Monsberger Michael, Koppelhuber Daniela, Sabol Vedran, Gursch Heimo, Spataru Adrian, Prentner Oliver
2019
A lot of research is currently focused on studying user behavior indirectly by analyzing sensor data. However, only little attention has been given to the systematic acquisition of immediate user feedback to study user behavior in buildings. In this paper, we present a novel user feedback system which allows building users to provide feedback on the perceived sense of personal comfort in a room. To this end, a dedicated easy-to-use mobile app has been developed; it is complemented by a supporting infrastructure, including a web page for an at-a-glance overview. The obtained user feedback is compared with sensor data to assess whether building services (e.g., heating, ventilation and air-conditioning systems) are operated in accordance with user requirements. This serves as a basis to develop algorithms capable of optimizing building operation by providing recommendations to facility management staff or by automatic adjustment of operating points of building services. In this paper, we present the basic concept of the novel feedback system for building users and first results from an initial test phase. The results show that building users utilize the developed app to provide both, positive and negative feedback on room conditions. They also show that it is possible to identify rooms with non-ideal operating conditions and that reasonable measures to improve building operation can be derived from the gathered information. The results highlight the potential of the proposed system.
Mutlu Belgin, Simic Ilija, Cicchinelli Analia, Sabol Vedran, Veas Eduardo Enrique
2018
Learning dashboards (LD) are commonly applied for monitoring and visual analysis of learning activities. The main purpose of LDs is to increase awareness, to support self assessment and reflection and, when used in collaborative learning platforms (CLP), to improve the collaboration among learners. Collaborative learning platforms serve astools to bring learners together, who share the same interests and ideas and are willing to work and learn together – a process which, ideally, leads to effective knowledge building. However, there are collaborationand communications factors which affect the effectiveness of knowledge creation – human, social and motivational factors, design issues, technical conditions, and others. In this paper we introduce a learning dashboard – the Visualizer – that serves the purpose of (statistically) analyzing andexploring the behaviour of communities and users. Visualizer allows a learner to become aware of other learners with similar characteristics and also to draw comparisons with individuals having similar learninggoals. It also helps a teacher become aware of how individuals working in the groups (learning communities) interact with one another and across groups.
Silva Nelson, Schreck Tobias, Veas Eduardo Enrique, Sabol Vedran, Eggeling Eva, Fellner Dieter W.
2018
We developed a new concept to improve the efficiency of visual analysis through visual recommendations. It uses a novel eye-gaze based recommendation model that aids users in identifying interesting time-series patterns. Our model combines time-series features and eye-gaze interests, captured via an eye-tracker. Mouse selections are also considered. The system provides an overlay visualization with recommended patterns, and an eye-history graph, that supports the users in the data exploration process. We conducted an experiment with 5 tasks where 30 participants explored sensor data of a wind turbine. This work presents results on pre-attentive features, and discusses the precision/recall of our model in comparison to final selections made by users. Our model helps users to efficiently identify interesting time-series patterns.
d'Aquin Mathieu , Adamou Alessandro , Dietze Stefan , Fetahu Besnik , Gadiraju Ujwal , Hasani-Mavriqi Ilire, Holz Peter, Kümmerle Joachim, Kowald Dominik, Lex Elisabeth, Lopez Sola Susana, Mataran Ricardo, Sabol Vedran, Troullinou Pinelopi, Veas Eduardo, Veas Eduardo Enrique
2017
More and more learning activities take place online in a self-directed manner. Therefore, just as the idea of self-tracking activities for fitness purposes has gained momentum in the past few years, tools and methods for awareness and self-reflection on one's own online learning behavior appear as an emerging need for both formal and informal learners. Addressing this need is one of the key objectives of the AFEL (Analytics for Everyday Learning) project. In this paper, we discuss the different aspects of what needs to be put in place in order to enable awareness and self-reflection in online learning. We start by describing a scenario that guides the work done. We then investigate the theoretical, technical and support aspects that are required to enable this scenario, as well as the current state of the research in each aspect within the AFEL project. We conclude with a discussion of the ongoing plans from the project to develop learner-facing tools that enable awareness and self-reflection for online, self-directed learners. We also elucidate the need to establish further research programs on facets of self-tracking for learning that are necessarily going to emerge in the near future, especially regarding privacy and ethics.
Tschinkel Gerwald, Sabol Vedran
2017
When using classical search engines, researchers are often confronted with a number of results far beyond what they can realistically manage to read; when this happens, recommender systems can help, by pointing users to the most valuable sources of information. In the course of a long-term research project, research into one area can extend over several days, weeks, or even months. Interruptions are unavoidable, and, when multiple team members have to discuss the status of a project, it’s important to be able to communicate the current research status easily and accurately. Multiple type-specific interactive views can help users identify the results most relevant to their focus of interest. Our recommendation dashboard uses micro-filter visualizations intended to improve the experience of working with multiple active filters, allowing researchers to maintain an overview of their progress. Within this paper, we carry out an evaluation of whether micro-visualizations help to increase the memorability and readability of active filters in comparison to textual filters. Five tasks, quantitative and qualitative questions, and the separate view on the different visualisation types enabled us to gain insights on how micro-visualisations behave and will be discussed throughout the paper.
Hasitschka Peter, Sabol Vedran, Thalmann Stefan
2017
Industry 4.0 describes the digitization and the interlinkingof companies working together in a supply chain [1]. Thereby,the digitization and the interlinking does not only affects themachines and IT infrastructure, rather also the employees areaffected [3]. The employees have to acquire more and morecomplex knowledge within a shorter period of time. To copewith this challenge, the learning needs to be integrated into thedaily work practices, while the learning communities shouldmap the organizational production networks [2]. Such learningnetworks support the knowledge exchange and joint problemsolving together with all involved parties [4]. However, insuch communities not all involved actors are known and hencesupport to find the right learning material and peers is needed.Nowadays, many different learning environments are usedin the industry. Their complexity makes it hard to understandwhether the system provides an optimal learning environment.The large number of learning resources, learners and theiractivities makes it hard to identify potential problems inside alearning environment. Since the human visual system providesenormous power for discovering patterns from data displayedusing a suitable visual representation [5], visualizing such alearning environment could provide deeper insights into itsstructure and activities of the learners.Our goal is to provide a visual framework supporting theanalysis of communities that arise in a learning environment.Such analysis may lead to discovery of information that helpsto improve the learning environment and the users’ learningsuccess.
Mutlu Belgin, Sabol Vedran, Gursch Heimo, Kern Roman
2016
Graphical interfaces and interactive visualisations are typical mediators between human users and data analytics systems. HCI researchers and developers have to be able to understand both human needs and back-end data analytics. Participants of our tutorial will learn how visualisation and interface design can be combined with data analytics to provide better visualisations. In the first of three parts, the participants will learn about visualisations and how to appropriately select them. In the second part, restrictions and opportunities associated with different data analytics systems will be discussed. In the final part, the participants will have the opportunity to develop visualisations and interface designs under given scenarios of data and system settings.
Tschinkel Gerwald, Hasitschka Peter, Sabol Vedran, Hafner R
2016
Faceted search is a well known and broadly imple- mented paradigm for filtering information with various types of structured information. In this paper we introduce a multiple-view faceted interface, consisting of one main visualisation for exploring the data and multiple minia- turised visualisations showing the filters. The Recommen- dation Dashboard tool provides several interactive visual- isations for analysing recommender results along various faceted dimensions specific to cultural heritage and scien- tific content. As our aim is to reduce the user load and opti- mise the use of screen area, we permit only one main visu- alisation to be visible at a time, and introduce the concept of micro-visualisations – small, simplified views conveying only the necessary information – to provide natural, easy to understand representation of the the active filter set.
di Sciascio Maria Cecilia, Sabol Vedran, Veas Eduardo Enrique
2016
Whenever users engage in gathering and organizing new information, searching and browsing activities emerge at the core of the exploration process. As the process unfolds and new knowledge is acquired, interest drifts occur inevitably and need to be accounted for. Despite the advances in retrieval and recommender algorithms, real-world interfaces have remained largely unchanged: results are delivered in a relevance-ranked list. However, it quickly becomes cumbersome to reorganize resources along new interests, as any new search brings new results. We introduce uRank and investigate interactive methods for understanding, refining and reorganizing documents on-the-fly as information needs evolve. uRank includes views summarizing the contents of a recommendation set and interactive methods conveying the role of users' interests through a recommendation ranking. A formal evaluation showed that gathering items relevant to a particular topic of interest with uRank incurs in lower cognitive load compared to a traditional ranked list. A second study consisting in an ecological validation reports on usage patterns and usability of the various interaction techniques within a free, more natural setting.
Wozelka Ralph, Kröll Mark, Sabol Vedran
2015
The analysis of temporal relationships in large amounts of graph data has gained significance in recent years. In-formation providers such as journalists seek to bring order into their daily work when dealing with temporally dis-tributed events and the network of entities, such as persons, organisations or locations, which are related to these events. In this paper we introduce a time-oriented graph visualisation approach which maps temporal information to visual properties such as size, transparency and position and, combined with advanced graph navigation features, facilitates the identification and exploration of temporal relationships. To evaluate our visualisation, we compiled a dataset of ~120.000 news articles from international press agencies including Reuters, CNN, Spiegel and Aljazeera. Results from an early pilot study show the potentials of our visualisation approach and its usefulness for analysing temporal relationships in large data sets.
di Sciascio Maria Cecilia, Sabol Vedran, Veas Eduardo Enrique
2015
uRankis a Web-based tool combining lightweight text analyticsand visual methods for topic-wise exploration of document sets.It includes a view summarizing the content of the document setin meaningful terms, a dynamic document ranking view and a de-tailed view for further inspection of individual documents. Its ma-jor strength lies in how it supports users in reorganizing documentson-the-fly as their information interests change. We present a pre-liminary evaluation showing that uRank helps to reduce cognitiveload compared to a traditional list-based representation.
di Sciascio Maria Cecilia, Sabol Vedran, Veas Eduardo Enrique
2015
Whenever we gather or organize knowledge, the task of searching inevitably takes precedence. As exploration unfolds, it becomes cumbersome to reorganize resources along new interests, as any new search brings new results. Despite huge advances in retrieval and recommender systems from the algorithmic point of view, many real-world interfaces have remained largely unchanged: results appear in an infinite list ordered by relevance with respect to the current query. We introduce uRank, a user-driven visual tool for exploration and discovery of textual document recommendations. It includes a view summarizing the content of the recommendation set, combined with interactive methods for understanding, refining and reorganizing documents on-the-fly as information needs evolve. We provide a formal experiment showing that uRank users can browse the document collection and efficiently gather items relevant to particular topics of interest with significantly lower cognitive load compared to traditional list-based representations.
Rauch Manuela, Klieber Hans-Werner, Wozelka Ralph, Singh Santokh, Sabol Vedran
2015
The amount of information available on the internet and within enterprises has reached an incredible dimension.Efficiently finding and understanding information and thereby saving resources remains one of the major challenges in our daily work. Powerful text analysis methods, a scalable faceted retrieval engine and a well-designed interactive user interface are required to address the problem. Besides providing means for drilling-down to the relevant piece of information, a part of the challenge arises from the need of analysing and visualising data to discover relationships and correlations, gain an overview of data distributions and unveil trends. Visual interfaces leverage the enormous bandwidth of the human visual system to support pattern discovery in large amounts of data. Our Knowminer search builds upon the well-known faceted search approach which is extended with interactive visualisations allowing users to analyse different aspects of the result set. Additionally, our system provides functionality for organising interesting search results into portfolios, and also supports social features for rating and boosting search results and for sharing and annotating portfolios.
Tschinkel Gerwald, di Sciascio Maria Cecilia, Mutlu Belgin, Sabol Vedran
2015
Recommender systems are becoming common tools supportingautomatic, context-based retrieval of resources.When the number of retrieved resources grows large visualtools are required that leverage the capacity of humanvision to analyse large amounts of information. Weintroduce a Web-based visual tool for exploring and organisingrecommendations retrieved from multiple sourcesalong dimensions relevant to cultural heritage and educationalcontext. Our tool provides several views supportingfiltering in the result set and integrates a bookmarkingsystem for organising relevant resources into topic collections.Building upon these features we envision a systemwhich derives user’s interests from performed actions anduses this information to support the recommendation process.We also report on results of the performed usabilityevaluation and derive directions for further development.
Veas Eduardo Enrique, Sabol Vedran, Singh Santokh, Ulbrich Eva Pauline
2015
An information landscape is commonly used to represent relatedness in large, high-dimensional datasets, such as text document collections. In this paper we present interactive metaphors, inspired in map reading and visual transitions, that enhance the landscape representation for the analysis of topical changes in dynamic text repositories. The goal of interactive visualizations is to elicit insight, to allow users to visually formulate hypotheses about the underlying data and to prove them. We present a user study that investigates how users can elicit information about topics in a large document set. Our study concentrated on building and testing hypotheses using the map reading metaphors. The results show that people indeed relate topics in the document set from spatial relationships shown in the landscape, and capture the changes to topics aided by map reading metaphors.
Mutlu Belgin, Veas Eduardo Enrique, Trattner Christoph, Sabol Vedran
2015
isualizations have a distinctive advantage when dealing with the information overload problem: being grounded in basic visual cognition, many people understand visualizations. However, when it comes to creating them, it requires specific expertise of the domain and underlying data to determine the right representation. Although there are rules that help generate them, the results are too broad as these methods hardly account for varying user preferences. To tackle this issue, we propose a novel recommender system that suggests visualizations based on (i) a set of visual cognition rules and (ii) user preferences collected in Amazon-Mechanical Turk. The main contribution of this paper is the introduction and the evaluation of a novel approach called VizRec that is able suggest an optimal list of top-n visualizations for heterogeneous data sources in a personalized manner.
Mutlu Belgin, Sabol Vedran
2015
The steadily increasing amount of scientific publications demands for more powerful, user-oriented technologiessupporting querying and analyzing scientific facts therein. Current digital libraries that provide services to accessscientific content are rather closed in a way that they deploy their own meta-models and technologies to query and analysethe knowledge contained in scientific publications. The goal of the research project CODE is to realize a framework basedon Linked Data principles which aims to provide methods for federated querying within scientific data, and interfacesenabling user to easily perform exploration and analysis tasks on received content. The main focus in this paper lieson the one hand on extraction and organization of scientific facts embedded in publications and on the other hand on anintelligent framework facilitating search and visual analysis of scientific facts through suggesting visualizations appropriatefor the underlying data.
Mutlu Belgin, Veas Eduardo Enrique, Trattner Christoph, Sabol Vedran
2015
Identifying and using the information from distributed and heterogeneous information sources is a challenging task in many application fields. Even with services that offer welldefined structured content, such as digital libraries, it becomes increasingly difficult for a user to find the desired information. To cope with an overloaded information space, we propose a novel approach – VizRec– combining recommender systems (RS) and visualizations. VizRec suggests personalized visual representations for recommended data. One important aspect of our contribution and a prerequisite for VizRec are user preferences that build a personalization model. We present a crowd based evaluation and show how such a model of preferences can be elicited.
Veas Eduardo Enrique, Mutlu Belgin, di Sciascio Maria Cecilia, Tschinkel Gerwald, Sabol Vedran
2015
Supporting individuals who lack experience or competence to evaluate an overwhelming amout of informationsuch as from cultural, scientific and educational content makes recommender system invaluable to cope withthe information overload problem. However, even recommended information scales up and users still needto consider large number of items. Visualization takes a foreground role, letting the user explore possiblyinteresting results. It leverages the high bandwidth of the human visual system to convey massive amounts ofinformation. This paper argues the need to automate the creation of visualizations for unstructured data adaptingit to the user’s preferences. We describe a prototype solution, taking a radical approach considering bothgrounded visual perception guidelines and personalized recommendations to suggest the proper visualization.
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.
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
Höfler Patrick, Granitzer Michael, Sabol Vedran, Lindstaedt Stefanie
2013
Linked Data has become an essential part of the Semantic Web. A lot of Linked Data is already available in the Linked Open Data cloud, which keeps growing due to an influx of new data from research and open government activities. However, it is still quite difficult to access this wealth of semantically enriched data directly without having in-depth knowledge about SPARQL and related semantic technologies. In this paper, we present the Linked Data Query Wizard, a prototype that provides a Linked Data interface for non-expert users, focusing on keyword search as an entry point and a tabular interface providing simple functionality for filtering and exploration.
Granitzer Michael, Kienreich Wolfgang, Sabol Vedran, Lex Elisabeth
2010
Technological advances and paradigmatic changes in the utilization of the World Wide Web havetransformed the information seeking strategies of media consumers and invalidated traditionalbusiness models of media providers. We discuss relevant aspects of this development and presenta knowledge relationship discovery pipeline to address the requirements of media providers andmedia consumers. We also propose visually enhanced access methods to bridge the gap betweencomplex media services and the information needs of the general public. We conclude that acombination of advanced processing methods and visualizations will enable media providers totake the step from content-centered to service-centered business models and, at the same time,will help media consumers to better satisfy their personal information needs.
Sabol Vedran, Scharl A.
2008
Andrews K., Kienreich Wolfgang, Sabol Vedran, Granitzer Michael
2004
Granitzer Michael, Kienreich Wolfgang, Sabol Vedran, Andrews K.
2004
Lux M., Granitzer Michael, Kienreich Wolfgang, Sabol Vedran, Klieber Hans-Werner, Sarka W.
2004
Granitzer Michael, Kienreich Wolfgang, Sabol Vedran, Dösinger G.
2003
Lux M., Granitzer Michael, Sabol Vedran, Kienreich Wolfgang, Becker J.
2003
Andrews K., Kienreich Wolfgang, Sabol Vedran, Granitzer Michael
2003
Kappe F., Droschl G., Kienreich Wolfgang, Sabol Vedran, Andrews K., Granitzer Michael, Auer P.
2003
Kienreich Wolfgang, Sabol Vedran, Granitzer Michael, Kappe F., Andrews K.
2003
Sabol Vedran, Kienreich Wolfgang, Granitzer Michael, Becker J.
2003
Sabol Vedran, Kienreich Wolfgang, Granitzer Michael, Becker J.
2002
Kappe F., Droschl G., Kienreich Wolfgang, Sabol Vedran, Becker J., Andrews K., Granitzer Michael, Auer P.
2002
Sabol Vedran, Kienreich Wolfgang, Granitzer Michael, Becker J., Andrews K.
2002
Andrews K., Gütl Christian, Moser J., Sabol Vedran, Lackner W.
2001
The xFIND gatherer-broker architecture provides a wealth of metadata, which can be used to provide sophisticated search functionality. Local or remote documents are indexed and summaries and metadata are stored on an xFIND broker (server). An xFIND client can search a particular broker and access rich metadata for search result presentation, without having to fetch the original documents themselves. Search result sets are not only presented as a traditional ranked list, but also in an interactive scatterplot (Search Result Explorer) and using dynamic thematic clustering (VisIslands)