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  • 1.
    Gimenez, Alfredo
    et al.
    University of California, USA.
    Gamblin, Todd
    Lawrence Livermore National Laboratory, USA.
    Jusufi, Ilir
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Bhatele, Abhinav
    Lawrence Livermore National Laboratory, USA.
    Schulz, Martin
    Lawrence Livermore National Laboratory, USA.
    Bremer, Peer-Timo
    Lawrence Livermore National Laboratory, USA.
    Hamann, Bernd
    University of California, USA.
    MemAxes: Visualization and Analytics for Characterizing Complex Memory Performance Behaviors2018In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, Vol. 24, no 7, p. 2180-2193Article in journal (Refereed)
    Abstract [en]

    Memory performance is often a major bottleneck for high-performance computing (HPC) applications. Deepening memory hierarchies, complex memory management, and non-uniform access times have made memory performance behavior difficult to characterize, and users require novel, sophisticated tools to analyze and optimize this aspect of their codes. Existing tools target only specific factors of memory performance, such as hardware layout, allocations, or access instructions. However, today's tools do not suffice to characterize the complex relationships between these factors. Further, they require advanced expertise to be used effectively. We present MemAxes, a tool based on a novel approach for analytic-driven visualization of memory performance data. MemAxes uniquely allows users to analyze the different aspects related to memory performance by providing multiple visual contexts for a centralized dataset. We define mappings of sampled memory access data to new and existing visual metaphors, each of which enabling a user to perform different analysis tasks. We present methods to guide user interaction by scoring subsets of the data based on known performance problems. This scoring is used to provide visual cues and automatically extract clusters of interest. We designed MemAxes in collaboration with experts in HPC and demonstrate its effectiveness in case studies.

  • 2.
    Giménez, Alfredo
    et al.
    University of California, Davis, California.
    Gamblin, Todd
    Lawrence Livermore National Laboratory, USA.
    Rountree, Barry
    Lawrence Livermore National Laboratory, USA.
    Bhatele, Abhinav
    Lawrence Livermore National Laboratory, USA.
    Jusufi, Ilir
    University of California, Davis, USA.
    Bremer, Peer-Timo
    Lawrence Livermore National Laboratory, USA.
    Hamann, Bernd
    University of California, Davis, California.
    Dissecting On-node Memory Access Performance: A Semantic Approach2014In: SC14: International Conference for High Performance Computing, Networking, Storage and Analysis, Piscataway, NJ, USA: IEEE Press, 2014, p. 166-176Conference paper (Refereed)
    Abstract [en]

    Optimizing memory access is critical for performance and power efficiency. CPU manufacturers have developed sampling-based performance measurement units (PMUs) that report precise costs of memory accesses at specific addresses. However, this data is too low-level to be meaningfully interpreted and contains an excessive amount of irrelevant or uninteresting information.

    We have developed a method to gather fine-grained memory access performance data for specific data objects and regions of code with low overhead and attribute semantic information to the sampled memory accesses. This information provides the context necessary to more effectively interpret the data. We have developed a tool that performs this sampling and attribution and used the tool to discover and diagnose performance problems in real-world applications. Our techniques provide useful insight into the memory behavior of applications and allow programmers to understand the performance ramifications of key design decisions: domain decomposition, multi-threading, and data motion within distributed memory systems.

  • 3. Gleicher, Michael
    et al.
    Albers, D.
    Walker, R.
    Jusufi, Ilir
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Hansen, C. D.
    Roberts, Jonathan C.
    Visual Comparison for Information Visualization2011In: Information Visualization, ISSN 1473-8716, E-ISSN 1473-8724, Vol. 10, no 5-4, p. 289-309Article in journal (Refereed)
    Abstract [en]

    Data analysis often involves the comparison of complex objects. With the ever increasing amounts and complexity of data, the demand for systems to help with these comparisons is also growing. Increasingly, information visualization tools support such comparisons explicitly, beyond simply allowing a viewer to examine each object individually. In this paper, we argue that the design of information visualizations of complex objects can, and should, be studied in general, that is independently of what those objects are. As a first step in developing this general understanding of comparison, we propose a general taxonomy of visual designs for comparison that groups designs into three basic categories, which can be combined. To clarify the taxonomy and validate its completeness, we provide a survey of work in information visualization related to comparison. Although we find a great diversity of systems and approaches, we see that all designs are assembled from the building blocks of juxtaposition, superposition and explicit encodings. This initial exploration shows the power of our model, and suggests future challenges in developing a general understanding of comparative visualization and facilitating the development of more comparative visualization tools.

  • 4.
    Golub, Koraljka
    et al.
    Linnaeus University, Faculty of Arts and Humanities, Department of Cultural Sciences.
    Tyrkkö, Jukka
    Linnaeus University, Faculty of Arts and Humanities, Department of Languages.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Jusufi, Ilir
    Linnaeus University, Faculty of Technology, Department of Media Technology.
    Ardö, Anders
    Lund University.
    Automatic subject classification for improving retrieval in a Swedish repository2017In: ISKO UK Conference 2017: Knowledge Organization: what's the story?, 11 – 12 September 2017, London, 2017Conference paper (Refereed)
    Abstract [en]

    The recent adoption of the Dewey Decimal Classification (DDC) in Sweden has ignited discussions about automated subject classification especially for digital collections, which generally seem to lack subject indexing from controlled vocabularies. This is particularly problematic in the context of academic resource retrieval tasks, which require an understanding of discipline-specific terminologies and the narratives behind their internal ontologies. The currently available experimental classification software have not been adequately tested and their usefulness is unproven especially for Swedish language resources. We address these issues by investigating a unifying framework of automatic subject indexing for the DDC, including an analysis of suitable interactive visualisation features for supporting these aims. We will address the disciplinary narratives behind the DDC in selected subject areas and the preliminary results will include an analysis of the data collection and a breakdown of the methodology. Major visualisation possibilities in support of the classification process are also outlined. The project will contribute significantly to Swedish information infrastructure by improving the findability of Swedish research resources by subject searching, one of the most common yet the most challenging types of searching.

  • 5.
    Isaacs, Katherin E.
    et al.
    University of California, Davis, USA.
    Giménez, Alfredo
    University of California, Davis, USA.
    Jusufi, Ilir
    University of California, Davis, USA.
    Gamblin, Todd
    Lawrence Livermore National Laboratory, USA.
    Bhatele, Abhinav
    Lawrence Livermore National Laboratory, USA.
    Schulz, Martin
    Lawrence Livermore National Laboratory, USA.
    Hamann, Bernd
    University of California, Davis, USA.
    Bremer, Peer-Timo
    Lawrence Livermore National Laboratory, USA.
    State of the Art of Performance Visualization2014In: Eurographics Conference on Visualization (EuroVis) (2014) / [ed] R. Borgo and R. Maciejewski and I. Viola, Eurographics - European Association for Computer Graphics, 2014, p. 141-160Conference paper (Refereed)
    Abstract [en]

    Performance visualization comprises techniques that aid developers and analysts in improving the time and energy efficiency of their software. In this work, we discuss performance as it relates to visualization and survey existing approaches in performance visualization. We present an overview of what types of performance data can be collected and a categorization of the types of goals that performance visualization techniques can address. We develop a taxonomy for the contexts in which different performance visualizations reside and describe the state of the art research pertaining to each. Finally, we discuss unaddressed and future challenges in performance visualization.

  • 6.
    Isaacs, Katherine
    et al.
    University of California, USA.
    Bremer, Peer-Timo
    Lawrence Livermore National Laboratory, USA.
    Jusufi, Ilir
    University of California, Davis, USA.
    Gamblin, Todd
    Lawrence Livermore National Laboratory, USA.
    Bhatele, Abhinav
    Lawrence Livermore National Laboratory, USA.
    Schulz, Martin
    Lawrence Livermore National Laboratory, USA.
    Hamann, Bernd
    University of California, Davis, USA.
    Combing the Communication Hairball: Visualizing Parallel Execution Traces using Logical Time2014In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, Vol. 20, no 12, p. 2349-2358Article in journal (Refereed)
    Abstract [en]

    With the continuous rise in complexity of modern supercomputers, optimizing the performance of large-scale parallel programs is becoming increasingly challenging. Simultaneously, the growth in scale magnifies the impact of even minor inefficiencies - potentially millions of compute hours and megawatts in power consumption can be wasted on avoidable mistakes or sub-optimal algorithms. This makes performance analysis and optimization critical elements in the software development process. One of the most common forms of performance analysis is to study execution traces, which record a history of per-process events and interprocess messages in a parallel application. Trace visualizations allow users to browse this event history and search for insights into the observed performance behavior. However, current visualizations are difficult to understand even for small process counts and do not scale gracefully beyond a few hundred processes. Organizing events in time leads to a virtually unintelligible conglomerate of interleaved events and moderately high process counts overtax even the largest display. As an alternative, we present a new trace visualization approach based on transforming the event history into logical time inferred directly from happened-before relationships. This emphasizes the code’s structural behavior, which is much more familiar to the application developer. The original timing data, or other information, is then encoded through color, leading to a more intuitive visualization. Furthermore, we use the discrete nature of logical timelines to cluster processes according to their local behavior leading to a scalable visualization of even long traces on large process counts. We demonstrate our system using two case studies on large-scale parallel codes.

  • 7.
    Jusufi, Ilir
    Linnaeus University, Faculty of Engineering and Technology, Department of Computer Science.
    Multivariate Networks: Visualization and Interaction Techniques2013Doctoral thesis, monograph (Other academic)
    Abstract [en]

    As more and more data is created each day, researchers from different science domains are trying to make sense of it. A lot of this data, for example our connections to friends on different social networking websites, can be modeled as graphs, where the nodes are actors and the edges are relationships between them. Researchers analyze this data to find new forms of communication, to explore different social groups or subgroups, to detect illegal activities or to seek for different communication patterns that could help companies in their marketing campaigns. Another example are huge networks in system biology. Their visualization is crucial for the understanding of living beings. The topological structure of a network on its own could give insight into the existence or distribution of interesting actors in the network. However, this is often not enough to understand complex network systems in real-world applications. The reason for this is that all the network elements (nodes or edges) are not simple one-dimensional data. For instance in biology, experiments can be performed on biological networks. These experiments and network analysis approaches produce additional data that are often important to be analyzed with respect to the underlying network structure. Therefore, it is crucial to visualize the additional attributes of the network while preserving the network structure as much as possible. The problem is not trivial as these so-called multivariate networks could have a high number of attributes that are related to their nodes, edges, different groups, or clusters of nodes and/or edges.

    The aim of this thesis is to contribute to the development of different visualization and interaction techniques for the visual analysis of multivariate networks. Two research goals are defined in this thesis: first, a deeper understanding of existing approaches for visualizing multivariate networks should be acquired in order to classify them into categories and to identify disadvantages or unsolved visualization challenges. The second goal is to develop visualization and interaction techniques that will overcome various issues of these approaches.

    Initially, a brief survey on techniques to visualize multivariate networks is presented in this thesis. Afterwards, a small task-based user study investigating the usefulness of two main approaches for multivariate network visualization is discussed. Then, various visualization and interaction techniques for multivariate network visualization are presented. Three different software tools were implemented to demonstrate our research efforts. All features of our systems are highlighted, including a description of visualization and interaction techniques as well as disadvantages and scalability issues if present.

  • 8.
    Jusufi, Ilir
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Towards the Visualization of Multivariate Biochemical Networks2012Licentiate thesis, monograph (Other academic)
    Abstract [en]

     Many open challenges exist when dealing with different biological networks. They are crucial for the understanding of living beings. Complete drawings of these typically large networks usually suffer from clutter and visual overload. In order to overcome this issue, the networks are divided into single, hierarchically structured pathways. However, this subdivision makes it harder to navigate and understand the connections between pathways. Another challenge is to visualize ontologies and hierarchical clusterings, which are important tools to study high-throughput data that are automatically generated nowadays. Both of these methods produce different types of large graphs. Although these methods are used to explore the same data set, they are usually considered independently. Therefore, a combined view showing the results of both methods is desired. Additionally, real life data sets, including biological networks, usually have additional attributes related to the considered network. Investigating means to visualize such multivariate data together with the network drawing is also one of the ongoing challenges in biology, but also in other fields.

    The aim of this thesis is to lay out the foundations towards defining techniques for the visualization of multivariate biochemical networks. An overall understanding of the problems related to biochemical networks should be acquired to achieve this aim. More importantly, a contribution to the aforementioned challenges is necessary.

    Two research goals have been defined to accomplish our aim: for the first goal, we should improve shortcomings of the approach of dividing larger biological networks into smaller pieces and contribute to the problem of a visualization of different types of interconnected biological networks. The second goal is a contribution for the visualization of multivariate biological networks.

    Initially, a brief survey on techniques to visualize multivariate networks is presented in this thesis. Then, various visualization and interaction techniques are presented that address the challenges in biochemical network analysis. Three different software tools were implemented to demonstrate our research efforts. We discuss all features of our systems in detail, describe the visualization and interaction techniques as well as disadvantages and scalability issues if present.

  • 9.
    Jusufi, Ilir
    et al.
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Dingjie, Yang
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Kerren, Andreas
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    The Network Lens: Interactive Exploration of Multivariate Networks Using Visual Filtering2010In: Proceedings of the 14th International Conference on Information Visualisation (IV '10), IEEE, 2010, p. 35-42Conference paper (Refereed)
    Abstract [en]

    Networks are widely used in modeling relational data often comprised of thousands of nodes and edges. This kind of data alone implies a challenge for its visualization as it is hard to avoid clutter of network elements if using traditional node-link diagrams. Moreover, real-life network data sets usually represent objects with a large number of additional attributes that need to be visualized, such as in software engineering, social network analysis, or biochemistry. In this paper, we present a novel approach, called Network Lens, to visualize such attributes in context of the underlying network. Our implementation of the Network Lens is an interactive tool that extends the idea of so-called magic lenses in such a way that users can interactively build and combine various lenses by specifying different attributes and selecting suitable visual representations.

  • 10.
    Jusufi, Ilir
    et al.
    Växjö University, Faculty of Mathematics/Science/Technology, School of Mathematics and Systems Engineering.
    Junuzi, Lulzim
    Växjö University, Faculty of Mathematics/Science/Technology, School of Mathematics and Systems Engineering.
    Content Visualization of GeoAudio Notes2008Independent thesis Advanced level (degree of Master (One Year)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The total population of GPS-enabled location-based services (LBS) subscribers is constantly increasing. This fact implies new research possibilities for visualizing geospatial data produced by these mobile devices. The aim of this thesis is to explore novel techniques and methods to visualize the content of voice notes (messages recorded by users on GPS-enabled devices) that will be placed in maps using GPS coordinates, and visualize the semantical, temporal, and spatial relations between the notes. Our research is part of the Geovisualization field which deals with geospatial data.

    Based on our research and analyzes of this problem, we combined different visualization and interaction techniques, thus providing a novel approach to achieve the research aim. We have built a prototype application, called GNV System (GeoAudio Notes Visualization System), that demonstrates our achievements.

  • 11.
    Jusufi, Ilir
    et al.
    Växjö University, Faculty of Mathematics/Science/Technology, School of Mathematics and Systems Engineering. Computer Science.
    Junuzi, Lulzim
    Kerren, Andreas
    Växjö University, Faculty of Mathematics/Science/Technology, School of Mathematics and Systems Engineering. Computer Science.
    Milrad, Marcelo
    Växjö University, Faculty of Mathematics/Science/Technology, School of Mathematics and Systems Engineering.
    Visualization of Content and Semantical Relations of Geonotes2008In: Proceedings of the 8th IASTED International Conference on Visualization, Imaging, and Image Processing (VIIP '08), ACTA Press , 2008, p. 131-136Conference paper (Refereed)
  • 12.
    Jusufi, Ilir
    et al.
    Linnaeus University, Faculty of Technology, Department of Media Technology.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Network Visualization for Digital Humanities: Two Case Studies of Visual Analyses for Text Analytics2016In: International Symposium on Digital Humanities, Växjö 7-8 November 2016: Book of Abstracts, Linnaeus University , 2016, p. 39-43Conference paper (Refereed)
    Abstract [en]

    Much of the data created nowadays in fields such as Digital Humanities (DH) is of relational nature, such as social or semantic networks. Researchers often decide to depict networks as node-link diagrams to make a better sense of the complex nature of data. Understanding the topology of such a network can be very important. For instance, if we show our friends as network nodes and their friendship as edges between the nodes, it becomes easy to identify groups of friends from different social settings (work friends, high school friends, etc.).

    Networks usually have additional attributes attached to their elements. For instance, we can model a number of documents in a repository as nodes and use edges to describe co-authorship. Additionally, we might want to explore other aspects of such a corpus, like the keywords for each document, its genre, and various other data associated. Here, it is often desirable to get an overview about the network structure and how different data values relate to this structure. In this paper, we present two case studies for visualizations in DH with a focus on publication networks. But first, we will introduce our data sets used in these studies.

  • 13.
    Jusufi, Ilir
    et al.
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Kerren, Andreas
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Aleksakhin, Vladyslav
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Schreiber, Falk
    Martin-Luther University.
    Visualization of Mappings between the Gene Ontology and Cluster Trees2012In: Proceedings of the SPIE 2012 Conference on Visualization and Data Analysis (VDA '12) / [ed] Pak Chung Wong, David L. Kao, Ming C. Hao, Chaomei Chen, Robert Kosara, Mark A. Livingston, Jinah Park, and Ian Roberts, SPIE - International Society for Optical Engineering, 2012Conference paper (Refereed)
    Abstract [en]

    Ontologies and hierarchical clustering are both important tools in biology and medicine to study high-throughput data such as transcriptomics and metabolomics data. Enrichment of ontology terms in the data is used to identify statistically overrepresented ontology terms, giving insight into relevant biological processes or functional modules. Hierarchical clustering is a standard method to analyze and visualize data to find relatively homogeneous clusters of experimental data points. Both methods support the analysis of the same data set, but are usually considered independently. However, often a combined view is desired: visualizing a large data set in the context of an ontology under consideration of a clustering of the data. This paper proposes a new visualization method for this task.

  • 14.
    Jusufi, Ilir
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Liu, Jiayi
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Zimmer, Björn
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Visual Exploration of Relationships between Document Clusters2014In: IVAPP 2014: Proceedings od the 5th International Conference on Information Visualization Theory and Applications / [ed] Robert S. Laramee, Andreas Kerren, José Braz, SciTePress, 2014, p. 195-203Conference paper (Refereed)
    Abstract [en]

    The visualization of networks with additional attributes attached to the network elements is one of the ongoing challenges in the information visualization domain. Such so-called multivariate networks regularly appear in various application fields, for instance, in data sets which describe friendship networks or co-authorship networks. Here, we focus on networks that are based on text documents, i.e., the network nodes represent documents and the edges show relationships between them. Those relationships can be derived from common topics or common co-authors. Attached attributes may be specific keywords (topics), keyword frequencies, etc. The analysis of such multivariate networks is challenging, because a deeper understanding of the data provided depends on effective visualization and interaction techniques that are able to bring all types of information together. In addition, automatic analysis methods should be used to support the analysis process of potentially large amounts of data. In this paper, we present a visualization approach that tackles those analysis problems. Our implementation provides a combination of new techniques that shows intra-cluster and inter-cluster relations while giving insight into the content of the cluster attributes. Hence, it facilitates the interactive exploration of the networks under consideration by showing the relationships between node clusters in context of network topology and multivariate attributes.

  • 15.
    Jusufi, Ilir
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Schreiber, Falk
    Univ Halle Wittenberg, Inst Comp Sci, D-06108 Halle, Germany.
    Exploring Biological Data: Mappings between Ontology- and Cluster-based Representations2013In: Information Visualization, ISSN 1473-8716, E-ISSN 1473-8724, Vol. 12, no 3-4, p. 291-307Article in journal (Refereed)
    Abstract [en]

    Ontologies and hierarchical clustering are both important tools in biology and medicineto study high-throughput data such as transcriptomics and metabolomics data. Enrichmentof ontology terms in the data is used to identify statistically overrepresented ontology terms,giving insight into relevant biological processes or functional modules. Hierarchical clusteringis a standard method to analyze and visualize data to find relatively homogeneousclusters of experimental data points. Both methods support the analysis of the same dataset, but are usually considered independently. However, often a combined view is desired:visualizing a large data set in the context of an ontology under consideration of a clusteringof the data. This article proposes new visualization methods for this task. They allow forinteractive selection and navigation to explore the data under consideration as well as visualanalysis of mappings between ontology- and cluster-based space-filling representations. Inthis context, we discuss our approach together with specific properties of the biological inputdata and identify features that make our approach easily usable for domain experts.

  • 16.
    Jusufi, Ilir
    et al.
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Kerren, Andreas
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Wang, Yuanmao
    A New Radial Space-Filling Visualization Approach for Planar st-Graphs2012In: Poster Abstracts of IEEE VisWeek 2012, 2012Conference paper (Refereed)
    Abstract [en]

    Planar st-graphs are used in a number of different application fieldsin the sciences, but also in industry. So far, mainly node-link-basedlayouts have been used to visualize such graphs especially in theGraph Drawing community. One drawback of these standard layoutsis their high consumption of space. In Information Visualization,there exist visualization techniques for graphs which achieveconsiderable space savings, such as matrix-based approaches. Inthis work, we present a novel space-filling representation to visualizeplanar st-graphs.

  • 17.
    Jusufi, Ilir
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Zimmer, Björn
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Multivariate Network Exploration with JauntyNets2013In: Proceedings 2013 17th International Conference on Information Visualisation IV 2013: 16-18 July 2013, London, United Kingdom, IEEE, 2013, p. 19-27Conference paper (Refereed)
    Abstract [en]

    The amount of data produced in the world every day implies a huge challenge in understanding and extracting knowledge from it. Much of this data is of relational nature, such as social networks, metabolic pathways, or links between software components. Traditionally, those networks are represented as node-link diagrams or matrix representations. They help us to understand the structure (topology) of the relational data. However in many real world data sets, additional (often multidimensional) attributes are attached to the network elements. One challenge is to show these attributes in context of the underlying network topology in order to support the user in further analyses. In this paper, we present a novel approach that extends traditional force-based graph layouts to create an attribute-driven layout. In addition, our prototype implementation supports interactive exploration by introducing clustering and multidimensional scaling into the analysis process.

  • 18.
    Jusufi, Ilir
    et al.
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Klukas, Christian
    Kerren, Andreas
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Schreiber, Falk
    Guiding the Interactive Exploration of Metabolic Pathway Interconnections2012In: Information Visualization, ISSN 1473-8716, E-ISSN 1473-8724, Vol. 11, no 2, p. 136-150Article in journal (Refereed)
    Abstract [en]

    Approaches to investigate biological processes have been of strong interest in the past few years and are thefocus of several research areas, especially Systems Biology. Biochemical networks as representations ofprocesses are very important for a comprehensive understanding of living beings. Drawings of these networksare often visually overloaded and do not scale. A common solution to deal with this complexity is to divide thecomplete network, for example, the metabolism, into a large set of single pathways that are hierarchicallystructured. If those pathways are visualized, this strategy generates additional navigation and explorationproblems as the user loses the context within the complete network.

    In this article, we present a general solution to this problem of visualizing interconnected pathways anddiscuss it in context of biochemical networks. Our new visualization approach supports the analyst in obtainingan overview to related pathways if they are working within a particular pathway of interest. By usingglyphs, brushing, and topological information of the related pathways, our interactive visualization is ableto intuitively guide the exploration and navigation process, and thus the analysis processes too. To deal withreal data and current networks, our tool has been implemented as a plugin for the VANTED system.

  • 19.
    Jusufi, Ilir
    et al.
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Klukas, Christian
    Kerren, Andreas
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Schreiber, Falk
    Interactive Navigation in Interconnected Biochemical Pathways2010Other (Other academic)
    Abstract [en]

    Approaches to investigate biological processes have been of strong interest in the last years and are in the focus of several research areas, especially Systems Biology. Biochemical networks are very important for such a comprehensive understanding of living beings. Drawings of these networks are often visually overloaded and do not scale. A common solution to deal with this complexity is to divide the complete network into a large set of single pathways that are hierarchically structured. In this poster paper, we present a solution of visualizing and navigating interconnected biochemical pathways.

  • 20.
    Jusufi, Ilir
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Memedi, Mevludin
    Örebro University.
    Nyholm, Dag
    Uppsala University.
    TapVis: A Data Visualization Approach for Assessment of Alternating Tapping Performance in Patients with Parkinson's Disease2018In: EuroVis 2018 - Short Papers / [ed] / J. Johansson, F. Sadlo, and T. Schreck, Eurographics - European Association for Computer Graphics, 2018, p. 55-59Conference paper (Refereed)
    Abstract [en]

    Advancements in telemedicine have been helpful for frequent monitoring of patients with Parkinson's disease (PD) from remote locations and assessment of their individual symptoms and treatment-related complications. These data can be useful for helping clinicians to interpret symptom states and individually tailor the treatments by visualizing the physiological information collected by sensor-based systems. In this paper we present a visualization metaphor that represents symptom information of PD patients during tapping tests performed with a smartphone. The metaphor has been developed and evaluated with a clinician. It enabled the clinician to observe fine motor impairments and identify motor fluctuations regarding several movement aspects of patients that perform the tests from their homes.

  • 21.
    Jusufi, Ilir
    et al.
    Linnaeus University, Faculty of Technology, Department of Media Technology.
    Milrad, Marcelo
    Linnaeus University, Faculty of Technology, Department of Media Technology.
    Legaspi, Xurxo
    Linnaeus University, Faculty of Technology, Department of Media Technology.
    Interactive Exploration of Student Generated Content presented in Blogs2016In: EuroVis 2016 - Posters: Eurographics - European Association for Computer Graphics / [ed] Tobias Isenberg & Filip Sadlo, Eurographics - European Association for Computer Graphics, 2016, p. 53-55Conference paper (Refereed)
    Abstract [en]

    Nowadays blogs are regarded as tools for communication as well as an important source for spreading information in almost every subject. In recent years, school teachers have started to take advantage of this technology in order to support their educational practices. In this paper we focus on the data generated by a project involving more than 50 Swedish schools where teachers and pupils are posting content related to their astronomy class activities in their blogs with the aims of improving the teaching process. The challenge here is to find suitable methods to explore all these blogs in an interactive and discovery fashion. Our proposed solution to this challenge is to provide a visual and interactive tool for the exploration of blog corpora by teachers, pupils, project managers and parents.

  • 22.
    Jusufi, Ilir
    et al.
    Department of Computer Science, University of California Davis, CA, USA.
    Nyholm, Dag
    Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Memedi, Mevludin
    Högskolan Dalarna, Datateknik.
    Visualization of spiral drawing data of patients with Parkinson's disease2014In: Information Visualisation (IV), 2014 18th International Conference on 16-18 July 2014, IEEE conference proceedings, 2014, p. 346-350Conference paper (Refereed)
    Abstract [en]

    Patients with Parkinson's disease (PD) need to be frequently monitored in order to assess their individual symptoms and treatment-related complications. Advances in technology have introduced telemedicine for patients in remote locations. However, data produced in such settings lack much information and are not easy to analyze or interpret compared to traditional, direct contact between the patient and clinician. Therefore, there is a need to present the data using visualization techniques in order to communicate in an understandable and objective manner to the clinician. This paper presents interaction and visualization approaches used to aid clinicians in the analysis of repeated measures of spirography of PD patients gathered by means of a telemetry touch screen device. The proposed approach enables clinicians to observe fine motor impairments and identify motor fluctuations of their patients while they perform the tests from their homes using the telemetry device.

  • 23.
    Kerren, Andreas
    et al.
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Jusufi, Ilir
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    3D Kiviat Diagrams for the Interactive Analysis of Software Metric Trends2010In: Proceedings of the 5th ACM Symposium on Software Visualization (SoftVis '10), ACM Press, 2010, p. 203-204Conference paper (Refereed)
    Abstract [en]

    Previous techniques for visualizing time-series of multivariate data mostly plot the time along additional axes, are often complex, and does not support intuitive interaction. In this poster paper, we present an interactive visualization approach for the analysis of software metric trends that allows users to operate with Kiviat diagrams on 2D planes in the space and to intuitively extend this visual representation into 3D if needed.

  • 24.
    Kerren, Andreas
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Jusufi, Ilir
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    A Novel Radial Visualization Approach for Undirected Hypergraphs2013In: EuroVis: Short Papers, Eurographics - European Association for Computer Graphics, 2013, p. 25-29Conference paper (Refereed)
    Abstract [en]

    Hypergraphs are a more generalized concept of graphs where an edge typically connects multiple vertices. They are applicable to many different domains such as the representation of complex biochemical pathways or classification problems with non-empty intersections between different groups, for instance, in social network analysis. There is a need to visualize those relational data structures in such a way that a better understanding of the relationships between vertices as well as their interactive exploration is supported. This paper describes a new radial visualization technique to layout undirected hypergraphs without clutter and to provide methods of interaction and data analysis. 

  • 25.
    Kerren, Andreas
    et al.
    Växjö University, Faculty of Mathematics/Science/Technology, School of Mathematics and Systems Engineering. Computer Science.
    Jusufi, Ilir
    Växjö University, Faculty of Mathematics/Science/Technology, School of Mathematics and Systems Engineering. Computer Science.
    Novel Visual Representations for Software Metrics Using 3D and Animation2009In: Software Engineering 2009 – Workshopband, GI-Edition , 2009, p. 147-154Conference paper (Refereed)
    Abstract [en]

    The visualization of software metrics is an important step towards a better understanding of the software product to be developed. Software metrics are quantitative measurements of a piece of software, e.g., a class, a package, or a component. A good understanding of software metrics supports the identification of possible problems in the development process and helps to improve the software quality. In this paper, we present two possibilities how novel visual representations can support the user to discover interesting properties within the metric data set. The first one uses a new interactive 3D metaphor to overcome known problems in the visualization of the evolution of software metrics. Then, we focus on the usage of 2D animation to represent metric values. Both approaches were implemented and address different aspects in human-centered visualization, i.e., the design of visual metaphors that are intuitive from the user perspective in the first case as well as the support of patterns in motion to facilitate the visual perception of metric outliers in the second case.

  • 26.
    Kerren, Andreas
    et al.
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Jusufi, Ilir
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Aleksakhin, Vladyslav
    Schreiber, Falk
    CluMa-GO: Bring Gene Ontologies and Hierarchical Clusterings Together2011Other (Refereed)
    Abstract [en]

    Ontologies and hierarchical clustering are both important tools in biology and medicine to study high-throughput data such as transcriptomics and metabolomics data. Enrichment of ontology terms in the data is used to identify statistically overrepresented ontology terms, giving insight into relevant biological processes or functional modules. Hierarchical clustering is a standard method to analyze and visualize data to find relatively homogeneous clusters of experimental data points. Both methods support the analysis of the same data set, but are usually considered independently. However, often a combined view is desired: visualizing a large data set in the context of an ontology under consideration of a clustering of the data. This paper proposes a new visualization method for this task.

  • 27.
    Kerren, Andreas
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Jusufi, Ilir
    University of California, USA.
    Liu, Jiayi
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Multi-Scale Trend Visualization of Long-Term Temperature Data Sets2014In: Proceedings of SIGRAD 2014, Visual Computing, June 12-13, 2014, Göteborg, Sweden / [ed] M. Obaid, D. Sjölie and M. Fjeld, Linköping University Electronic Press, 2014, p. 91-94Conference paper (Refereed)
    Abstract [en]

    The analysis and presentation of climate observations is a traditional application of various visualization approaches. The available data sets are usually huge and were typically collected over a long period of time. In this paper, we focus on the visualization of a specific aspect of climate data: our visualization tool was primarily developed for providing an overview of temperature measurements for one location over decades or even centuries. In order to support an efficient overview and visual representation of the data, it is based on a region-oriented metaphor that includes various granularity levels and aggregation features. 

  • 28.
    Kerren, Andreas
    et al.
    Växjö University, Faculty of Mathematics/Science/Technology, School of Mathematics and Systems Engineering. Computer Science.
    Jusufi, Ilir
    Växjö University, Faculty of Mathematics/Science/Technology, School of Mathematics and Systems Engineering. Computer Science.
    Milrad, Marcelo
    Växjö University, Faculty of Mathematics/Science/Technology, School of Mathematics and Systems Engineering.
    GNV System: A Tool for Visualizing Geo-tagged Data2008Other (Other academic)
    Abstract [en]

    The total population of GPS-enabled location-based services (LBS) subscribers is constantly increasing. These GPS-enabled devices produce a wide range of media content (e.g., text/audio notes, pictures, or videos) enhanced by geo-tagged information. This fact poses a challenge regarding how to store and retrieve it and opens new research opportunities for visualizing this type of data. The overall aim of our current research is to develop novel approaches and methods for visualizing the content of these documents that will be placed in maps using GPS-coordinates as well as to visualize the semantical, temporal, and spatial relations between the documents themselves. We combined different visualization and interaction techniques, such as glyph-based techniques and visual clustering, to analyze the produced data. Our prototype application, called GNV System (GeoNotes Visualization System), demonstrates the interplay of different interaction techniques and components as well as their functionality.

  • 29.
    Lincke, Alisa
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Lundberg, Jenny
    Thunander, Maria
    Lund University .
    Milrad, Marcelo
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Lundberg, Jonas
    Jusufi, Ilir
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Diabetes Information in Social Media2018In: Proceedings of the 11th International Symposium on Visual Information Communication and Interaction (VINCI '18) / [ed] Karsten Klein, Yi-Na Li, and Andreas Kerren, ACM Publications, 2018, p. 104-105Conference paper (Refereed)
    Abstract [en]

    Social media platforms have created new ways for people to communicate and express themselves. Thus, it is important to explore how e-health related information is generated and disseminated in these platforms. The aim of our current efforts is to investigate the content and flow of information when people in Sweden use Twitter to talk about diabetes related issues. To achieve our goals, we have used data mining and visualization techniques in order to explore, analyze and cluster Twitter data we have collected during a period of 10 months. Our initial results indicate that patients use Twitter to share diabetes related information and to communicate about their disease as an alternative way that complements the traditional channels used by health care professionals.

  • 30.
    Memedi, Mevludin
    et al.
    Högskolan Dalarna, Datateknik.
    Jusufi, Ilir
    Computer Science, University of California, Davis, USA.
    Nyholm, Dag
    Uppsala University, Neuroscience, Neurology.
    Visualization of spirography-based objective measures in Parkinson's disease2014In: Movement Disorders Supplement: Abstracts of the Eighteenth International Congress of Parkinson's Disease and Movement Disorders, Wiley-Blackwell, 2014, p. S187-S189Conference paper (Other academic)
    Abstract [en]

    Objective: To investigate whether advanced visualizations of spirography-based objective measures are useful in differentiating drug-related motor dysfunctions between Off and dyskinesia in Parkinson’s disease (PD).

    Background: During the course of a 3 year longitudinal clinical study, in total 65 patients (43 males and 22 females with mean age of 65) with advanced PD and 10 healthy elderly (HE) subjects (5 males and 5 females with mean age of 61) were assessed. Both patients and HE subjects performed repeated and time-stamped assessments of their objective health indicators using a test battery implemented on a telemetry touch screen handheld computer, in their home environment settings. Among other tasks, the subjects were asked to trace a pre-drawn Archimedes spiral using the dominant hand and repeat the test three times per test occasion.

    Methods: A web-based framework was developed to enable a visual exploration of relevant spirography-based kinematic features by clinicians so they can in turn evaluate the motor states of the patients i.e. Off and dyskinesia. The system uses different visualization techniques such as time series plots, animation, and interaction and organizes them into different views to aid clinicians in measuring spatial and time-dependent irregularities that could be associated with the motor states. Along with the animation view, the system displays two time series plots for representing drawing speed (blue line) and displacement from ideal trajectory (orange line). The views are coordinated and linked i.e. user interactions in one of the views will be reflected in other views. For instance, when the user points in one of the pixels in the spiral view, the circle size of the underlying pixel increases and a vertical line appears in the time series views to depict the corresponding position. In addition, in order to enable clinicians to observe erratic movements more clearly and thus improve the detection of irregularities, the system displays a color-map which gives an idea of the longevity of the spirography task. Figure 2 shows single randomly selected spirals drawn by a: A) patient who experienced dyskinesias, B) HE subject, and C) patient in Off state.

    Results: According to a domain expert (DN), the spirals drawn in the Off and dyskinesia motor states are characterized by different spatial and time features. For instance, the spiral shown in Fig. 2A was drawn by a patient who showed symptoms of dyskinesia; the drawing speed was relatively high (cf. blue-colored time series plot and the short timestamp scale in the x axis) and the spatial displacement was high (cf. orange-colored time series plot) associated with smooth deviations as a result of uncontrollable movements. The patient also exhibited low amount of hesitation which could be reflected both in the animation of the spiral as well as time series plots. In contrast, the patient who was in the Off state exhibited different kinematic features, as shown in Fig. 2C. In the case of spirals drawn by a HE subject, there was a great precision during the drawing process as well as unchanging levels of time-dependent features over the test trial, as seen in Fig. 2B.

    Conclusions: Visualizing spirography-based objective measures enables identification of trends and patterns of drug-related motor dysfunctions at the patient’s individual level. Dynamic access of visualized motor tests may be useful during the evaluation of drug-related complications such as under- and over-medications, providing decision support to clinicians during evaluation of treatment effects as well as improve the quality of life of patients and their caregivers. In future, we plan to evaluate the proposed approach by assessing within- and between-clinician variability in ratings in order to determine its actual usefulness and then use these ratings as target outcomes in supervised machine learning, similarly as it was previously done in the study performed by Memedi et al. (2013).

  • 31.
    Memedi, Mevludin
    et al.
    Örebro University.
    Tshering, Gaki
    Örebro University.
    Fogelberg, Martin
    Örebro University.
    Jusufi, Ilir
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Kolkowska, Ella
    Örebro University.
    Klein, Gunnar O.
    Örebro University.
    An interface for IoT: feeding back health-related data to Parkinson's disease patients2018In: Journal of Sensor and Actuator Networks, E-ISSN 2224-2708, Vol. 7, no 1, p. 1-16, article id 14Article in journal (Refereed)
    Abstract [en]

    This paper presents a user-centered design (UCD) process of an interface for Parkinson’s disease (PD) patients for helping them to better manage their symptoms. The interface is designed to visualize symptom and medication information, collected by an Internet of Things (IoT)-based system, which will consist of a smartphone, electronic dosing device, wrist sensor and a bed sensor. In our work, the focus is on measuring data related to some of the main health-related quality of life aspects such as motor function, sleep, medication compliance, meal intake timing in relation to medication intake, and physical exercise. A mock-up demonstrator for the interface was developed using UCD methodology in collaboration with PD patients. The research work was performed as an iterative design and evaluation process based on interviews and observations with 11 PD patients. Additional usability evaluations were conducted with three information visualization experts. Contributions include a list of requirements for the interface, results evaluating the performance of the patients when using the demonstrator during task-based evaluation sessions as well as opinions of the experts. The list of requirements included ability of the patients to track an ideal day, so they could repeat certain activities in the future as well as determine how the scores are related to each other. The patients found the visualizations as clear and easy to understand and could successfully perform the tasks. The evaluation with experts showed that the visualizations are in line with the current standards and guidelines for the intended group of users. In conclusion, the results from this work indicate that the proposed system can be considered as a tool for assisting patients in better management of the disease by giving them insights on their own aggregated symptom and medication information. However, the actual effects of providing such feedback to patients on their health-related quality of life should be investigated in a clinical trial.

  • 32.
    Memeti, Suejb
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Pllana, Sabri
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Ferati, Mexhid
    Linnaeus University, Faculty of Technology, Department of Informatics.
    Kurti, Arianit
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Jusufi, Ilir
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    IoTutor: How Cognitive Computing Can Be Applied to Internet of Things Education2019Conference paper (Refereed)
    Abstract [en]

    We present IoTutor that is a cognitive computing solution for education of students in the IoT domain. We implement the IoTutor as a platform-independent web-based application that is able to interact with users via text or speech using natural language. We train the IoTutor with selected scientific publications relevant to the IoT education. To investigate users' experience with the IoTutor, we ask a group of students taking an IoT master level course at the Linnaeus University to use the IoTutor for a period of two weeks. We ask students to express their opinions with respect to the attractiveness, perspicuity, efficiency, stimulation, and novelty of the IoTutor. The evaluation results show a trend that students express an overall positive attitude towards the IoTutor with majority of the aspects rated higher than the neutral value.

  • 33.
    Zimmer, Björn
    et al.
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Jusufi, Ilir
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Kerren, Andreas
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Analyzing Multiple Network Centralities with ViNCent2012In: Proceedings of SIGRAD 2012: Interactive Visual Analysis of Data, November 29-30, 2012, Växjö, Sweden, / [ed] Andreas Kerren and Stefan Seipel, Linköping: Linköping University Electronic Press, 2012, p. 87-90Conference paper (Refereed)
    Abstract [en]

    The analysis of multivariate networks is an important task in various application domains, such as social networkanalysis or biochemistry. In this paper, we address the interactive visual analysis of the results of centralitycomputations in context of networks. An important analytical aspect is to examine nodes according to specific centralityvalues and to compare them. We present a tool that combines exploratory data visualization with automaticanalysis techniques, such as computing a variety of centrality values for network nodes as well as hierarchicalclustering or node reordering based on centrality values. Automatic and interactive approaches are seamlesslyintegrated in one single tool which provides insight into the importance of an individual node or groups of nodesand allows quantifying the network structure.

1 - 33 of 33
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