After the second challenging year in the pandemic, the Human-Computer Interaction and Visual Analytics team at the h_da wishes you peace, joy, and prosperity throughout the coming year 2022.
Thank you for your continuous support and partnership. We look forward to working with you in the upcoming year.
We are again co-organizing the track International Symposium Visual Analytics and Data Science (VA) at the next International Information Visualisation Conference (iV 2022) in Vienna, Austria on 19-22 July 2020. The Information Visualisation Conference (iV) is an international conference that aims to provide a foundation for integrating the human-centered, technological and strategic aspects of information visualization to promote international exchange, cooperation, and development.
In the present information era, most aspects of our lives depend on and are driven by data, information, knowledge, user experience, and culture. Information Visualisation, Visual Analytics, Business Intelligence, Machine Learning, and Data Science are only a few research areas of the current state of art enabling to increase the comprehension of the information on which is based the infrastructure of our society. The International Conference on Information Visualisation is concentrated around these hot topics. As in the previous edition, also this year there will be the opportunity of starting international cooperations and European research projects, and of discussing with international relevance keynote speakers. The conference is articulated in symposia, with details and further information available on the Symposia webpage.
Your work may be submitted as
These submissions include, but are not limited to
Interested parties in the area of Visual Computing are invited to submit paper their proposal until 15 March 2022 via the conference submission system.
Please note our summarization of the 26th International Conference Information Visualization (iV 2022) event.
We are glad to announce that our article Visual analytics for technology and innovation management: An interaction approach for strategic decision making gets published in the current special issue of the Journal of Multimedia Tools and Applications.
Abstract:
The awareness of emerging trends is essential for strategic decision making because technological trends can affect a firm’s competitiveness and market position. The rise of artificial intelligence methods allows gathering new insights and may support these decision-making processes. However, it is essential to keep the human in the loop of these complex analytical tasks, which, often lack an appropriate interaction design. Including special interactive designs for technology and innovation management is therefore essential for successfully analyzing emerging trends and using this information for strategic decision making. A combination of information visualization, trend mining and interaction design can support human users to explore, detect, and identify such trends. This paper enhances and extends a previously published first approach for integrating, enriching, mining, analyzing, identifying, and visualizing emerging trends for technology and innovation management. We introduce a novel interaction design by investigating the main ideas from technology and innovation management and enable a more appropriate interaction approach for technology foresight and innovation detection.
Link to Paper: https://doi.org/10.1007/s11042-021-10972-3
With the kickoff of the European collaboration project “Partnerships with Eastern Europe”, the Darmstadt University of Applied Sciences, and particularly our team, has decided to focus more on European cooperation. Our exchange project funded by the German Academic Exchange Service (DAAD) includes partners from Russia, Romania and Poland and aims at strengthening the collaboration with Eastern Europe.
Subject of our collaboration activities are machine learning and information visualization with a strong collaboration with the Technical University of Ulyanovsk in Russia. The project is set up for three years and involves various faculties of the Darmstadt University.
Further information
– More information to the project Partnerships with Eastern Europe
After an extremely challanging year, the Human-Computer Interaction and Visual Analytics team at h_da wishes you peace, joy and prosperity throughout the coming year.
Thank you for your continued support and partnership. We look forward to working with you in the years to come.
Prof. Dr. Kawa Nazemi acts as guest editor together with Vincenzo Deufemia and Giuseppe Polese the special issue on “Visualization in Big Data and Data Science” of the Big Data Research Journal.
Introduction
As witnessed by Covid19 outbreak, big data and data science are becoming vital disciplines in several application domains, mainly due to the great availability of big data collections from which it is possible to mine precious patterns of knowledge. The Internet of Things (IoT) industrial revolution has furtherly contributed to boost this trend, yielding an increased interest for data analytics techniques capable of working on streams of data and time series. This has led to the development of powerful predictive models, including deep neural networks, also thanks to the availability of powerful hardware and distributed computing paradigms. Nevertheless, one of the aspects that is hindering the contamination of Big Data and Data Science in many sectors is to be ascribed to the difficulty in explaining the rationale underlying complex data analytics processes to stakeholders. While it is well-known that the data pre-processing and analytics phases account for about 70% and 20% of the whole data analytics process, respectively, there is a remaining 10% of the effort that should be devoted to the visualization and explanation of the results, also known as data journalism. Although this is a small percentage, and for this reason overlooked by many data scientists, it is one of the most critical ones in order to draw the attention of stakeholders and motivate them to trust and invest on the adoption of Big Data and Data Science technologies. This is true also in artificial intelligence where explainable AI is also becoming crucial. One way to tackle this problem is to rely on efficient visualization metaphors and intelligent visual interaction paradigms.
In this respect, we expect interdisciplinary contributions from several research communities, such as, data mining, big data, machine learning, and human-computer interaction, to provide new scalable ways for visualizing the results of complex data analytics processes, including efficient interaction techniques to explore them. In particular, they should stimulate the active involvement of stakeholders in the data analytics process by enhancing their understanding capabilities.
Paper Submission Format and Guidelines
All submitted papers must be clearly written in English and must contain only original work, which has not been published by, or is currently under review for, any other journal, conference, symposium, or workshop. Submissions are expected to not exceed 30 pages (including figures, tables, and references) in the journal’s single-column format using 11-point font. Detailed submission guidelines are available under “Guide for Authors” at:
http://www.journals.elsevier.com/big-data-research/
All manuscripts and any supplementary material should be submitted through the Elsevier Editorial System (EES). The authors must select “VSI: Big Data Science Vis” as Article Type when they reach the Article Type step in the submission process. The EES website is located at:
All papers will be peer-reviewed by at least three independent reviewers. Requests for additional information should be addressed to the guest editors.
Topics for the Special Issue
Topics of interest include, but are not limited to:
· User interfaces for data science
· Visualization of time-dependent data, geo-localized data, and maps
· Data to visualization mappings
· Interactive knowledge discovery
· Visual operators on data and knowledge
· Big data visualization in various domains (health, education, politics, …)
· Visual exploration of datasets
· Visualization of data correlations
· Visual data mining and visual knowledge discovery
· Visualization of learning models
· Visualization techniques for data profiles
· Visualization for IoT data analytics
· Visual analytics for explainable AI
· Collaborative visual analytics and data science
· Adaptive visual analytics
· Visual trend analytics
· User evaluations and case studies, reports on real cases and experiments
Important Dates
Submission Deadline: February 15, 2021
Author Notification: May 15, 2021
Revised Manuscript Due: July 1, 2021
Notification of Acceptance: October 1, 2021
Final Manuscript Due: October 15, 2021
Tentative Publication Date: December, 2021
Guest Editors
Vincenzo Deufemia, University of Salerno
Kawa Nazemi, Darmstadt University of Applied Sciences
Giuseppe Polese, University of Salerno
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Three of our submitted collaboration papers got accepted at the this year’s IEEE Information Technology and Management Science Conference 2020. The conference aims at bringing together young scientists and researchers from information technologies and management sciences in an effort to promote and encourage cross-fertilization of ideas and tools related to the general topics of the conference, such as Information Technology, Information Systems, Computer Technologies, Data Processing, System Security and Control, Modelling and Simulation, Automatic Control, E-Commerce and E-Governance, Cloud Computing, Human Computer Interaction, Cyber-Physical Systems, Intelligent Systems, Internet of Everything.
Abstract:
Visual Analytics enables a deep analysis of complex and multivariate data by applying machine learning methods and interactive visualization. These complex analyses lead to gain insights and knowledge for a variety of analytics tasks to enable the decision-making process. The enablement of decision-making processes is essential for managing and planning mobility and transportation. These are influenced by a variety of indicators such as new technological developments, ecological and economic changes, political decisions and in particular humans’ mobility behavior. New technologies will lead to a different mobility behavior with new constraints. These changes in mobility behavior and logistics require analytical systems to forecast the required information and probably appearing changes. These systems have to consider different perspectives and employ multiple indicators. Visual Analytics enable such analytical tasks. We introduce in this paper the main indicators for Visual Analytics for mobility and transportation that are e exemplary explained through two case studies to illustrate the advantages of such systems. The examples are aimed to demonstrate the benefits of Visual Analytics in mobility.
Link to paper/fulltext: DOI: 10.1109/ITMS51158.2020.9259321
More information about the technology and topic: Scitics for Visual Trend Analytics, Business Analytics, Trend Analytics and Technology Foresights
Abstract:
A variety of new technologies and business ideas are arising in the domain of mobility and logistics. It can be differentiated between fundamental new approaches, e.g. central packaging stations or deliveries via drones and minor technological advancements that aim on more ecologically and economic transportation. The need for analytical systems that enable identifying new technologies, innovations, business models etc. and give also the opportunity to rate those in perspective of business relevance is growing. The users’ behavior is commonly investigated in adaptive systems, which is considering the induvial preferences of users, but neglecting often the tasks and goals of the analysis. A process-related supports could assist to solve an analytical task in a more efficient and effective way. We introduce in this paper an approach that enables non-professionals to perform visual trend analysis through an advanced process assistance based on process mining and visual adaptation. This allows to calculate a process model based on events, which is the baseline for process support feature calculation. These features in form of visual adaptations and the process model enable assisting non-experts in complex analytical tasks.
Link to paper/fulltext: DOI: 10.1109/ITMS51158.2020.9259309
More information about the technology and topic: Scitics for Visual Trend Analytics, Business Analytics, Trend Analytics and Technology Foresights
Abstract:
Urban transport infrastructure nowadays involves environmentally friendly modes of transport, the most democratic of which is cycling. Citizens will use bicycles if a reasonably designed cycle path scheme will be provided. Cyclists also need to know the characteristics and load of the planned route before the trip. Prediction can be provided by simulation, but it is often necessary to use heterogeneous and distributed models that require a specific communication environment to ensure interaction. The article describes an easy communication environment that is used to implement interaction and interoperability in a multi-agent-based cycle path design and exploitation simulator, where each domain simulation is performed as a microservice.
Link to paper/fulltext: DOI: 10.1109/ITMS51158.2020.9259299
More information about the the topic: eGovernance and Policy Modeling
With the kickoff of the e-Learning project Digitallabor, we are supporting the education and training strategy of digital teaching in vocational training. Especially the COVID-19 crisis increased the need for solutions and approaches to enable digital trainings. However, there are many challenges, starting with the required infrastructure, moving further to pedagogic strategies and ending in individual electronic learning features.
In the project, any step has to be cover. Together with the project partners in the field of digital education, it is intended to use the opportunities offered by digitization for vocational education and training and to open up access possibilities for multipliers and teachers.
Further information
– More information to the project Digitallabor
At the this year’s Collaborative European Research Conference (CERC 2020) two of our students papers titled “Visual Dashboards in Trend Analytics to Observe Competitors and Leading Domain Experts” and “A Future Prospect for European Collaboration on Advanced Analytics in Economy and Society” were accepted for presentation. Due to Corona epidemic the conference is hold virtually. The multidisciplinary CERC is an annual event that takes place since 2011 when it was initiated by University partners across Europe. It brings together researchers from a wide range of disciplines in order to foster knowledge transfer, inter-disciplinary exchange and collaboration.
Abstract:
The rapid changes due to digitalization challenges a variety of market players and forces them to find strategies to be aware of changes in these markets, particularly those that impacts their business. The main challenge is how a practical solution could look like and how technology can support market players in these trend ob-servation tasks. The paper outlines therefore a technological solution to observe specific authors e.g. researchers who influence a certain market or engineers of competitor. In many branches both are well-known groups to market players and there is almost the need of a technology that support the topical observation. The main contributions of this paper are next to the concept of how a visual dash-board could enable a market observation and how data has to be processed for it, the prototypical implementation which enables an evaluation later on. Further-more, the definition of a principal technological analysis for innovation and tech-nology management is created and is also an important contribution to the scien-tific community that specifically considers the technology perspective and it cor-responding requirements.
Link to paper/fulltext: http://ceur-ws.org/Vol-2815/CERC2020_paper14.pdf
More information about the technology: Scitics for Visual Trend Analytics
Abstract:
Analytical Reasoning by applying machine learning approaches, artificial intelli-gence, NLP and visualizations allow to get deep insights into the different do-mains of various stakeholders and enable to solve complex tasks. Thereby the tasks are very heterogenous and subject of investigation in the different areas of application. These tasks or challenges should be defined by the stakeholders themselves and lead through a deep investigation to advanced analytical ap-proaches. We therefore set up a strategic alliance of research, enterprises and so-cietal organization with the goal of a strong collaboration to identify in a first step these challenges and workout technological solutions for each application scenar-io. We give in this paper a first draft of current challenges and technological ad-vancements. The main contribution of this paper is next to an accurate description of the current challenges in the analytics domain, also the description of an agen-da how these challenges can be solved. Furthermore, a process is explained, how the strategic alliance should act and organize their work to realize beneficial and useful analytical solutions.
Link to paper/fulltext: http://ceur-ws.org/Vol-2815/CERC2020_paper27.pdf
More information about the project and the expected collaboration project: EuroStraNet-project, VisCOST-project, partner network for COST
The whole proceedings of the CERC 2020 are available under: http://ceur-ws.org/Vol-2815/.
We are very glad to be accepted for presenting our papers titled “Comparison of Full-text Articles and Abstracts for Visual Trend Analytics through Natural Language Processing” and “An Industry 4.0-Ready Visual Analytics Model for Context-Aware Diagnosis in Smart Manufacturing” at the high-class conference Information Visualisation Conference (iV 2020). Due to Corona epidemic the conference is hold virtually. The iV 2020 is an international conference that aims to provide a foundation for integrating the human-centered, technological and strategic aspects of information visualization to promote international exchange, cooperation and development.
Abstract:
Scientific publications are an essential resource for detecting emerging trends and innovations in a very early stage, by far earlier than patents may allow. Thereby Visual Analytics systems enable a deep analysis by applying commonly unsupervised machine learning methods and investigating a mass amount of data. A main question from the Visual Analytics viewpoint in this context is, do abstracts of scientific publications provide a similar analysis capability compared to their corresponding full-texts? This would allow to extract a mass amount of text documents in a much faster manner. We compare in this paper the topic extraction methods LSI and LDA by using full text articles and their corresponding abstracts to obtain which method and which data are better suited for a Visual Analytics system for Technology and Corporate Foresight. Based on a easy replicable natural language processing approach, we further investigate the impact of lemmatization for LDA and LSI. The comparison will be performed qualitative and quantitative to gather both, the human perception in visual systems and coherence values. Based on an application scenario a visual trend analytics system will further illustrate the outcomes.
Link to paper/fulltext: DOI: 10.1109/10.1109/IV51561.2020.00065
More information about the technology: Scitics for Visual Trend Analytics
Abstract:
The integrated cyber-physical systems in Smart Manufacturing generate continuously vast amount of data. These complex data are difficult to assess and gather knowledge about the data. Tasks like fault detection and diagnosis are therewith difficult to solve. Visual Analytics mitigates complexity through the combined use of algorithms and visualization methods that allow to perceive information in a more accurate way. Thereby, reasoning relies more and more on the given situation within a smart manufacturing environment, namely the context. Current general Visual Analytics approaches only provide a vague definition of context. We introduce in this paper a model that specifies the context in Visual Analytics for Smart Manufacturing. Additionally, our model bridges the latest advances in research on Smart Manufacturing and Visual Analytics. We combine and summarize methodologies, algorithms and specifications of both vital research fields with our previous findings and fuse them together. As a result, we propose our novel industry 4.0-ready Visual Analytics model for context-aware diagnosis in Smart Manufacturing.
Link to paper/fulltext: DOI: 10.1109/10.1109/IV51561.2020.00064
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