In the evolving landscape of e-commerce, personalizing user experience through recommendation systems has become a way to boost user satisfaction and engagement. However, small-scale e-commerce platforms struggle with significant challenges, including data sparsity and user anonymity. These issues make it hard to effectively implement recommendation systems, resulting in difficulty in recommending the right products to users. This study introduces an innovative Hybrid Recommendation System (HRS) to address challenges in e-commerce personalization caused by data sparsity and user anonymity. By blending multiple dimensions of the data into one unified system for producing recommendations, this system represents a notable advancement in web engineering for achieving personalized user experiences in the context of limited data. This research emphasizes the significance of innovative and tech-driven solutions in transforming small-scale e-commerce platforms, providing direction for future research and development in the field. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

This article presents a systematic literature review on hybrid recommendation systems (HRS) in the e-commerce sector, a field characterized by constant innovation and rapid growth. As the complexity and volume of digital data increases, recommendation systems have become essential in guiding customers to services or products that align with their interests. However, the effectiveness of single-architecture recommendation algorithms is often limited by issues such as data sparsity, challenges in understanding user needs, and the cold start problem. Hybridization, which combines multiple algorithms in different methods, has emerged as a dominant solution to these limitations. This approach is utilized in various domains, including e-commerce, where it significantly improves user experience and sales. To capture the recent trends and advancements in HRS within e-commerce over the past six years, we review the state-of-the-art overview of HRS within e-commerce. This review meticulously evaluates existing research, addressing primary inquiries and presenting findings that contribute to evidence-based decision-making, understanding research gaps, and maintaining transparency. The review begins by establishing fundamental concepts, followed by detailed methodologies, findings from addressing the research questions, and exploration of critical aspects of HRS. In summarizing and incorporating existing research, this paper offers valuable insights for researchers and outlines potential avenues for future research, ultimately providing a comprehensive overview of the current state and prospects of HRS in e-commerce.

Research in machine learning has become very popular in recent years, with many types of models proposed to comprehend and predict patterns and trends in data originating from different domains. As these models get more and more complex, it also becomes harder for users to assess and trust their results, since their internal operations are mostly hidden in black boxes. The interpretation of machine learning models is currently a hot topic in the information visualization community, with results showing that insights from machine learning models can lead to better predictions and improve the trustworthiness of the results. Due to this, multiple (and extensive) survey articles have been published recently trying to summarize the high number of original research papers published on the topic. But there is not always a clear definition of what these surveys cover, what is the overlap between them, which types of machine learning models they deal with, or what exactly is the scenario that the readers will find in each of them. In this article, we present a metaanalysis (i.e. a ‘‘survey of surveys’’) of manually collected survey papers that refer to the visual interpretation of machine learning models, including the papers discussed in the selected surveys. The aim of our article is to serve both as a detailed summary and as a guide through this survey ecosystem by acquiring, cataloging, and presenting fundamental knowledge of the state of the art and research opportunities in the area. Our results confirm the increasing trend of interpreting machine learning with visualizations in the past years, and that visualization can assist in, for example, online training processes of deep learning models and enhancing trust into machine learning. However, the question of exactly how this assistance should take place is still considered as an open challenge of the visualization community.

Machine learning (ML) models are nowadays used in complex applications in various domains such as medicine, bioinformatics, and other sciences. Due to their black box nature, however, it may sometimes be hard to understand and trust the results they provide. This has increased the demand for reliable visualization tools related to enhancing trust in ML models, which has become a prominent topic of research in the visualization community over the past decades. To provide an overview and present the frontiers of current research on the topic, we present a State-of-the-Art Report (STAR) on enhancing trust in ML models with the use of interactive visualization. We define and describe the background of the topic, introduce a categorization for visualization techniques that aim to accomplish this goal, and discuss insights and opportunities for future research directions. Among our contributions is a categorization of trust against different facets of interactive ML, expanded and improved from previous research. Our results are investigated from different analytical perspectives: (a) providing a statistical overview, (b) summarizing key findings, (c) performing topic analyses, and (d) exploring the data sets used in the individual papers, all with the support of an interactive web-based survey browser. We intend this survey to be beneficial for visualization researchers whose interests involve making ML models more trustworthy, as well as researchers and practitioners from other disciplines in their search for effective visualization techniques suitable for solving their tasks with confidence and conveying meaning to their data.

The ranking of authors is an important task within the field of sci- entometrics, and several different methods and criteria exist. In this poster abstract, we present an interactive visualization approach for exploring combinations of several different ranking criteria for a given set of publications and its associated co-author network. Ourvisualization tool allows the user to gain insights into the relative importance of individual authors as well as into the interdependency of different ranking criteria.

The visualization of large multivariate networks (MVN) continues to be a great challenge and will probably remain so for a foreseeable future. The field of Multivariate Network Embedding seeks to meet this challenge by providing MVN-specific embedding technologies that targets different properties such as network topology or attribute values for nodes or links. Although many steps forward have been taken, the goal of efficiently embedding all aspects of a MVN remains distant. This position paper contrasts the current trend of finding new ways of jointly embedding several properties with the alternative strategy of instead using, and combining, already existing state-of-the-art single scope embedding technologies. From this comparison, we argue that the latter strategy provides a more generic and flexible approach with several advantages. Hence, we hope to convince the visual analytics community to invest more work in resolving some of the key issues that would make this methodology possible.

The visualization and visual analytics of large multivariate networks (MVN) continues to be a great challenge and will probably remain so for a foreseeable future. The field of Multivariate Network Embedding seeks to meet this challenge by providing MVN-specific embedding technologies that targets different properties such as network topology or attribute values for nodes or links. Embeddings are relatively low-dimensional vector representations of the embedded items and they are well suited for similarity calculations. Although many steps forward have been taken, the goal of efficiently embedding all aspects of a MVN remains distant. As a possible way forward we suggest a new angle of approach where, instead of trying to fit all aspects of a MVN into one embedding, the strategy would be to embed each property by itself and then find ways to combine these sets of embeddings.

Comparing text documents is an essential task for a variety of applications within diverse research fields, and several different methods have been developed for this. However, calculating text similarity is an ambiguous and context-dependent task, so many open challenges still exist. In this paper, we present a novel method for text similarity calculations based on the combination of embedding technology and ensemble methods. By using several embeddings, instead of only one, we show that it is possible to achieve higher quality, which in turn is a key factor for developing high-performing applications for text similarity exploitation. We also provide a prototype visual analytics tool which helps the analyst to find optimal performing ensembles and gain insights to the inner workings of the similarity calculations. Furthermore, we discuss the generalizability of our key ideas to fields beyond the scope of text analysis.

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.

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.

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.

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.

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.

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.

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.

 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 and patients to interpret symptom states and individually tailor the treatments by visualizing the physiological information collected by sensor-based systems as well as patient self-reported states. Here we present various visualization and interaction techniques to help physicians explore patient’s daily activities, which could be useful for guiding them during the decision-making process. An interface is designed to visualize symptom and medication information, collected by an Internet of Things-based system comprising of a smartphone, electronic dosing device, wrist sensor and a bed sensor.

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 and patients to interpret symptom states and individually tailor the treatments by visualizing the physiological information collected by sensor-based systems. Here we present various visualization and interaction techniques developed to help patients track their daily activities. We also present our most recent development to aid physicians in exploring this data in more detailed fashion. Both sets of interfaces are designed to visualize symptom and medication information, collected by an Internet of Things (IoT)-based system comprising of a smartphone, electronic dosing device, wrist sensor and a bed sensor.

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.

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.

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.

BACKGROUND: Parkinson disease (PD) is a chronic degenerative disorder that causes progressive neurological deterioration with profound effects on the affected individual’s quality of life. Therefore, there is an urgent need to improve patient empowerment and clinical decision support in PD care. Home-based disease monitoring is an emerging information technology with the potential to transform the care of patients with chronic illnesses. Its acceptance and role in PD care need to be elucidated both among patients and caregivers. OBJECTIVE: Our main objective was to develop a novel home-based monitoring system (named EMPARK) with patient and clinician interface to improve patient empowerment and clinical care in PD. METHODS: We used elements of design science research and user-centered design for requirement elicitation and subsequent information and communications technology (ICT) development. Functionalities of the interfaces were the subject of user-centric multistep evaluation complemented by semantic analysis of the recorded end-user reactions. The ICT structure of EMPARK was evaluated using the ICT for patient empowerment model. RESULTS: Software and hardware system architecture for the collection and calculation of relevant parameters of disease management via home monitoring were established. Here, we describe the patient interface and the functional characteristics and evaluation of a novel clinician interface. In accordance with our previous findings with regard to the patient interface, our current results indicate an overall high utility and user acceptance of the clinician interface. Special characteristics of EMPARK in key areas of interest emerged from end-user evaluations, with clear potential for future system development and deployment in daily clinical practice. Evaluation through the principles of ICT for patient empowerment model, along with prior findings from patient interface evaluation, suggests that EMPARK has the potential to empower patients with PD. CONCLUSIONS: The EMPARK system is a novel home monitoring system for providing patients with PD and the care team with feedback on longitudinal disease activities. User-centric development and evaluation of the system indicated high user acceptance and usability. The EMPARK infrastructure would empower patients and could be used for future applications in daily care and research.

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.

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. 

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.

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.

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. 

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.

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.

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).

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.

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.

The recent development in the data analytics field provides a boost in production for modern industries. Small-sized factories intend to take full advantage of the data collected by sensors used in their machinery. The ultimate goal is to minimize cost and maximize quality, resulting in an increase in profit. In collaboration with domain experts, we implemented a data visualization tool to enable decision-makers in a plastic factory to improve their production process. The tool is an interactive dashboard with multiple coordinated views supporting the exploration from both local and global perspectives. In summary, we investigate three different aspects: methods for preprocessing multivariate time series data, clustering approaches for the already refined data, and visualization techniques that aid domain experts in gaining insights into the different stages of the production process. Here we present our ongoing results grounded in a human-centered development process. We adopt a formative evaluation approach to continuously upgrade our dashboard design that eventually meets partners’ requirements and follows the best practices within the field. We also conducted a case study with a domain expert to validate the potential application of the tool in the real-life context. Finally, we assessed the usability and usefulness of the tool with a two-layer summative evaluation that showed encouraging results.

The recent development in the data analytics field provides a boost in production for modern industries. Small-sized factories intend to take full advantage of the data collected by sensors used in their machinery. The ultimate goal is to minimize cost and maximize quality, resulting in an increase in profit. In collaboration with domain experts, we implemented a data visualization tool to enable decision-makers in a plastic factory to improve their production process. We investigate three different aspects: methods for preprocessing multivariate time series data, clustering approaches for the already refined data, and visualization techniques that aid domain experts in gaining insights into the different stages of the production process. Here we present our ongoing results grounded in a human-centered development process. We adopt a formative evaluation approach to continuously upgrade our dashboard design that eventually meets partners’ requirements and follows the best practices within the field.

Natural language processing in combination with visualization can provide efficient ways to discover latent patterns of similarity which can be useful for exploring large sets of text documents. In this poster abstract, we describe the ongoing work on a visual analytics application, called SimBaTex, which is based on embedding technology, dynamic specification of similarity criteria, and a novel approach for similarity-based clustering. The goal of SimBaTex is to provide search-and-explore functionality to enable the user to identify items of interest in a large set of text documents by interactive assessment of both high-level similarity patterns and pairwise similarity of chosen texts.

Embeddings are powerful tools for transforming complex and unstructured data into numeric formats suitable for computational analysis tasks. In this paper, we extend our previous work on using multiple embeddings for text similarity calculations to the field of networks. The embedding ensemble approach improves network reconstruction performance compared to single-embedding strategies. Our visual analytics methodology is successful in handling both text and network data, which demonstrates its generalizability beyond its originally presented scope.

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.