TGVizTab: An ontology visualisation extension for Protégé

Ontologies are gaining a lot of interest and many are being developed to provide a variety of knowledge services. There is an increasing need for tools to graphically and in-teractively visualise such modelling structures to enhance their clarification, verification and analysis. Protégé 2000 is one of the most popular ontology modelling tools currently available. This paper introduces TGVizTab; a new Protégé plugin based on TouchGraph technology to graphically visualise Protégé?s ontologies

RSS FEED VISUALIZATION

This paper describes how the web content visualization can be greatly improved using the modeling technique. Web content visualization is the outcome of effort made to avail an improved 3D visualization unlike the 2D web content visualization at present. Web page navigation in this case will be depicted by a 2D graph and the web content will be visualized in the form of 3D graph. Also the RSS feeds will be visualized in the form of 3D graph. In normal browser we type name of the URL in the address bar and that URL is downloaded. But the 3D browser takes any URL as an input and generates a 3D graph of the whole website. When we type the URL, a root node of this URL is created. And then this URL goes to the Parser. The parser, parse this web page and gives output in the form of the set of the hyperlinks. Corresponding to each link we create a node and it is attached to the root node. In this way the whole 3D graph of the website is generated. Different color schemes are used for the nodes of different links e.g. text links, image links, video links etc. Advanced search facility is also provided. Moreover as the graph is 3D in nature, the user can rotate the graph as per his requirement

From Keyword Search to Exploration: How Result Visualization Aids Discovery on the Web

A key to the Web's success is the power of search. The elegant way in which search results are returned is usually remarkably effective. However, for exploratory search in which users need to learn, discover, and understand novel or complex topics, there is substantial room for improvement. Human computer interaction researchers and web browser designers have developed novel strategies to improve Web search by enabling users to conveniently visualize, manipulate, and organize their Web search results. This monograph offers fresh ways to think about search-related cognitive processes and describes innovative design approaches to browsers and related tools. For instance, while key word search presents users with results for specific information (e.g., what is the capitol of Peru), other methods may let users see and explore the contexts of their requests for information (related or previous work, conflicting information), or the properties that associate groups of information assets (group legal decisions by lead attorney). We also consider the both traditional and novel ways in which these strategies have been evaluated. From our review of cognitive processes, browser design, and evaluations, we reflect on the future opportunities and new paradigms for exploring and interacting with Web search results

Comparison of two approaches for web-based 3D visualization of smart building sensor data

Abstract. This thesis presents a comparative study on two different approaches for visualizing sensor data collected from smart buildings on the web using 3D virtual environments. The sensor data is provided by sensors that are deployed in real buildings to measure several environmental parameters including temperature, humidity, air quality and air pressure. The first approach uses the three.js WebGL framework to create the 3D model of a smart apartment where sensor data is illustrated with point and wall visualizations. Point visualizations show sensor values at the real locations of the sensors using text, icons or a mixture of the two. Wall visualizations display sensor values inside panels placed on the interior walls of the apartment. The second approach uses the Unity game engine to create the 3D model of a 4-floored hospice where sensor data is illustrated with aforementioned point visualizations and floor visualizations, where the sensor values are shown on the floor around the location of the sensors in form of color or other effects. The two approaches are compared with respect to their technical performance in terms of rendering speed, model size and request size, and with respect to the relative advantages and disadvantages of the two development environments as experienced in this thesis

Computational photography, 3D modelling and online publication of basketry for Cache Cave (CA, USA)

Advanced 2D and 3D computer visualizations are increasingly being used for recording and documentation, analysis, dissemination and public engagement purposes. Recent technological advances improve considerably not only data acquisition, processing and analysis but also provide easy and efficient online presentation. This paper aims to evaluate the contribution of advanced 2D and 3D computer visualization and discuss the potential of 3D modelling for recording basketry technology and documenting the state of preservation of baskets. It explores the available analysis, integration and on-line dissemination tools, using as case studies recently excavated baskets from Cache Cave, Southern California. Results indicate that the proposed methodology, which incorporates Reflectance Transformation Imaging visualizations and photogrammetric 3D models, further processed using 3D modelling software and integrated analysis tools, and transformed to web-based format, is useful addition to the basketry analysis toolkit

Scaling Up Medical Visualization : Multi-Modal, Multi-Patient, and Multi-Audience Approaches for Medical Data Exploration, Analysis and Communication

Medisinsk visualisering er en av de mest applikasjonsrettede områdene av visualiseringsforsking. Tett samarbeid med medisinske eksperter er nødvendig for å tolke medisinsk bildedata og lage betydningsfulle visualiseringsteknikker og visualiseringsapplikasjoner. Kreft er en av de vanligste dødsårsakene, og med økende gjennomsnittsalder i i-land øker også antallet diagnoser av gynekologisk kreft. Moderne avbildningsteknikker er et viktig verktøy for å vurdere svulster og produsere et økende antall bildedata som radiologer må tolke. I tillegg til antallet bildemodaliteter, øker også antallet pasienter, noe som fører til at visualiseringsløsninger må bli skalert opp for å adressere den økende kompleksiteten av multimodal- og multipasientdata. Dessuten er ikke medisinsk visualisering kun tiltenkt medisinsk personale, men har også som mål å informere pasienter, pårørende, og offentligheten om risikoen relatert til visse sykdommer, og mulige behandlinger. Derfor har vi identifisert behovet for å skalere opp medisinske visualiseringsløsninger for å kunne håndtere multipublikumdata. Denne avhandlingen adresserer skaleringen av disse dimensjonene i forskjellige bidrag vi har kommet med. Først presenterer vi teknikkene våre for å skalere visualiseringer i flere modaliteter. Vi introduserer en visualiseringsteknikk som tar i bruk små multipler for å vise data fra flere modaliteter innenfor et bildesnitt. Dette lar radiologer utforske dataen effektivt uten å måtte bruke flere sidestilte vinduer. I det neste steget utviklet vi en analyseplatform ved å ta i bruk «radiomic tumor profiling» på forskjellige bildemodaliteter for å analysere kohortdata og finne nye biomarkører fra bilder. Biomarkører fra bilder er indikatorer basert på bildedata som kan forutsi variabler relatert til kliniske utfall. «Radiomic tumor profiling» er en teknikk som genererer mulige biomarkører fra bilder basert på første- og andregrads statistiske målinger. Applikasjonen lar medisinske eksperter analysere multiparametrisk bildedata for å finne mulige korrelasjoner mellom kliniske parameter og data fra «radiomic tumor profiling». Denne tilnærmingen skalerer i to dimensjoner, multimodal og multipasient. I en senere versjon la vi til funksjonalitet for å skalere multipublikumdimensjonen ved å gjøre applikasjonen vår anvendelig for livmorhalskreft- og prostatakreftdata, i tillegg til livmorkreftdataen som applikasjonen var designet for. I et senere bidrag fokuserer vi på svulstdata på en annen skala og muliggjør analysen av svulstdeler ved å bruke multimodal bildedata i en tilnærming basert på hierarkisk gruppering. Applikasjonen vår finner mulige interessante regioner som kan informere fremtidige behandlingsavgjørelser. I et annet bidrag, en digital sonderingsinteraksjon, fokuserer vi på multipasientdata. Bildedata fra flere pasienter kan sammenlignes for å finne interessante mønster i svulstene som kan være knyttet til hvor aggressive svulstene er. Til slutt skalerer vi multipublikumdimensjonen med en likhetsvisualisering som er anvendelig for forskning på livmorkreft, på bilder av nevrologisk kreft, og maskinlæringsforskning på automatisk segmentering av svulstdata. Som en kontrast til de allerede fremhevete bidragene, fokuserer vårt siste bidrag, ScrollyVis, hovedsakelig på multipublikumkommunikasjon. Vi muliggjør skapelsen av dynamiske og vitenskapelige “scrollytelling”-opplevelser for spesifikke eller generelle publikum. Slike historien kan bli brukt i spesifikke brukstilfeller som kommunikasjon mellom lege og pasient, eller for å kommunisere vitenskapelige resultater via historier til et generelt publikum i en digital museumsutstilling. Våre foreslåtte applikasjoner og interaksjonsteknikker har blitt demonstrert i brukstilfeller og evaluert med domeneeksperter og fokusgrupper. Dette har ført til at noen av våre bidrag allerede er i bruk på andre forskingsinstitusjoner. Vi ønsker å evaluere innvirkningen deres på andre vitenskapelige felt og offentligheten i fremtidige arbeid.Medical visualization is one of the most application-oriented areas of visualization research. Close collaboration with medical experts is essential for interpreting medical imaging data and creating meaningful visualization techniques and visualization applications. Cancer is one of the most common causes of death, and with increasing average age in developed countries, gynecological malignancy case numbers are rising. Modern imaging techniques are an essential tool in assessing tumors and produce an increasing number of imaging data radiologists must interpret. Besides the number of imaging modalities, the number of patients is also rising, leading to visualization solutions that must be scaled up to address the rising complexity of multi-modal and multi-patient data. Furthermore, medical visualization is not only targeted toward medical professionals but also has the goal of informing patients, relatives, and the public about the risks of certain diseases and potential treatments. Therefore, we identify the need to scale medical visualization solutions to cope with multi-audience data. This thesis addresses the scaling of these dimensions in different contributions we made. First, we present our techniques to scale medical visualizations in multiple modalities. We introduced a visualization technique using small multiples to display the data of multiple modalities within one imaging slice. This allows radiologists to explore the data efficiently without having several juxtaposed windows. In the next step, we developed an analysis platform using radiomic tumor profiling on multiple imaging modalities to analyze cohort data and to find new imaging biomarkers. Imaging biomarkers are indicators based on imaging data that predict clinical outcome related variables. Radiomic tumor profiling is a technique that generates potential imaging biomarkers based on first and second-order statistical measurements. The application allows medical experts to analyze the multi-parametric imaging data to find potential correlations between clinical parameters and the radiomic tumor profiling data. This approach scales up in two dimensions, multi-modal and multi-patient. In a later version, we added features to scale the multi-audience dimension by making our application applicable to cervical and prostate cancer data and the endometrial cancer data the application was designed for. In a subsequent contribution, we focus on tumor data on another scale and enable the analysis of tumor sub-parts by using the multi-modal imaging data in a hierarchical clustering approach. Our application finds potentially interesting regions that could inform future treatment decisions. In another contribution, the digital probing interaction, we focus on multi-patient data. The imaging data of multiple patients can be compared to find interesting tumor patterns potentially linked to the aggressiveness of the tumors. Lastly, we scale the multi-audience dimension with our similarity visualization applicable to endometrial cancer research, neurological cancer imaging research, and machine learning research on the automatic segmentation of tumor data. In contrast to the previously highlighted contributions, our last contribution, ScrollyVis, focuses primarily on multi-audience communication. We enable the creation of dynamic scientific scrollytelling experiences for a specific or general audience. Such stories can be used for specific use cases such as patient-doctor communication or communicating scientific results via stories targeting the general audience in a digital museum exhibition. Our proposed applications and interaction techniques have been demonstrated in application use cases and evaluated with domain experts and focus groups. As a result, we brought some of our contributions to usage in practice at other research institutes. We want to evaluate their impact on other scientific fields and the general public in future work.Doktorgradsavhandlin

Space-Time Kernel Density Estimation for Real-Time Interactive Visual Analytics

We present a GPU-based implementation of the Space-Time Kernel Density Estimation (STKDE) that provides massive speed up in analyzing spatial- temporal data. In our work we are able to achieve sub- second performance for data sizes transferable over the Internet in realistic time. We have integrated this into web-based visual interactive analytics tools for analyzing spatial-temporal data. The resulting inte- grated visual analytics (VA) system permits new anal- yses of spatial-temporal data from a variety of sources. Novel, interlinked interface elements permit efficient, meaningful analyses

Categorized Graphical Overviews for Web Search Results: An Exploratory Study using U.S. Government Agencies as a Meaningful and Stable Structure

Search engines are very effective at generating long lists of results that are highly relevant to user-provided query terms. But the lack of effective overviews presents challenges to users who seek to understand these results, especially for a complex task such as learning about a topic area, which requires gaining overviews of and exploring large sets of search results, identifying unusual documents, and understanding their context. Categorizing the results into comprehensible visual displays using meaningful and stable classifications can support user exploration and understanding of large sets of search results. This extended abstract presents a set of principles that we are developing for search result visualization. It also describes an exploratory study that investigated categorized overviews of search results for complex search tasks within the domain of U. S. government web sites, using a hierarchy based on the federal government organization