Pammer-Schindler Viktoria, Simon Jörg Peter, Wilding Karin, Keller Stephan, Scherer Reinhold
2014
Brain-computer interface (BCI) technology translatesbrain activity to machine-intelligible patterns, thusserving as input “device” to computers. BCI traininggames make the process of acquiring training data forthe machine learning more engaging for the users. Inthis work, we discuss the design space for BCI traininggames based on existing literature, and a traininggame in form of a Jigsaw Puzzle. The game wastrialled with four cerebral palsy patients. All patientswere very acceptant of the involved technology, which,we argue, relates back to the concept of BCI traininggames plus the adaptations we made. On the otherhand, the data quality was unsatisfactory. Hence, infuture work both concept and implementation need tobe finetuned to achieve a balance between useracceptance and data quality.
Rauch Manuela, Wozelka Ralph, Veas Eduardo Enrique, Sabol Vedran
2014
Graphs are widely used to represent relationshipsbetween entities. Indeed, their simplicity in depicting connect-edness backed by a mathematical formalism, make graphs anideal metaphor to convey relatedness between entities irrespec-tive of the domain. However, graphs pose several challenges forvisual analysis. A large number of entities or a densely con-nected set quickly render the graph unreadable due to clutter.Typed relationships leading to multigraphs cannot clearly berepresented in hierarchical layout or edge bundling, commonclutter reduction techniques. We propose a novel approach tovisual analysis of complex graphs based on two metaphors:semantic blossom and selective expansion. Instead of showingthe whole graph, we display only a small representative subsetof nodes, each with a compressed summary of relations in asemantic blossom. Users apply selective expansion to traversethe graph and discover the subset of interest. A preliminaryevaluation showed that our approach is intuitive and usefulfor graph exploration and provided insightful ideas for futureimprovements.
Tschinkel Gerwald, Veas Eduardo Enrique, Mutlu Belgin, Sabol Vedran
2014
Providing easy to use methods for visual analysis of LinkedData is often hindered by the complexity of semantic technologies. Onthe other hand, semantic information inherent to Linked Data providesopportunities to support the user in interactively analysing the data. Thispaper provides a demonstration of an interactive, Web-based visualisa-tion tool, the “Vis Wizard”, which makes use of semantics to simplify theprocess of setting up visualisations, transforming the data and, most im-portantly, interactively analysing multiple datasets using brushing andlinking method
Sabol Vedran, Albert Dietrich, Veas Eduardo Enrique, Mutlu Belgin, Granitzer Michael
2014
Linked Data has grown to become one of the largest availableknowledge bases. Unfortunately, this wealth of data remains inaccessi-ble to those without in-depth knowledge of semantic technologies. Wedescribe a toolchain enabling users without semantic technology back-ground to explore and visually analyse Linked Data. We demonstrateits applicability in scenarios involving data from the Linked Open DataCloud, and research data extracted from scientific publications. Our fo-cus is on the Web-based front-end consisting of querying and visuali-sation tools. The performed usability evaluations unveil mainly positiveresults confirming that the Query Wizard simplifies searching, refiningand transforming Linked Data and, in particular, that people using theVisualisation Wizard quickly learn to perform interactive analysis taskson the resulting Linked Data sets. In making Linked Data analysis ef-fectively accessible to the general public, our tool has been integratedin a number of live services where people use it to analyse, discover anddiscuss facts with Linked Data.
Granitzer MIchael, Veas Eduardo Enrique, Seifert C.
2014
In an interconnected world, Linked Data is more importantthan ever before. However, it is still quite dicult to accessthis new wealth of semantic data directly without havingin-depth knowledge about SPARQL and related semantictechnologies. Also, most people are currently used to consumingdata as 2-dimensional tables. Linked Data is by de-nition always a graph, and not that many people are used tohandle data in graph structures. Therefore we present theLinked Data Query Wizard, a web-based tool for displaying,accessing, ltering, exploring, and navigating Linked Datastored in SPARQL endpoints. The main innovation of theinterface is that it turns the graph structure of Linked Datainto a tabular interface and provides easy-to-use interactionpossibilities by using metaphors and techniques from currentsearch engines and spreadsheet applications that regular webusers are already familiar with.
Mutlu Belgin, Tschinkel Gerwald, Veas Eduardo Enrique, Sabol Vedran, Stegmaier Florian, Granitzer Michael
2014
Research papers are published in various digital libraries, which deploy their own meta-models and tech-nologies to manage, query, and analyze scientific facts therein. Commonly they only consider the meta-dataprovided with each article, but not the contents. Hence, reaching into the contents of publications is inherentlya tedious task. On top of that, scientific data within publications are hardcoded in a fixed format (e.g. tables).So, even if one manages to get a glimpse of the data published in digital libraries, it is close to impossibleto carry out any analysis on them other than what was intended by the authors. More effective querying andanalysis methods are required to better understand scientific facts. In this paper, we present the web-basedCODE Visualisation Wizard, which provides visual analysis of scientific facts with emphasis on automatingthe visualisation process, and present an experiment of its application. We also present the entire analyticalprocess and the corresponding tool chain, including components for extraction of scientific data from publica-tions, an easy to use user interface for querying RDF knowledge bases, and a tool for semantic annotation ofscientific data set
Silva Nelson
2014
Silva Nelson
2014
Silva Nelson, Settgast Volker, Eggeling Eva, Grill Florian, Zeh Theodor, Fellner Dieter W.
2014