Interactive Graph Visualization Research Articles

Using provenance and replay for qualitative analysis of gameplay sessions

There is an increasing interest to use game telemetry for analyzing gameplay sessions, with numerous techniques created to help game developers analyze different game aspects like game balancing and behavioral analysis. Among different gameplay session analysis techniques, the collection of provenance data has stood out due to a crucial advantage of this approach: the possibility to identify cause–effect relationships between game events. In previous work, we presented our conceptual framework called Prov-Replay, which provides a replay synchronized with an interactive provenance graph visualization. We validate Prov-Replay by creating PinGU Replay, a tool that implements our conceptual framework and applied it in a commercial game. Due to the promising results, this paper extends our previous work by presenting a detailed overview about Prov-Replay implementation, introducing a new feature that provides an analysis dashboard, and applying our experiment methodology to a new commercial game. We also enhance the concept of analytics related to provenance through the replay pipeline. Finally, we made PinGU Replay available as open-source software. Our new experiment results reinforce that, when using our conceptual framework fundamentals, it is possible to improve the efficiency and effectiveness of qualitative analysis process.

An Interactive Augmented Reality Graph Visualization for Chinese Painters

Recent research in the area of immersive analytics demonstrated the utility of augmented reality for data analysis. However, there is a lack of research on how to facilitate engaging, embodied, and interactive AR graph visualization. In this paper, we explored the design space for combining the capabilities of AR with node-link diagrams to create immersive data visualization. We first systematically described the design rationale and the design process of the mobile based AR graph including the layout, interactions, and aesthetics. Then, we validated the AR concept by conducting a user study with 36 participants to examine users’ behaviors with an AR graph and a 2D graph. The results of our study showed the feasibility of using an AR graph to present data relations and also introduced interaction challenges in terms of the effectiveness and usability with mobile devices. Third, we iterated the AR graph by implementing embodied interactions with hand gestures and addressing the connection between the physical objects and the digital graph. This study is the first step in our research, aiming to guide the design of the application of immersive AR data visualization in the future.

Анализ и визуализация плагиата исходного кода в практических курсах по программированию

The problem of unfair borrowing in the academic environment is still relevant. Unfair borrowing, or plagiarism, is found today in various forms of academic activity, ranging from semester papers of students to dissertations of scientists. The development of communications and the global nature of interaction have led to a wide availability of materials that are easy to copy. This leads to the fact that it becomes easier for students to find a solution than to compose it. A separate problem is the unfair borrowing in the works of students of educational institutions, which they perform in the framework of practical programming courses. As in the case of text, it is possible to detect plagiarism manually only in the smallest subsamples of data. Fortunately, today there are quite a large number of systems that allow you to automatically identify the similarity of the source code. Moreover, there are tools that allow you to aggregate the results of the search for plagiarism by several different systems, which also increases the likelihood of detecting cases of unfair borrowing. At the same time, the use of these tools is still not so widespread in educational institutions. This article describes the process of plagiarism analysis built for use in practical programming courses, as well as a tool for interactive graph visualization of the results of plagiarism analysis.

Bringing the Security Analyst into the Loop: From Human‐Computer Interaction to Human‐Computer Collaboration

This case study examines how one Artificial Intelligence (AI) security software team made the decision to abandon a core feature of the product – an interactive Knowledge Graph visualization deemed by prospective buyers as “cool,” “impressive,” and “complex” – in favor of one that its users – security analysts – found easier to use and interpret. Guided by the results of ethnographic and user research, the QRadar Advisor with Watson team created a new knowledge graph (KG) visualization more aligned with how security analysts actually investigate potential security threats than evocative of AI and “the way that the internet works.” This new feature will be released in Q1 2020 by IBM and has been adopted as a component in IBM's open‐source design system. In addition, it is currently being reviewed by IBM as a patent application submission. The commitment of IBM and the team to replace a foundational AI component with one that better aligns to the mental models and practices of its users represents a victory for users and user‐centered design, alike. It took designers and software engineers working with security analysts and leaders to create a KG representation that is valued for more than its role as “eye candy.” This case study thus speaks to the power of ethnographic research to embolden product teams in their development of AI applications.Dominant expressions of AI that reinforce the image of AI as autonomous “black box” systems can be resisted, and alternatives that align with the mental models of users proposed. Product teams can create new experiences that recognize the co‐dependency of AI software and users, and, in so doing, pave the way for designing more collaborative partnerships between AI software and humans.

Beyond patent analytics: Insights from a scientific and technological data mashup based on a case example

Although access to open and commercial digital sources is easily available thanks to the proliferation of the internet, R&D departments still face the challenge of how to analyze information from several sources. This paper addresses this issue specifically when technological and scientific information needs to be analyzed in an integrated manner. 12,577 families of patents, 2601 scientific papers and 706 news articles are combined, normalized and analyzed using their own metadata and text. A software tool is used to extract insights from semi-structured and unstructured data by means of text mining. Additionally, interactive force-directed graph visualization is employed to show the multiple relations of concepts during different time periods with regard to the entire technology ecosystem. Through a case study of 3D printing technology, this paper shows how to apply mashup and obtain the benefits, and it defines the challenges of using interactive visualization representation.

The Network Data Repository with Interactive Graph Analytics and Visualization

NetworkRepository (NR) is the first interactive data repository with a web-based platform for visual interactive analytics. Unlike other data repositories (e.g., UCI ML Data Repository, and SNAP), the network data repository (networkrepository.com) allows users to not only download, but to interactively analyze and visualize such data using our web-based interactive graph analytics platform. Users can in real-time analyze, visualize, compare, and explore data along many different dimensions. The aim of NR is to make it easy to discover key insights into the data extremely fast with little effort while also providing a medium for users to share data, visualizations, and insights. Other key factors that differentiate NR from the current data repositories is the number of graph datasets, their size, and variety. While other data repositories are static, they also lack a means for users to collaboratively discuss a particular dataset, corrections, or challenges with using the data for certain applications. In contrast, NR incorporates many social and collaborative aspects that facilitate scientific research, e.g., users can discuss each graph, post observations, and visualizations.

Disassembly Sequence Evaluation: A User Study Leveraging Immersive Computing Technologies

As interest in product recovery, reuse, and recycling rises, planning and evaluating disassembly sequences are becoming increasingly important. The manner in which a product can be taken apart strongly influences end-of-life (EOL) operations and costs. Early disassembly planning can also inform non-EOL processes including repair and routine maintenance. Recently, research has concentrated on creating optimization algorithms which automatically generate disassembly sequences. These algorithms often require data that are unavailable or estimated with high uncertainty. Furthermore, industries often employ Cad modeling software to evaluate disassembly sequences during the design stage. The combination of these methods result in mathematically generated solutions, however, the solutions may not account for attributes that are difficult to quantify (human interaction). To help designers better explore and understand disassembly sequence opportunities, the research presented in this paper combines the value of mathematical modeling with the benefits of immersive computing technologies (ICT) to aid in early design decision making. For the purposes of this research, an ICT application was developed. The application displays both 3D geometry of a product and an interactive graph visualization of existing disassembly sequences. The user can naturally interact with the geometric models and explore sequences outlined in the graph visualization. The calculated optimal path can be highlighted allowing the user to quickly compare the optimal sequence against alternatives. The application has been implemented in a three wall immersive projection environment. A user study involving a hydraulic pump assembly was conducted. The results suggest that this approach may be a viable method of evaluating disassembly sequences early in design.

Analysis and visualisation of RDF resources in Ondex

An increasing number of biomedical resources provide their information on the Semantic Web and this creates the basis for a distributed knowledge base which has the potential to advance biomedical research [1]. This potential, however, cannot be realized until researchers from the life sciences can interact with information in the Semantic Web. In particular, there is a need for tools that provide data reduction, visualization and interactive analysis capabilities.Ondex is a data integration and visualization platform developed to support Systems Biology Research [2]. At its core is a data model based on two main principles: first, all information can be represented as a graph and, second, all elements of the graph can be annotated with ontologies. This data model conforms to the Semantic Web framework, in particular to RDF, and therefore Ondex is ideally positioned as a platform that can exploit the semantic web.The Ondex system offers a range of features and analysis methods of potential value to semantic web users, including:- An interactive graph visualization interface (Ondex user client), which provides data reduction and representation methods that leverage the ontological annotation.- A suite of importers from a variety of data sources to Ondex (http://ondex.org/formats.html)- A collection of plug-ins which implement graph analysis, graph transformation and graph-matching functions.- An integration toolkit (Ondex Integrator) which allows users to compose workflows from these modular components- In addition, all importers and plug-ins are available as web-services which can be integrated in other tools, as for instance Taverna [3].The developments that will be presented in this demo have made this functionality interoperable with the Semantic Web framework. In particular we have developed an interactive importer, based on SPARQL that allows the query-driven construction of datasets which brings together information from different RDF data resources into Ondex.These datasets can then be further refined, analysed and annotated both interactively using the Ondex user client and via user-defined workflows. The results of these analyses can be exported in RDF, which can be used to enrich existent knowledge bases, or to provide application-specific views of the data. Both importer and exporter only focus on a subset of the Ondex and RDF data models, which are shared between these two data representations [4].In this demo we will show how Ondex can be used to query, analyse and visualize Semantic Web knowledge bases. In particular we will present real use cases focused, but not limited to, resources relevant to plant biology.We believe that Ondex can be a valid contribution to the adoption of the Semantic Web in Systems Biology research and in biomedical investigation more generally. We welcome feedback on our current import/export prototype and suggestions for the advancement of Ondex for the Semantic Web. 1. Ruttenberg, A. et. al.: Advancing translational research with the Semantic Web, BMC Bioinformatics, 8 (Suppl. 3): S2 (2007).2. Köhler, J., Baumbach, J., Taubert, J., Specht, M., Skusa, A., Ruegg, A., Rawlings, C., Verrier, P., Philippi, S.: Graph-based analysis and visualization of experimental results with Ondex. Bioinformatics 22 (11):1383-1390 (2006).3. Rawlings, C.: Semantic Data Integration for Systems Biology Research, Technology Track at ISMB’09, http://www.iscb.org/uploaded/css/36/11846.pdf (2009).4. Splendiani, A. et. al.: Ondex semantic definition, (Web document) http://ondex.svn.sourceforge.net/viewvc/ondex/trunk/doc/semantics/ (2009).

Analysis and visualisation of RDF resources in Ondex

AbstractAn increasing number of biomedical resources provide their information on the Semantic Web and this creates the basis for a distributed knowledge base which has the potential to advance biomedical research [1]. This potential, however, cannot be realized until researchers from the life sciences can interact with information in the Semantic Web. In particular, there is a need for tools that provide data reduction, visualization and interactive analysis capabilities.Ondex is a data integration and visualization platform developed to support Systems Biology Research [2]. At its core is a data model based on two main principles: first, all information can be represented as a graph and, second, all elements of the graph can be annotated with ontologies. This data model conforms to the Semantic Web framework, in particular to RDF, and therefore Ondex is ideally positioned as a platform that can exploit the semantic web.The Ondex system offers a range of features and analysis methods of potential value to semantic web users, including:- An interactive graph visualization interface (Ondex user client), which provides data reduction and representation methods that leverage the ontological annotation.- A suite of importers from a variety of data sources to Ondex (http://ondex.org/formats.html)- A collection of plug-ins which implement graph analysis, graph transformation and graph-matching functions.- An integration toolkit (Ondex Integrator) which allows users to compose workflows from these modular components- In addition, all importers and plug-ins are available as web-services which can be integrated in other tools, as for instance Taverna [3].The developments that will be presented in this demo have made this functionality interoperable with the Semantic Web framework. In particular we have developed an interactive importer, based on SPARQL that allows the query-driven construction of datasets which brings together information from different RDF data resources into Ondex.These datasets can then be further refined, analysed and annotated both interactively using the Ondex user client and via user-defined workflows. The results of these analyses can be exported in RDF, which can be used to enrich existent knowledge bases, or to provide application-specific views of the data. Both importer and exporter only focus on a subset of the Ondex and RDF data models, which are shared between these two data representations [4].In this demo we will show how Ondex can be used to query, analyse and visualize Semantic Web knowledge bases. In particular we will present real use cases focused, but not limited to, resources relevant to plant biology.We believe that Ondex can be a valid contribution to the adoption of the Semantic Web in Systems Biology research and in biomedical investigation more generally. We welcome feedback on our current import/export prototype and suggestions for the advancement of Ondex for the Semantic Web.

Interaction Techniques for Selecting and Manipulating Subgraphs in Network Visualizations

We present a novel and extensible set of interaction techniques for manipulating visualizations of networks by selecting subgraphs and then applying various commands to modify their layout or graphical properties. Our techniques integrate traditional rectangle and lasso selection, and also support selecting a node's neighbourhood by dragging out its radius (in edges) using a novel kind of radial menu. Commands for translation, rotation, scaling, or modifying graphical properties (such as opacity) and layout patterns can be performed by using a hotbox (a transiently popped-up, semi-transparent set of widgets) that has been extended in novel ways to integrate specification of commands with 1D or 2D arguments. Our techniques require only one mouse button and one keyboard key, and are designed for fast, gestural, in-place interaction. We present the design and integration of these interaction techniques, and illustrate their use in interactive graph visualization. Our techniques are implemented in NAViGaTOR, a software package for visualizing and analyzing biological networks. An initial usability study is also reported.

A multi-graph approach to complexity management in interactive graph visualization

In this paper we describe a new, multi-graph approach for development of a comprehensive set of complexity management techniques for interactive graph visualization tools. This framework facilitates efficient implementation of management of multiple associated graphs with navigation links and nesting of graphs as well as ghosting, folding and hiding of unwanted graph elements. The theoretical analyses show that the involved data structures and operations on them are quite efficient, and an implementation in a graph drawing tool has proven to be successful.