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University of Fribourg, Switzerland

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21 Papers

Flowstrates++: An Approach to Visualize Multi-Dimensional OD Data

Topics: Visual Data Analysis and Knowledge Discovery; Visual Representation and Interaction; Coordinated and Multiple Views; Geographic Information Visualisation; Multi-dimensional or Multivariate data visualisation

Authors: Nicolas Fuchs , Pierre Vanhulst , Raphaël Tuor and Denis Lalanne

Affiliation: Human-IST Institute, Université of Fribourg, Boulevard de Pérolles 90, Fribourg, Switzerland

Digital Object Identifier: 10.5220/0012252300003660

Proceedings of IVAPP 2024, ISBN: 978-989-758-679-8

Quick Abstract

Is it possible to visualize complex Origin-Destination (OD) data along with relevant spatio-temporal data? In this paper, we tackle this issue by presenting Flowstrates++, an augmented version of Flowstrates which aims to visualize additional time-series datasets linked with OD data. On top of Flowstrates’ heatmap, we designed a second heatmap for spatio-temporal data, synchronized on the temporal axis, as well as other dataset comparison features. Two versions of Flowstrates++ have been designed and implemented: Switch, that displays one external dataset at a time, and Combi (for ”combined”), that displays two external datasets at the same time. We aimed to assess to which extent both variants spur users into making multidimensional findings. To achieve this goal, we evaluated both variants with ninety participants: ten were pilot users in live remote sessions, and eighty were provided by Prolific.co, a crowd-sourcing platform. In a within-groups study, these participants were asked to take relevant annotations about the data on both variants, and to evaluate them through a survey. We then classified the annotations using a framework whose validity was evaluated with an Intercoder Agreement and Fleiss’ Kappa. We found that the Combi variant yielded consistently better results, both in terms of number of produced multidimensional annotations, and in terms of appreciation of the participants. Yet regardless of the variant, our solution allows users to highlight potential correlations between time-series data and temporal OD data.

Designing a Classification for User-authored Annotations in Data Visualization

Topics: Visual Data Analysis and Knowledge Discovery; Information and Scientific Visualization; Visual Analytical Reasoning

Authors: Pierre Vanhulst ¹ , Florian Évéquoz ² , Raphaël Tuor ¹ and Denis Lalanne ¹

Affiliations: ¹ University of Fribourg, Switzerland , ² University of Applied Sciences Western Switzerland, Switzerland

Digital Object Identifier: 10.5220/0006613700850096

Proceedings of IVAPP 2018/VISIGRAPP, ISBN: 978-989-758-289-9

Quick Abstract

This article introduces a classification system for user-authored annotations in the domain of data visualization. The classification system was created with a bottom-up approach, starting from actual user-authored annotations. To devise relevant dimensions for this classification, we designed a data analysis web platform displaying four visualizations of a common dataset. Using this tool, 16 analysts recorded over 300 annotations that were used to design a classification system. That classification system was then iteratively evaluated and refined until a high inter-coder agreement was found. Use cases for such a classification includes assessing the expressiveness of visualizations on a common ground, based on the types of annotations that are produced with each visualization.

Related Ontology Subjects/Areas/Topics (6)

Abstract Data Visualization		General Data Visualization		Visual Analytical Reasoning
Computer Vision, Visualization and Computer Graphics		Information and Scientific Visualization		Visual Data Analysis and Knowledge Discovery

Parallel Bubbles - Evaluation of Three Techniques for Representing Mixed Categorical and Continuous Data in Parallel Coordinates

Topics: Visual Data Analysis and Knowledge Discovery; Databases and Visualization, Visual Data Mining; Visualization Algorithms and Technologies; Scientific Visualization

Authors: Raphaël Tuor ¹ , Florian Evéquoz ² and Denis Lalanne ¹

Affiliations: ¹ University of Fribourg, Switzerland , ² University of Fribourg, University of Applied Sciences Western Switzerland and HES-SO Valais-Wallis, Switzerland

Digital Object Identifier: 10.5220/0006615602520263

Proceedings of IVAPP 2018/VISIGRAPP, ISBN: 978-989-758-289-9

Quick Abstract

Parallel Coordinates are a widely used visualization method for multivariate data analysis tasks. In this paper we discuss the techniques that aim to enhance the representation of categorical data in Parallel Coordinates. We propose Parallel Bubbles, a method that improves the graphical perception of categorical dimensions in Parallel Coordinates by adding a visual encoding of frequency. Our main contribution consists in a user study that compares the performance of three variants of Parallel Coordinates, with similarity and frequency tasks. We base our design choices on the literature review, and on the research guidelines provided by Johansson et al (2016). Parallel Bubbles are a good trade-off between Parallel Coordinates and Parallel Sets in terms of performance for both types of tasks. Adding a visual encoding of frequency leads to a significant difference in performance for a frequency-based task consisting in assessing the most represented category. This study is the first of a series that will aim at testing the three visualization methods in tasks centered on the continuous axis, and where we assume that the performance of Parallel Sets will be worse.

Related Ontology Subjects/Areas/Topics (8)

Abstract Data Visualization	General Data Visualization	Visual Data Analysis and Knowledge Discovery
Computer Vision, Visualization and Computer Graphics	Scientific Visualization	Visualization Algorithms and Technologies
Databases and Visualization, Visual Data Mining	Spatial Data Visualization

A Study about Discovery of Critical Food Consumption Patterns Linked with Lifestyle Diseases using Data Mining Methods

Topics: Datamining; Decision Support Systems; Clinical Problems and Applications; Pattern Recognition and Machine Learning; Practice-based Research Methods for Healthcare IT

Authors: Farshideh Einsele ¹ , Leila Sadeghi ² , Rolf Ingold ³ and Helena Jenzer ²

Affiliations: ¹ Berne University of Applied Sciences, Switzerland , ² Bern University of Applied Sciences, Switzerland , ³ University of Fribourg, Switzerland

Digital Object Identifier: 10.5220/0005170402390245

Proceedings of HEALTHINF 2015/BIOSTEC, ISBN: 978-989-758-068-0

Quick Abstract

Background: To date, the analysis of the implications of dietary patterns on lifestyle diseases is based on data coming either from clinical studies or food surveys, both comprised of a limited number of participants. This article demonstrates that linking big data from a grocery store sales database with demographical and health data by using data mining tools such as classification and association rules is a powerful way to determine if a specific population subgroup is at particular risk for developing a lifestyle disease based on its food consumption patterns. Objective: The objective of the study was to link big data from grocery store sales with demographic and health data to discover critical food consumption patterns linked with lifestyle diseases known to be strongly tied with food consumption. Design: Food consumption databases from a publicly available grocery store database dating from 1997–1998 were gathered along with corresponding demographics and health data from the U. S. west coast, pre-processed, cleaned and finally integrated to a unique database. Results: This study applied data mining techniques such as classification and association mining analysis. Firstly, the studied population was classified according to the demographical information “ age groups” and “race” and data for lifestyle diseases were correspondingly attributed. Secondly, association mining analysis was used to incorporate rules about food consumption and lifestyle diseases. A set of promising preliminary rules and their corresponding interpretation was generated and reported in the present paper. Conclusions: Association mining rules were successfully used to describe and predict rules linking food consumption patterns with lifestyle diseases. In the selected grocery store database, information about interesting aspects of the grocery store customers were found such as marital status, educational background, profession and number of children at home. An in-depth research on these attributes is needed to further expand the present demographical database. Since the search on the internet for demographical attributes back to the year of 2000 corresponding to the studied population subgroup was extremely laborious, the selected demographical attributes to prove the feasibility of the study were limited to age groups and race.

Related Ontology Subjects/Areas/Topics (16)

Artificial Intelligence	Databases and Information Systems Integration	Pattern Recognition and Machine Learning
Biomedical Engineering	Datamining	Practice-based Research Methods for Healthcare IT
Business Analytics	Decision Support Systems	Sensor Networks
Clinical Problems and Applications	Enterprise Information Systems	Signal Processing
Data Engineering	Health Information Systems	Soft Computing
Data Mining

LARGE-SCALE LINKED DATA PROCESSING - Cloud Computing to the Rescue?

Topics: Semantic Web Technologies; Cloud Standards; SAAS, PAAS, IAAS

Authors: Michael Hausenblas ¹ , Robert Grossman ² , Andreas Harth ³ and Philippe Cudré-Mauroux ⁴

Affiliations: ¹ NUI Galway, Ireland , ² University of Chicago & Open Cloud Consortium, United States , ³ Karlsruhe Institute of Technology, Germany , ⁴ University of Fribourg, Switzerland

Digital Object Identifier: 10.5220/0003928702460251

Proceedings of CLOSER 2012, ISBN: 978-989-8565-05-1

Quick Abstract

Processing large volumes of Linked Data requires sophisticated methods and tools. In the recent years we have mainly focused on systems based on relational databases and bespoke systems for Linked Data processing. Cloud computing offerings such as SimpleDB or BigQuery, and cloud-enabled NoSQL systems including Cassandra or CouchDB as well as frameworks such as Hadoop offer appealing alternatives along with great promises concerning performance, scalability and elasticity. In this paper we state a number of Linked Data-specific requirements and review existing cloud computing offerings as well as NoSQL systems that may be used in a cloud computing setup, in terms of their applicability and usefulness for processing datasets on a large-scale.

Related Ontology Subjects/Areas/Topics (9)

Cloud Computing	Fundamentals	Semantic Web Technologies
Cloud Standards	Platforms and Applications	Services Science
Enterprise Information Systems	SAAS, PAAS, IAAS	Software Agents and Internet Computing

DATA PRIVACY IN WEB ANALYTICS - An Empirical Study and Declaration Model of Data Collection on Websites

Topics: e-Business and e-Commerce; Social and Legal Issues; Privacy and Confidentiality

Authors: Darius Zumstein , Aleksandar Drobnjak and Andreas Meier

Affiliation: University of Fribourg, Switzerland

Digital Object Identifier: 10.5220/0003301404660472

Proceedings of WEBIST 2011, ISBN: 978-989-8425-51-5

Quick Abstract

Web analytics has become a useful instrument for electronic business and website management to analyze and optimize website usage. However, different concerns arise in web analytics regarding the collection, storage and usage of web data. To maintain user confidence in websites, operators need to comply with privacy and to inform truthfully about data collection. One way to achieve transparency would be by means of informing users on the purpose, methods and processes of data collection taking place and on the subsequent analysis and use of such. Results of an empirical study conducted show that 16% of the Forbes 500 listed companies, do not declare their data collection practices at all. Moreover, 35% do not declare the usage of cookies and 61% do not declare the recording of IP addresses. Surprisingly, 91% of the websites do not name the web analytics system (provider) used to track web usage and user data. A large backlog exists regarding transparency of data collection in privacy policies, especially when Google Analytics is used. This paper demands more transparency in web analytics and proposes a declaration model with seven criteria for the evaluation of data collection on websites: type of data being collected, purpose of data collection, method of data collection as well as the technology and software used for data collection. In addition, websites should provide the right to access personal data and to deactivate data collection during visits.

Related Ontology Subjects/Areas/Topics (4)

e-Business and e-Commerce		Society, e-Business and e-Government		Web Information Systems and Technologies
Social and Legal Issues

WEB ANALYTICS - Analysing, Classifying and Describing Web Metrics with Fuzzy Logic

Topics: e-Business and e-Commerce; Web Site Classification; Web-Based Direct Marketing and CRM

Author: Darius Zumstein

Affiliation: University of Fribourg, Switzerland

Digital Object Identifier: 10.5220/0002778502820290

Proceedings of WEBIST 2010, ISBN: 978-989-674-025-2

Quick Abstract

In the Internet economy, it has become a crucial task of electronic business to monitor and optimize websites, their usage and online marketing success. Web analytics, which is defined as the measurement, collection, analysis and reporting of Internet data, is an effective instrument of website management. First, this paper describes the technical functionality and use of web analytics and discusses different web metrics. Second, a fuzzy web analytics approach is proposed, which makes it possible to classify metrics precisely into more than one class at the same time. Third, a fuzzy web metrics index has been developed for multidimensional, intelligent web analysis. Fuzzy logic enables computing with words and more intuitive, human-oriented queries, segmentation and descriptions of metrics in natural language. Finally, a web analytics framework is suggested to analyze and control key performance indicators in a web controlling loop.

Related Ontology Subjects/Areas/Topics (3)

e-Business and e-Commerce

Society, e-Business and e-Government

Web Information Systems and Technologies

Checking Inherently Fair Linear-Time Properties in a Non-Näıve Way

Authors: Thierry Nicola , Frank Nießner and Ulrich Ultes-Nitsche

Affiliation: University of Fribourg, Switzerland

Digital Object Identifier: 10.5220/0001736600490058

Proceedings of MSVVEIS 2008/ICEIS, ISBN: 978-989-8111-43-2

Quick Abstract

In [9], an alternative verification relation for linear-time properties is introduced which uses an inherent fairness condition. That relation is specifically tailored to the verification of distributed systems under a relaxed version of strong fairness. We will call it the inherently fair linear-time verification relation in this paper, or IFLTV relation for short. We present an analysis of the mathematical structure of the IFLTV relation, which enables us to obtain an improved non-naïve procedure for checking the IFLTV relation.

JAVA NIO FRAMEWORK - Introducing a High-performance I/O Framework for Java

Topic: High Performance Computing

Authors: Ronny Standtke and Ulrich Ultes-Nitsche

Affiliation: Telecommunications, Networks & Security Research Group, University of Fribourg, Switzerland

Digital Object Identifier: 10.5220/0001901502060211

Proceedings of ICSOFT 2008, ISBN: 978-989-8111-51-7

Quick Abstract

A new input/output (NIO) library that provides block-oriented I/O was introduced with Java v1.4. Because of its complexity, creating network applications with the Java NIO library has been very difficult and build-in support for high-performance, distributed and parallel systems was missing. Parallel architectures are now becoming the standard in computing and Java network application programmers need a framework to build upon. In this paper, we introduce the Java NIO Framework, an extensible programming library that hides most of the NIO library details and at the same time provides support for secure and high-performance network applications. The Java NIO Framework is already used by well-known organizations, e.g. the U.S. National Institute of Standards and Technology, and is running successfully in a distributed computing framework that has more than 1000 nodes.

Related Ontology Subjects/Areas/Topics (3)

Distributed and Mobile Software Systems

Parallel and High Performance Computing

Software Engineering

Weakly Continuation Closed Homomorphisms on Automata

Authors: Thierry Nicola and Ulrich Ultes-Nitsche

Affiliation: University of Fribourg, Switzerland

Digital Object Identifier: 10.5220/0001736500390048

Proceedings of MSVVEIS 2008/ICEIS, ISBN: 978-989-8111-43-2

Quick Abstract

A major limitation of system and program verification is the state space explosion problem. To avoid this problem, there exists several approaches to reduce the size of the system state space. Some methods try to keep the state space small during the verification run, other methods reduce the original state space prior to the verification. One of the later are abstraction homomorphisms. Weakly Continuation Closed homomorphisms are abstraction homomorphisms preserving exactly those properties of the original behaviour which are satisfied inherently fair [1]. However, the practical use of WCC homomorphisms is limited by the lack of a reasonably efficient algorithm, checking whether or not a homomorphism is WCC and performing reasonably well. This paper presents a result which allows to develop algorithms for WCC on automata.

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