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Best Paper Award at the iV 2021

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We are proud to announce that our paper on “Visual Analytics and Similarity Search – Interest-based Similarity Search in Scientific Data” at the iV2021 conference was honored with “The Best Paper Award” for its innovative contribution in terms of originality of concepts and application in Visual Analytics and Data Science. The “Best Paper Awards” is given to contributions that will be selected by the committee among the papers presented in iV2021 and applied for the award. The study’s relevance to the symposium’s scope, its scientific contribution, writing/presentation style will be considered in the evaluation process as well.

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. The conference was held virtually at the University of Technology, Sydney.

Article on Visual Analytics for Technology and Innovation Management Published in Journal Multimedia Tools and Applications

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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.

Article: Visual analytics for technology and innovation management: An interaction approach for strategic decision making

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

Article Published to Visual Data Analytics in impact Magazine for Applied Research and Art

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The impact Magazine for Applied Research and Art of the Darmstadt University of Applied Sciences published an article, based on an interview with Prof. Dr. Kawa Nazemi, to the research field Visual Data Analytics.

The article is about how tangible patterns can be discerned from huge data sets with the help of visual analysis and representation, and how applicable predictions can be made using a combination of artificial and human intelligence: That is the specialty of Kawa Nazemi, professor for computer science at the h_da’s Department of Media. The head of the Human-Computer Interaction and Visual Analytics research group is a sought-after expert in this still-young field and is also successfully making his mark on the international stage.

Further information

Call for Papers to the Collaborative European Research Conference 2021

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This year’s Collaborative European Research Conference (CERC) will take place with the support of scientists and members of our team. The 7th edition of the conference aims at young academics and is primarily organized and held by colleagues from Munster Technological University (Cork, Ireland). The conference will take place from 9-10 September 2021 as blended event in presence and online/virtual.

The CERC was held for the first time in 2010 by the Darmstadt University of Applied Sciences (h_da) and the Munster Technological University (Cork, Ireland) – previously named Cork Institute of Technology (CIT) – and was originally organized by lecturers from the IT department of these two institutions. The event, which takes place alternately every year at one of the partner universities, is deliberately interdisciplinary and the presented researches cover a wide range of topics – from bioinformatics to social history. This promotes the exchange of ideas between the specialist areas and thus the opportunity for new collaborations.

CERC 2021 has the goal of a broad research program. Relevant topics for the conference include, but are not limited to, the following areas:

  • eHealth
  • Natural Language Processing and its applications
  • Internet of Things, Networks and Robotics
  • Sensors and wireless temperature measurements
  • Data Computing and Artificial Intelligence
  • Business and Society
  • Visual Computing and Data Analytics
  • Cybersecurity and cybercrime
  • BioInformatics and BioMedicine

Accepted contributions will be published in digital proceedings with an ISSN number – it is intended that publications shall be submitted to CEUR-WS for online publication, indexed among other at DBLP, Scopus, Semantic Scholar and Google Scholar. Extended versions of the best papers may be published into an upcoming special issues of “Sensors” (ISSN 1424-8220, Impact Factor: 3.275) and “Methods and Protocols” (ISSN 2409-9279; CODEN: MPERC6) Journals.

If you have interesting research results, you are invited to submit your paper by June 3oth. Interested parties can also attend the conference without a submission. Please note the information on the website: http://www.cerc-conf.eu.

Book Chapter published in Student Handbook “Praxishandbuch Forschungsdatenmanagement”

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We are glad to announce that our chapter to the student handbook “Praxishandbuch Forschungsdatenmanagement” (Engl.: practice handbook research data management) was accepted and got printed at De Gruyter. Our chapter addresses the foundations of Data Visualization, which is explained on behalf of selected examples.

The book covers nowadays societal research challenges in pespective of data management and how current approaches in research can helo to handle it. Therewith, events such as the entry into force of the code “Guidelines for Safeguarding Good Scientific Practice” of the German Research Foundation (DFG) or the establishment of the National Research Data Infrastructure (NFDI) and the European Open Science Cloud (EOSC) put providers, producers and users of research data in front of specialist, technical, legal and organizational challenges. The practical handbook for research data management comprehensively covers all relevant aspects of research data management and the current framework conditions in the data ecosystem.

In particular, the practical implications of data policy and law, the respective data market, data culture, personal qualification, data management and “FAIR” data transfer and reuse are examined. The practical handbook also provides an overview of projects, developments and challenges in Research data management.

Kickoff of the European Project “Partnerships with Eastern Europe”

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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

Our team wishes you happy christmas holidays and a happy new year!

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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.

Guest Editing of Special Issue in Big Data Research Journal

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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.

 

Call for Papers:

Visualization in Big Data and Data Science

 

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:

http://ees.elsevier.com/bdr

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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Website of the Special Issue

Three Papers Accepted at IEEE Information Technology and Management Science Conference (ITMS 2020)

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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.

Paper #1: Visual Analytics Indicators for Mobility and Transportation

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

Paper #2: Innovations in Mobility and Logistics: Assistance of Complex Analytical Processes in Visual Trend Analytics

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

Paper #3: On Microservice Architecture Based Communication Environment for Cycling Map Developing and Maintenance Simulator

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

Accepted Paper at International Conference on Industry 4.0 and Smart Manufacturing

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Over the past few years, the growing and intensive development of information technology in the manufacturing industry has led to a significant change in the methods and tools supporting the factories of the future. The hot topics around Industry 4.0 and smart manufacturing aim at the digital and organizational transformation of traditional factories and industrial systems as well as several other sectors. The International Conference on Industry 4.0 and Smart Manufacturing (ISM 2020) provides the perfect setting and a unique opportunity for knowledge exchange, the review and discussion of theoretical advances, research results, and industrial experiences, among scientists, researchers, decision makers, practitioners and students.

Paper: CONTEXT: An Industry 4.0 Dataset of Contextual Faults in a Smart Factory

Abstract:
Cyber-physical systems in smart factories get more and more integrated and interconnected. Industry 4.0 accelerates this trend even further. Through the broad interconnectivity a new class of faults arise, the contextual faults, where contextual knowledge is needed to find the underlying reason. Fully-automated systems and the production line in a smart factory form a complex environment making the fault diagnosis non-trivial. Along with the dataset, we give a first definition of contextual faults in the smart factory and name initial use cases. Additionally, the dataset encompasses all the data recorded in a current state-of-the-art smart factory. We also add additional information measured by our developed sensing units to enrich the smart factory data even further. In the end, we show a first approach to detect the contextual faults in a manual preliminary analysis of the recorded log data.

Link to Paper: https://doi.org/10.1016/j.procs.2021.01.265

 

The whole proceedings of the ISM 2020 are available under: Elsevier.

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