Abstract-Recently there has been increasing research interest in displaying graphs with curved edges to produce more readable visualizations. While there are several automatic techniques, little has been done to evaluate their effectiveness empirically. In this paper we present two experiments studying the impact of edge curvature on graph readability. The goal is to understand the advantages and disadvantages of using curved edges for common graph tasks compared to straight line segments, which are the conventional choice for showing edges in node-link diagrams. We included several edge variations: straight edges, edges with different curvature levels, and mixed straight and curved edges. During the experiments, participants were asked to complete network tasks including determination of connectivity, shortest path, node degree, and common neighbors. We also asked the participants to provide subjective ratings of the aesthetics of different edge types. The results show significant performance differences between the straight and curved edges and clear distinctions between variations of curved edges.
A spatial k-NN query returns k nearest points in a point dataset to a given query point. To measure the distance between two points, most of the literature focuses on the Euclidean distance or the network distance. For many applications, such as wildlife movement, it is necessary to consider the surface distance, which is computed from the shortest path along a terrain surface. In this paper, we investigate the problem of efficient surface k-NN (sk-NN ) query processing. This is an important yet highly challenging problem because the underlying environment data can be very large and the computational cost of finding the shortest path on a surface can be very high. To minimize the amount of surface data to be used and the cost of surface distance computation, a multiresolution surface distance model is proposed in this paper to take advantage of monotonic distance changes when the distances are computed at different resolution levels. Based on this innovative model, sk-NN queries can be processed efficiently by accessing and processing surface data at a justenough resolution level within a just-enough search region. Our extensive performance evaluations using real world datasets confirm the efficiency of our proposed model.
This paper describes the GEOMI system, a visual analysis tool for the visualisation and analysis of large and complex networks. GEOMI provides a collection of network analysis methods, graph layout algorithms and several graph navigation and interaction methods. GEOMI is part of a new generation of visual analysis tools combining graph visualisation techniques with network analysis methods. GEOMI is available from http://www.cs.usyd.edu.au/ ∼ visual/valacon/geomi/.
This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractSensemaking is a process of find meaning from information, and often involves activities such as information foraging and hypothesis generation. It can be valuable to maintain a history of the data and reasoning involved, commonly known as provenance information. Provenance information can be a resource for "reflection-in-action" during analysis, supporting collaboration between analysts, and help trace data quality and uncertainty through analysis process. Currently, there is limited work of utilizing analytic provenance, which captures the interactive data exploration and human reasoning process, to support sensemaking. In this article, we present and extend the research challenges discussed in a IEEE VIS 2014 workshop in order to provide an agenda for sensemaking analytic provenance.Keywords. Provenance, Senesmaking, Visual Analytics, Collaboration, Data Quality.Sensemaking is a process of finding meaning from information -a process of comprehension. It is the construction, elaboration and reconciliation of representations which account for and explain the information we receive about the world. Sensemaking often involves a variety of activities such as information foraging and triage, schematization, and hypothesis generation and validation. During complex sensemaking tasks, it can be valuable to maintain a history of the data and reasoning involved and the context within which sensemaking was performed -referred to as provenance information. Provenance information can be a resource for "reflection-in-action" during analysis, supporting collaboration between analysts, and help trace data quality and uncertainty through analysis process. It can also act as a resource after the event, supporting the interpretation of claims, audit, accountability, and training.There has been considerable work on capturing and visualizing data provenance, which focuses on data collection and computation, and analytic provenance, which captures the interactive data exploration and human reasoning process. However, there is limited work of utilizing such provenance information to support sensemaking, in terms of improving efficacy and avoiding pitfalls such as uncertainty and human bias. A workshop was held during IEEE VIS 2014 with the aim of bringing together researchers involved in visual analytics
There is fast‐growing literature on provenance‐related research, covering aspects such as its theoretical framework, use cases, and techniques for capturing, visualizing, and analyzing provenance data. As a result, there is an increasing need to identify and taxonomize the existing scholarship. Such an organization of the research landscape will provide a complete picture of the current state of inquiry and identify knowledge gaps or possible avenues for further investigation. In this STAR, we aim to produce a comprehensive survey of work in the data visualization and visual analytics field that focus on the analysis of user interaction and provenance data. We structure our survey around three primary questions: (1) WHY analyze provenance data, (2) WHAT provenance data to encode and how to encode it, and (3) HOW to analyze provenance data. A concluding discussion provides evidence‐based guidelines and highlights concrete opportunities for future development in this emerging area. The survey and papers discussed can be explored online interactively at https://provenance-survey.caleydo.org.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.