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Call for Articles to Special Issue in Journal of Electronics

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Prof. Dr.-Ing. Kawa Nazemi is organizing together with Prof. Dr. Egils Ginters and Dr. Michael Bažant a special issue on “Visual Analytics, Simulation, and Decision-Making Technologies” in the MDPI Journal of Electronics.

The Journal of Electronics is an international, peer-reviewed, open-access journal on the science of electronics and its applications published semimonthly online by MDPI. It publishes reviews, research articles, short communications, and letters. The aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. Full experimental and/or methodical details must be provided.

With the recent rise in cutting-edge technologies such as artificial intelligence, new and unsolved societal economic challenges can be solved sophisticatedly. Disciplines such as visual analytics, simulation, data analytics, natural language processing, and image and video processing combined with the human in the interaction loop have led to new systems, methods, concepts, and architectural designs that help us to face societal challenges, e.g., climate, mobility, sustainability, smart city. Furthermore, these approaches lead to predicting likely future scenarios in the economy. These enhancements enable gathering the market relevance of upcoming technologies, analyzing the competitors and other forthcoming competitive technologies, and analyzing new markets for technologies. Thus, the aspect of sustainability plays an increasing role, even in market positioning or in the development and deployment of new technologies.

In this Special Issue, we are interested in systems, system architectures, computational techniques, methods and models, and literature reviews in the areas of analytical decision making, collaborative work, sustainability, economy, simulation, object monitoring, and behavior detection.

From a methodological point of view, the focus is on combining technological approaches from various disciplines to provide new ways and methods for analyzing data and models and enabling novel approaches for solving societal and economic challenges. On the practical side, we are looking for algorithms, software, prototypes, and demonstrators of decision-making support with the human-in-the-loop in various application fields. Topics of interest include but are not limited to the following:

  • Visual analytics and information visualization
  • Artificial intelligence and machine learning
  • Simulation and modeling for digital twins
  • Collaboration systems and collaborative work
  • Data analytics and natural language processing
  • Analytical systems for mobility, transportation, and traffic
  • Analytical systems for sustainability and environment
  • Analytical systems for corporate foresight
  • Analytical systems for smart manufacturing
  • Technologies for smart city, virtual, and augmented reality applications
  • Quantum and high-performance computing use
  • Digital wallet and blockchain synergy
  • Fault diagnosis in cyber-physical systems
  • Predictive maintenance in Industry 4.0
  • Object monitoring and behavior detection

If you are interested in the journal and in submitting an article, please note the information on the special issue website: Special Issue “Visual Analytics, Simulation, and Decision-Making Technologies”.

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

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.

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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Talk on Artificial Intelligence and Data Analytics at WissDok

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WissDok is an annual event for scientific and media documentation of public media organizations. The event focuses on the pragmatic-current edge of technical, organizational and scientific development in the documentation in its role as an incarnation of information science. The invited speakers deliver an annual update on the current development of various dimensions in this professional field in approximately 2-hour sessions. The event is part of the wissDok program and an offer to the specialist with opportunity for discussion.

At wissDok our team, represented by Lukas Kaupp, will give a talk about Artificial Intelligence and Data Analytics. The talk will cover three main aspects: (1) Overview on Artificial Intelligence, (2) Machine Learning and (3) Data Analytics. All parts are covered by an introduction to the foundations, current state-of-the-art approaches and upcoming research trends.

Tag Archive for: Data Analytics

WissDok 2019

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WissDok is an annual event for scientific and media documentation of public media organizations. The event focuses on the pragmatic-current edge of technical, organizational and scientific development in the documentation in its role as an incarnation of information science. The invited speakers deliver an annual update on the current development of various dimensions in this professional field in approximately 2-hour sessions. The event is part of the wissDok program and an offer to the specialist with opportunity for discussion.

The symposium at the Mediencampus is part of the wissDok program and an offer to the specialist public with opportunity for discussion. At the symposium our team will give a talk about Artificial Intelligence and Data Analytics.

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