Publikationen

Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen

2015

Veas Eduardo Enrique, Mutlu Belgin, di Sciascio Maria Cecilia, Tschinkel Gerwald, Sabol Vedran

Visual Recommendations for Scientific and Cultural Content

IVAPP 2015, Berlin, 2015

Konferenz
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.
2015

Mutlu Belgin, Veas Eduardo Enrique, Trattner Christoph, Sabol Vedran

VizRec: A Two-Stage Recommender System for Personalized Visualizations

ACM IUI, ACM, Atlanta, Georgia, USA, 2015

Konferenz
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.
2015

Mutlu Belgin, Sabol Vedran

Visual Analysis of Scientific Content

STCSN E-LETTER ON SCIENCE 2.0, 2015

Konferenz
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.
2015

Mutlu Belgin, Veas Eduardo Enrique, Trattner Christoph, Sabol Vedran

Towards a Recommender Engine for Personalized Visualizations

UMAP, 2015

Konferenz
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.
2015

Veas Eduardo Enrique, Sabol Vedran, Singh Santokh, Ulbrich Eva Pauline

Reading through Graphics: Interactive Landscapes to Explore Dynamic Topic Spaces

Proceedings Part I of the 17th HCI International Conference, HCI International 2015, Los Angeles, CA, USA, August 2-7, 2015, 2015

Konferenz
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.
2015

Tschinkel Gerwald, di Sciascio Maria Cecilia, Mutlu Belgin, Sabol Vedran

The Recommendation Dashboard: A System to Visualise and Organise Recommendations

Proceedings of the 19th International Conference on Information Visualisation (IV2015), 2015

Konferenz
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.
2015

Rauch Manuela, Klieber Hans-Werner, Wozelka Ralph, Singh Santokh, Sabol Vedran

Knowminer Search - a Multi-Visualisation Collaborative Approach to Search Result Analysis

Proceedings of the 19th International Conference on Information Visualisation (IV2015), 2015

Konferenz
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.
2015

di Sciascio Maria Cecilia, Sabol Vedran, Veas Eduardo Enrique

uRank: Exploring Document Recommendations through an Interactive User-Driven Approach

RecSys Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS'15), CEUR-WS, 2015

Konferenz
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.
2015

di Sciascio Maria Cecilia, Sabol Vedran, Veas Eduardo Enrique

uRank: Visual analytics approach for search result exploration

Visual Analytics Science and Technology (VAST), 2015 IEEE Conference on, IEEE, 2015

Konferenz
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.
2015

Wozelka Ralph, Kröll Mark, Sabol Vedran

Exploring Time Relations in Semantic Graphs

Proceedings of SIGRAD, SIGRAD, Linköping University Electronic Press, Stockholm, Sweden, 2015

Konferenz
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.
Kontakt Karriere

Hiermit erkläre ich ausdrücklich meine Einwilligung zum Einsatz und zur Speicherung von Cookies. Weiter Informationen finden sich unter Datenschutzerklärung

The cookie settings on this website are set to "allow cookies" to give you the best browsing experience possible. If you continue to use this website without changing your cookie settings or you click "Accept" below then you are consenting to this.

Close