems and the KAVA model. Its centerpiece is an ontology that is able to automatically analyze and classify input data, linked to a database to store classified instances. We discuss design decisions and advantages of the KR framework and illustrate its broad area of application in diverse integration possibilities of this architecture into an existing visualization system. In addition, we provide a detailed case study by augmenting an it-security system with knowledge-assistance facilities.

Knowledge-Assisted Visualization of Multi-Level Origin-Destination Flows Using Ontologies

Origin-destination matrices help understand the movement of people within cities. This work is built upon the premise that stakeholders, e.g. decision makers, need to analyze mobility flows from spatio-temporal perspectives that are appropriate to their context of analysis. The data retrieved from sensors and Intelligent Transportation Systems are useful for this purpose due to their lower acquisition costs and fine granularity, although it is complex to use such data in an integrated way, as they might have heterogeneous representations of spatio-temporal attributes and granularities. Most of the related works on the analysis of OD flows consider matrices with a fixed spatio-temporal aggregation level, and do not explore the intrinsic issue of data heterogeneity. Herein we report our findings on building the semantic foundation of knowledge-assisted visualization tools for analyzing OD matrices from multiple stakeholder levels. We propose a set of ontology design patterns for modeling the semantics of OD data, and the relations between the spatio-temporal constructs that stakeholders ought to choose when visualizing urban mobility flows. Our approach aims to be reusable by researchers and practitioners. We describe a practical implementation using estimated flows from smart card data from Porto, Portugal.

Multi-Purpose Ontology-Based Visualization of Spatio-Temporal Data: A Case Study on Silk Heritage

Due to the increasing use of data analytics, information visualization is getting more and more important. However, as data get more complex, so does visualization, often leading to ad hoc and cumbersome solutions. A recent alternative is the use of the so-called knowledge-assisted visualization tools. In this paper, we present STMaps (Spatio-Temporal Maps), a multipurpose knowledge-assisted ontology-based visualization tool of spatio-temporal data. STMaps has been (originally) designed to show, by means of an interactive map, the content of the SILKNOW project, a European research project on silk heritage. It is entirely based on ontology support, as it gets the source data from an ontology and uses also another ontology to define how data should be visualized. STMaps provides some unique features. First, it is a multi-platform application. It can work embedded in an HTML page and can also work as a standalone application over several computer architectures. Second, it can be used for multiple purposes by just changing its configuration files and/or the ontologies on which it works. As STMaps relies on visualizing spatio-temporal data provided by an ontology, the tool could be used to visualize the results of any domain (in other cultural and non-cultural contexts), provided that its datasets contain spatio-temporal information. The visualization mechanisms can also be changed by changing the visualization ontology. Third, it provides different solutions to show spatio-temporal data, and also deals with uncertain and missing information. STMaps has been tested to browse silk-related objects, discovering some interesting relationships between different objects, showing the versatility and power of the different visualization tools proposed in this paper. To the best of our knowledge, this is also the first ontology-based visualization tool applied to silk-related heritage.

An Ontology-based approach to Knowledge-assisted Integration and Visualization of Urban Mobility Data

This paper proposes an ontology-based framework to support integration and visualization of data from Intelligent Transportation Systems. These activities may be technically demanding for transportation stakeholders, due to technical and human factors, and may hinder the use of visualization tools in practice. The existing ontologies do not provide the necessary semantics for integration of spatio-temporal data from such systems. Moreover, a formal representation of the components of visualization techniques and expert knowledge can leverage the development of visualization tools that facilitate data analysis. The proposed Visualization-oriented Urban Mobility Ontology (VUMO) provides a semantic foundation to knowledge-assisted visualization tools (KVTs). VUMO contains three facets that interrelate the characteristics of spatio-temporal mobility data, visualization techniques and expert knowledge. A built-in rule set leverages semantic technologies standards to infer which visualization techniques are compatible with analytical tasks, and to discover implicit relationships within integrated data. The annotation of expert knowledge encodes qualitative and quantitative feedback from domain experts that can be exploited by recommendation methods to automate part of the visualization workflow. Data from the city of Porto, Portugal were used to demonstrate practical applications of the ontology for each facet. As a foundational domain ontology, VUMO can be extended to meet the distinctiveness of a KVT.

Visualization of Agriculture Data of Rajasthan: An Application of R

Applications of artificial intelligence, machine learning, data science, and advanced analytics are abundantly used in various areas; however, these are yet to be effectively utilized in agriculture sector in Rajasthan. There are some commonly used open source platforms to perform data processing and representation of patterns as graphs. Data visualization is a key aspect of both the analysis and understanding of these data, and users now have many visualization methods and tools to choose from. The challenge is to create clear, meaningful and integrated visualizations. This representation of data using R Language provides a quick insight into both Information-assisted and knowledge-assisted visualization for interactive exploration and can be used for analytic purpose

A knowledge-assisted visual malware analysis system: Design, validation, and reflection of KAMAS

IT-security experts engage in behavior-based malware analysis in order to learn about previously unknown samples of malicious software (malware) or malware families. For this, they need to find and categorize suspicious patterns from large collections of execution traces. Currently available systems do not meet the analysts' needs which are described as: visual access suitable for complex data structures, visual representations appropriate for IT-security experts, provision of workflow-specific interaction techniques, and the ability to externalize knowledge in the form of rules to ease the analysis process and to share with colleagues. To close this gap, we designed and developed KAMAS, a knowledge-assisted visualization system for behavior-based malware analysis. This paper is a design study that describes the design, implementation, and evaluation of the prototype. We report on the validation of KAMAS with expert reviews, a user study with domain experts and focus group meetings with analysts from industry. Additionally, we reflect on the acquired insights of the design study and discuss the advantages and disadvantages of the applied visualization methods. An interesting finding is that the arc-diagram was one of the preferred visualization techniques during the design phase but did not provide the expected benefits for finding patterns. In contrast, the seemingly simple looking connection line was described as supportive in finding the link between the rule overview table and the rule detail table which are playing a central role for the analysis in KAMAS.

A Data-Driven Approach to Hue-Preserving Color-Blending.

Color mapping and semitransparent layering play an important role in many visualization scenarios, such as information visualization and volume rendering. The combination of color and transparency is still dominated by standard alpha-compositing using the Porter-Duff over operator which can result in false colors with deceiving impact on the visualization. Other more advanced methods have also been proposed, but the problem is still far from being solved. Here we present an alternative to these existing methods specifically devised to avoid false colors and preserve visual depth ordering. Our approach is data driven and follows the recently formulated knowledge-assisted visualization (KAV) paradigm. Preference data, that have been gathered in web-based user surveys, are used to train a support-vector machine model for automatically predicting an optimized hue-preserving blending. We have applied the resulting model to both volume rendering and a specific information visualization technique, illustrative parallel coordinate plots. Comparative renderings show a significant improvement over previous approaches in the sense that false colors are completely removed and important properties such as depth ordering and blending vividness are better preserved. Due to the generality of the defined data-driven blending operator, it can be easily integrated also into other visualization frameworks.

CG&A—Past and Future Themes

When I was invited to take on the role of associate editor in chief for special issues, I knew that my predecessors, Maureen Stone and Holly Rushmeier, were going to be tough acts to follow. CGA virtual worlds, real sounds (July/August 2002); nonphotorealistic rendering (July/August 2003); haptic rendering (March April 2004); point-based graphics (July/ August 2004); and smart depiction for visual communication (May/June 2005). Over the past fi ve years, CGA see Figure 2). For July/August 2009, Norm Badler and I coedited a special issue on virtual populace, which was my fi rst introduction to editing a CG&A special issue—a most enjoyable and rewarding experience. Last year, knowledge-assisted visualization (January/February) and digital human faces (July/August) were just two of the areas we covered.

Visual Analysis of Flow Features Using Information Theory

Unsteady scientific data is one of the top challenges in visualization, because a huge amount of information must be displayed. ϵ-machines are an information-theoretic concept; they compress the dynamics in the data set to a finite-state machine, in which nodes represent local flow patterns and edges represent transitions between them. Several enhancements to the fundamental ϵ-machine representation can help users identify interesting time intervals, analyze the evolution of unusual local dynamics, and track features over time. Automatically abstracting information from the original data is a first step toward knowledge-assisted visualization. Successive findings from analysis can help provide subsequent users with knowledge gained in earlier research, resulting in a knowledge-assisted system for the analysis of unsteady-flow features based on information theory. This article is part of a special issue on knowledge-assisted visualization.

Knowledge-Assisted Visualization and Segmentation of Geologic Features using Implicit Surfaces

The proposed approach employs domain knowledge about the structure and topology of geologic features in seismic data to steer dynamic surfaces into those features. Specialized attributes guide evolving surfaces by adapting to the features' known shape. Geometric, topological, and geologic information can be rendered on the evolving surface, which lets the user infer knowledge during the process. The approach is implemented in a 3D visualization environment. A user study demonstrates this approach's ability to transfer domain knowledge to nonexperts. This article is part of a special issue on knowledge-assisted visualization.

Guest editors' introduction: knowledge-assisted visualization.

This special issue presents four articles demonstrating a first step from traditional interactive visualization to knowledge-assisted visualization. Knowledge-assisted visualization's objectives include sharing domain knowledge among different users and reducing the burden on users to acquire knowledge about complex visualization techniques. The four articles address this new challenge from different perspectives, including methodologies, systems, and applications.

Knowledge-assisted visualization and segmentation of geologic features.

A 3D visualization environment contains domain information about geologic features in seismic data and lets users interactively steer segmentations on the basis of visual sources of information and knowledge. A user study demonstrates this approach's ability to transfer domain knowledge to non experts.

A high-dimensional feature clustering approach to support knowledge-assisted visualization

The ever-growing arsenal of methods and parameters available for data visualization can be daunting to the casual user and even to domain experts. Furthermore, comprehensive expertise is often not available in a centralized venue, but distributed over sub-communities. As a means to overcome this inherent problem, efforts have begun to store visualization expertise directly with the visualization method and possibly the dataset, to then be utilized for user guidance in the data visualization, suggesting to the user both the visualization method and its best parameters for the data and task at hand. While this is certainly an immensely useful and promising development, one requirement remains – the matching of a newly acquired dataset with the appropriate segment of the library storing the expert knowledge. This requires one to detect and recognize the dataset's category at some level of granularity and then use this information as a library index. We describe a possible framework for accomplishing the first stage of this process, namely the data categorization, using data classification via a rich set of feature vectors sufficiently sensitive to detect critical variations. We demonstrate the utility of our framework by ways of a set of medical and computational datasets and visualize the resulting categorization as a layout in 2D.

Defining and applying knowledge conversion processes to a visual analytics system

Knowledge-assisted visualization has been a fast growing field because it directly integrates and utilizes domain knowledge to produce effective data visualization. However, most existing knowledge-assisted visualization applications focus on integrating domain knowledge that is tailored only for specific analytical tasks. This reflects not only the different understandings of what “knowledge” is in visualization, but also the difficulties in generalizing and reapplying knowledge to new problems or domains. In this paper, we differentiate knowledge into two types, tacit and explicit, and suggest four conversion processes between them (internalization, externalization, collaboration, and combination) that could be included in knowledge-assisted visualizations. We demonstrate the applications of these four processes in a bridge visual analytical system for the US Department of Transportation and discuss their roles and utilities in real-life scenarios.