Interactive visualization with user perspective: A new concept

With an astonishing amount of data generated for processing on a daily basic, it is essential to provide an effective methodology for understanding, reasoning and supporting decision making of large information spaces. This paper presents a new concept that provides an intelligent and interactive visualization in supporting large scale analysis. This aims to provide a much greater flexibility and control for the users to interactively customize the visualizations according to their preferences. A simple prototype is also presented to demonstrate the concept on hierarchical structures. Copyright © 2010 ACM

Teaching autonomous agents to move in a believable manner within Virtual Institutions

Abstract Believability of computerised agents is a growing area of research. This paper is focused on one aspect of believability- believable movements of avatars in normative 3D Virtual Worlds called Virtual Institutions. It presents a method for implicit training of autonomous agents in order to ”believably ” represent humans in Virtual Institutions. The proposed method does not require any explicit training efforts from human participants. The contribution is limited to the lazy learning methodology based on imitation and algorithms that enable believable movements by a trained autonomous agent within a Virtual Institution.

Assisting the Design of virtualwork processes via on-line reverse engineering

The design of virtual workplaces that can support virtual work processes has traditionally been either adhoc, or has been influenced by the virtual architecture or requirements engineering disciplines. The problem with these approaches is the difficulty in obtaining, and subsequently retaining and reusing, ready-made configurations of collaborative work processes. Such configurations naturally occur during the actual use of CVEs for conducting projects. Can we predict some elements of the evolution of a new collaborative process, based on similarities and analogies with processes formalised and supported before? Can we capture and utilise the evolutionary component in the workspace design process, so that we can provide better support to the developers of collaborative workspaces? The paper presents a new approach for supporting design and redesign of virtual workspaces, based on combining data mining techniques for refining lower level models with a reverse engineering cycle to create upper level models

Comparison of visualization methods of genome-wide SNP profiles in childhood acute lymphoblastic leukaemia

Data mining and knowledge discovery have been applied to datasets in various industries including biomedical data. Modelling, data mining and visualization in biomedical data address the problem of extracting knowledge from large and complex biomedical data. The current challenge of dealing with such data is to develop statistical-based and data mining methods that search and browse the underlying patterns within the data. In this paper, we employ several data reduction methods for visualizing genome- wide Single Nucleotide Polymorphism (SNP) datasets based on state-of-art data reduction techniques. Visualization approach has been selected based on the trustworthiness of the resultant visualizations. To deal with large amounts of genetic variation data, we have chosen to apply different data reduction methods to deal with the problem induced by high dimensionality. Based on the trustworthiness metric we found that neighbour Retrieval Visualizer (NeRV) outperformed other methods. This method optimizes the retrieval quality of Stochastic neighbour Embedding. The quality measure of the visualization (i.e. NeRV) showed excellent results, even though the dataset was reduced from 13917 to 2 dimensions. The visualization results will assist clinicians and biomedical researchers in understanding the systems biology of patients and how to compare different groups of clusters in visualizations. © 2008, Australian Computer Society, Inc

From graphs to Euclidean Virtual Worlds: Visualization of 3D Electronic Institutions

In this paper we propose an algorithm for automatic transformation of a graph into a 3D Virtual World and its Euclidean map, using the rectangular dualization technique. The nodes of the initial graph are transformed into rooms, the connecting arcs between nodes determine which rooms have to be placed next to each other and define the positions of the doors connecting those rooms. The proposed algorithm is general enough to be used for automatic generation of 3D Virtual Worlds representation of any planar graph, however, our research is particulary focused on the automatic generation of 3D Electronic Institutions from the Performative Structure graph. Copyright © 2007, Australian Computer Society, Inc

A Multi Agent Recommender System that Utilises Consumer Reviews in its Recommendations

Consumer reviews, opinions and shared experiences in the use of a product form a powerful source of information about consumer preferences that can be used for making recommendations. A novel approach, which utilises this valuable information sources first time to create recommendations in recommender agents was recently developed by Aciar et al. (2007). This paper presents a general framework of this approach. The proposed approach is demonstrated using digital camera reviews as an example

Enabling effective tree exploration using visual cues

© 2018 Elsevier Ltd This article presents a new interactive visualization for exploring large hierarchical structures by providing visual cues on a node link tree visualization. Our technique provides topological previews of hidden substructures with three types of visual cues including simple cues, tree cues and treemap cues. We demonstrate the visual cues on Degree-of-Interest Tree (DOITree) due to its familiar mapping, its capability of providing multiple focused nodes, and its dynamic rescaling of substructures to fit the available space. We conducted a usability study with 28 participants that measured completion time and accuracy across five different topology search tasks. The simple cues had the fastest completion time across three of the node identification tasks. The treemap cues had the highest rate of correct answers on four of the five tasks, although only reaching statistical significance for two of these. As predicted, user ratings demonstrated a preference for the easy to understand tree cues followed by the simple cue, despite this not consistently reflected in performance results

Understanding cancer patient cohorts in virtual reality environment for better clinical decisions: a usability study.

BACKGROUND: Visualising patient genomic data in a cohort with embedding data analytics models can provide relevant and sensible patient comparisons to assist a clinician with treatment decisions. As immersive technology is actively used around the medical world, there is a rising demand for an efficient environment that can effectively display genomic data visualisations on immersive devices such as a Virtual Reality (VR) environment. The VR technology will allow clinicians, biologists, and computer scientists to explore a cohort of individual patients within the 3D environment. However, demonstrating the feasibility of the VR prototype needs domain users' feedback for future user-centred design and a better cognitive model of human-computer interactions. There is limited research work for collecting and integrating domain knowledge into the prototype design. OBJECTIVE: A usability study for the VR prototype--Virtual Reality to Observe Oncology data Models (VROOM) was implemented. VROOM was designed based on a preliminary study among medical users. The goals of this usability study included establishing a baseline of user experience, validating user performance measures, and identifying potential design improvements that are to be addressed to improve efficiency, functionality, and end-user satisfaction. METHODS: The study was conducted with a group of domain users (10 males, 10 females) with portable VR devices and camera equipment. These domain users included medical users such as clinicians and genetic scientists and computing domain users such as bioinformatics and data analysts. Users were asked to complete routine tasks based on a clinical scenario. Sessions were recorded and analysed to identify potential areas for improvement to the data visual analytics projects in the VR environment. The one-hour usability study included learning VR interaction gestures, running visual analytics tool, and collecting before and after feedback. The feedback was analysed with different methods to measure effectiveness. The statistical method Mann-Whitney U test was used to analyse various task performances among the different participant groups, and multiple data visualisations were created to find insights from questionnaire answers. RESULTS: The usability study investigated the feasibility of using VR for genomic data analysis in domain users' daily work. From the feedback, 65% of the participants, especially clinicians (75% of them), indicated that the VR prototype is potentially helpful for domain users' daily work but needed more flexibility, such as allowing them to define their features for machine learning part, adding new patient data, and importing their datasets in a better way. We calculated the engaged time for each task and compared them among different user groups. Computing domain users spent 50% more time exploring the algorithms and datasets than medical domain users. Additionally, the medical domain users engaged in the data visual analytics parts (approximately 20%) longer than the computing domain users

Smart scalable ML-blockchain framework for large-scale clinical information sharing

Large-scale clinical information sharing (CIS) provides significant advantages for medical treatments, including enhanced service standards and accelerated scheduling of health services. The current CIS suffers many challenges such as data privacy, data integrity, and data availability across multiple healthcare institutions. This study introduces an innovative blockchain-based electronic healthcare system that incorporates synchronous data backup and a highly encrypted data-sharing mechanism. Blockchain technology, which eliminates centralized organizations and reduces the number of fragmented patient files, could make it easier to use machine learning (ML) models for predictive diagnosis and analysis. In turn, it might lead to better medical care. The proposed model achieved an improved patient-centered CIS by personalizing the separation of information with an intelligent ”allowed list“ for clinician data access. This work introduces a hybrid ML-blockchain solution that combines traditional data storage and blockchain-based access. The experimental analysis evaluated the proposed model against the competing models in comparative and quantitative studies in large-scale CIS examples in terms of model viability, stability, protection, and robustness, with improved results

A Game-Theoretical Approach to Clinical Decision Making with Immersive Visualisation

Cancer is a disease characterised by changes in combinations of genes within affected tumour cells. The deep understanding of genetic activity afforded to cancer specialists through complex genomics data analytics has advanced the clinical management of cancer by using deep machine learning algorithms and visualisation. However, most of the existing works do not integrate intelligent decision-making aids that can guide users in the analysis and exploration processes. This paper contributes a novel strategy that applies game theory within a VR-enabled immersive visualisation system designed as the decision support engine to mimic real-world interactions between stakeholders within complex relationships, in this case cancer clinicians. Our focus is to apply game theory to assist doctors in the decision-making process regarding the treatment options for rare-cancer patients. Nash Equilibrium and Social Optimality strategy profiles were used to facilitate complex analysis within the visualisation by inspecting which combination of genes and dimensionality reduction methods yields the best survival rate and by investigating the treatment protocol to form new hypotheses. Using a case simulation, we demonstrate the effectiveness of game theory in guiding the analyst with a patient cohort data interrogation system as compared to an analyst without a decision support system. Particularly, the strategy profile (t-SNE method and DNMT3B_ZBTB46_LAPTM4B gene) gains the highest payoff for the two doctors