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Irtaza Rasheed defended his Master Thesis on Name Disambiguation on Digital Library Data for an Enhanced Profile Analysis in Visual Trend Analytics

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In his thesis, Irtaza Rasheed implementated a universal name disambiguation approach that considers almost any existing property to identify authors. After an author of a paper is identied, the normalized name writing form on the paper is used to refine the author model and even give an overview about the different writing forms of the author’s name. This can be achieved by first examine the research on Human-Computer Interaction specifically with focus on (Visual) Trend Analysis. Furthermore, a research on different name disambiguation techniques. After that, building a concept and implementing a generalized method to identify author name and affiliation disambiguation while evaluating different properties.

Two succeeful Submissions to ICTE in Transportation and Logistics book

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We could successfully submit two chapters to the current ICTE in Transportation and Logistics book. The goal of the book “ICTE in Transportation and Logistics” is an interdisciplinary annual issue published by Springer Nature Switzerland AG on the edge between transportation, logistics, economy and computer science highlighting sociotechnical aspects of any real sustainable system. The issue would be the announcing area of successful research projects giving possibilities for fast dissemination the information about new findings. The book will be covered by Scopus and Web of Science.

#1 Visual Analytics in Mobility, Transportation and Logistics

Mobility, transportation and logistics are more and more influenced by a variety of indicators such as new technological developments, ecological and economic changes, political decisions and in particular humans’ mobility behavior. These indicators will lead to massive changes in our daily live with regards to mobility, transportation and logistics. 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 provides both, the analytical approaches by including machine learning approaches and interactive visualizations to enable such analytical tasks. In this paper the main indicators for Visual Analytics in the domain of mobility transportation and logistics are discussed and followed by exemplary 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: doi: 10.1007/978-3-030-39688-6_12

#2 Process Support and Visual Adaptation to Assist Visual Trend Analytics in Managing Transportation Innovations

In the domain of mobility and logistics, a variety of new technologies and business ideas are arising. Beside technologies that aim on ecologically and economic transportation, such as electric engines, there are also fundamental different approaches like central packaging stations or deliveries via drones. Yet, there is a growing 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. Commonly adaptive systems investigate only the users’ behavior, while a process-related supports could assist to solve an analytical task more efficient and effective. In this article an approach that enables non-experts to perform visual trend analysis through an advanced process support based on process mining is described. This allow us to calculate a process model based on events, which is the baseline for process support feature calculation. These features and the process model enable to assist non-expert users in complex analytical tasks.

Link to paper: doi: 10.1007/978-3-030-39688-6_40

Research Day 2019 of the Hessian Universities of Applied Sciences (HAW)

/in , , , , /by

October 29th was an important day for researchers in the applied research domain in Hesse, since they stayed in focus at the the Research Day 2019 of the Hessian Universities of Applied Sciences (HAW). On this day the spotlight of the he Hessen State Ministry for Higher Education, Research and the Arts layed on current results and achievements of researchers. Next to several awards for research results, it was also a great opportunity to show results and use the chance for networking.

Our team was invited to attend the event and presented ongoing results toward Advanced Visual Analytical Reasoning for Technology and Innovation Management, particularly the progress of the Hessen funded research project AVARTIM. As brief overview to the Visual Trend Analytics and Visual Business Analytics topic, we provided a poster with the summary of our current insights, which led to a variety of interesting discussions and new contacts for promising further collaboration actions – among other also for our planned European research network.

We really enjoyed the event and the opportunity to get connected with other Hessian researchers and look forward for some further collaborative actions – and, of course, the next Research Day of the Hessian Universities of Applied Sciences (HAW).

Insight on Visual Text Analytics for Technology and Innovation Management at OpenRheinMain Conference

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We get the opportunity to give some insights to “Visual Text Analytics for Technology and Innovation Management”, based on our core Trend Analytics technology Scitics, on the OpenRheinMain Conference. We want to give insights on how Visual Analytics techniques can be used to enable effective technology and innovation management on behalf of external/web data as well as internal/company data.

The OpenRheinMain (ORM 2019) is the 1st edition of an annual IT conference on open source and emerging digital technologies. This includes, but is not limited to, Open-Source, Intelligent Automation and DevOps, Cloud Computing, and Internet of Things. The purpose of the conference is to interlink researchers and industrial partner of the Rhein Main region. Therefore, the conference considers stakeholders from both in an appropriate proportion. The conference will take place on September 13th, 2019 at Darmstadt University of Applied Science.

Due to heterogeneity of the event participants, the chances are high to strengthen the cooperation with local enterprises. We expect, that this will be relevant for further research actions to strengthen  the local region.

The extended abstract of the presentation is available under: https://dx.doi.org/10.5281/zenodo.3408391
More information on the event website: https://www.openrheinmain.org

Events

Online @ 25th International Conference Information Visualization (iV 2021)

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We are co-organizing the International Symposium Visual Analytics and Data Science at the next International Information Visualisation Conference (iV 2021), which is held online. 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 proceedings will be published as usual in IEEE Xplore.

Visual Analytics is viewed as the science of analytical reasoning empowered by interactive visualizations. It combines interactive visualizations with models and approaches of machine learning and artificial intelligence, enabling solving complex analytical tasks by uncovering hidden patterns in data.
The research on Visual Analytics is closely related to that of Data Science. Both areas seek to enhance the knowledge discovery process using machine learning, data mining, and artificial intelligence methods, whereas Visual Analytics allows commonly a direct manipulation of the underlying models through graphical representations. By leveraging human perception of the visual space, patterns that might not otherwise be discovered. Visual Analytics utilizes concepts from a wide variety of disciplines, including Computer Graphics, Information Visualization, Machine Learning, Artificial Intelligence, Knowledge Discovery, Cognition, and Visual Perception.
Papers on all aspects of Visual Analytics and Data Science are solicited. Papers will be refereed and appear in the main conference proceedings published by Conference Publishing Services CPS – Conference Publishing Services, – Library of Congress/ISSN, ISBN, and other bibliographical registration details; Arrange for indexing through INSPEC, EI (Compendex), Thomson ISI, and other indexing services.

A selection of the best papers will be recommended for publication in special issues of scientific journals, or as an edited book.

Topics of interest:

  • Combining visual and computational methods of data analysis
  • Visual querying
  • Visual analytics of spatial, temporal, and spatiotemporal data
  • Knowledge construction and management in visual analytics
  • Privacy issues in visual analytics
  • Cognitive approaches and explanations for visual data mining
  • Visualization of the data mining algorithm
  • Scalability issues
  • Empirical studies of performance
  • Evaluation of visual data mining methods
  • Collaborative visualization and mining
  • Applications of visual data mining and analytics
  • Case studies
  • Computational steering for long-running data mining applications
  • Reviews and surveys of related literature


Related news for further information:

    Online @ 25th International Conference Information Visualization (iV 2021)

    /in /by

    We are co-organizing the International Symposium Visual Analytics and Data Science at the next International Information Visualisation Conference (iV 2021), which is held online. 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 proceedings will be published as usual in IEEE Xplore.

    Visual Analytics is viewed as the science of analytical reasoning empowered by interactive visualizations. It combines interactive visualizations with models and approaches of machine learning and artificial intelligence, enabling solving complex analytical tasks by uncovering hidden patterns in data.
    The research on Visual Analytics is closely related to that of Data Science. Both areas seek to enhance the knowledge discovery process using machine learning, data mining, and artificial intelligence methods, whereas Visual Analytics allows commonly a direct manipulation of the underlying models through graphical representations. By leveraging human perception of the visual space, patterns that might not otherwise be discovered. Visual Analytics utilizes concepts from a wide variety of disciplines, including Computer Graphics, Information Visualization, Machine Learning, Artificial Intelligence, Knowledge Discovery, Cognition, and Visual Perception.
    Papers on all aspects of Visual Analytics and Data Science are solicited. Papers will be refereed and appear in the main conference proceedings published by Conference Publishing Services CPS – Conference Publishing Services, – Library of Congress/ISSN, ISBN, and other bibliographical registration details; Arrange for indexing through INSPEC, EI (Compendex), Thomson ISI, and other indexing services.

    A selection of the best papers will be recommended for publication in special issues of scientific journals, or as an edited book.

    Topics of interest:

    • Combining visual and computational methods of data analysis
    • Visual querying
    • Visual analytics of spatial, temporal, and spatiotemporal data
    • Knowledge construction and management in visual analytics
    • Privacy issues in visual analytics
    • Cognitive approaches and explanations for visual data mining
    • Visualization of the data mining algorithm
    • Scalability issues
    • Empirical studies of performance
    • Evaluation of visual data mining methods
    • Collaborative visualization and mining
    • Applications of visual data mining and analytics
    • Case studies
    • Computational steering for long-running data mining applications
    • Reviews and surveys of related literature


    Related news for further information:

      Irtaza Rasheed defended his Master Thesis on Name Disambiguation on Digital Library Data for an Enhanced Profile Analysis in Visual Trend Analytics

      /in , , , /by

      In his thesis, Irtaza Rasheed implementated a universal name disambiguation approach that considers almost any existing property to identify authors. After an author of a paper is identied, the normalized name writing form on the paper is used to refine the author model and even give an overview about the different writing forms of the author’s name. This can be achieved by first examine the research on Human-Computer Interaction specifically with focus on (Visual) Trend Analysis. Furthermore, a research on different name disambiguation techniques. After that, building a concept and implementing a generalized method to identify author name and affiliation disambiguation while evaluating different properties.

      Online @ 24rd International Conference Information Visualization (iV 2020)

      /in /by

      We are co-organizing the International Symposium Visual Analytics and Data Science at the next International Information Visualisation Conference (iV 2020) in Vienna, Austria. 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.

      Please note that the IV2020 will be held online and the submission deadline was extended. The processings will be published as usual in IEEE Xplore.

      The scope of the conference covers the following topics:

      • Combining visual and computational methods of Data Analysis, Machine Learning and Artificial Intelligence
      • Visual Analytics models and approaches
      • Novel Visual Analytics applications
      • Visual Trend Analytics
      • Visual Analytics of spatial, temporal, and spatio-temporal data
      • Knowledge construction and management in Visual Analytics
      • Guidance in Visual Analytics
      • Intelligent approaches of Visual Analytics and Data Science
      • Adaptive Visual Analytics
      • Cognitive approaches and explanations for Visual Analytics
      • Visual Analytics for explaining AI
      • Visualization of Data Mining algorithms
      • Empirical performance studies
      • Evaluation of Visual Data Mining methods
      • Collaborative Visual

       

      Our Group co-organizes the main symposium 12th International Symposium Visual Analytics and Data Science.
      Visual Analytics is viewed as the science of analytical reasoning empowered by interactive visualizations. It combines interactive visualizations with models and approaches of machine learning and artificial intelligence, enabling solving complex analytical tasks by uncovering hidden patterns in data.
      The research on Visual Analytics is closely related to that of Data Science. Both areas seek to enhance the knowledge discovery process using machine learning, data mining and artificial intelligence methods, whereas Visual Analytics allows commonly a direct manipulation of the underlying models through graphical representations. By leveraging human perception of the visual space, patterns that might not otherwise be discovered. Visual Analytics utilizes concepts from a wide variety of disciplines, including Computer Graphics, Information Visualization, Machine Learning, Artificial Intelligence, Knowledge Discovery, Cognition and Visual Perception.

      The scope of the VA track covers the following topics:

      • Combining visual and computational methods of Data Analysis, Machine Learning and Artificial Intelligence
      • Visual Analytics models and approaches
      • Novel Visual Analytics applications
      • Visual Trend Analytics
      • Visual Analytics of spatial, temporal, and spatio-temporal data
      • Knowledge construction and management in Visual Analytics
      • Guidance in Visual Analytics
      • Intelligent approaches of Visual Analytics and Data Science
      • Adaptive Visual Analytics
      • Cognitive approaches and explanations for Visual Analytics
      • Visual Analytics for explaining AI
      • Visualization of Data Mining algorithms
      • Empirical performance studies
      • Evaluation of Visual Data Mining methods
      • Collaborative Visual Analytics and Data Science
      • Computational steering for long-running Data Mining applications
      • Reviews and surveys of related literature


      Related news for further information:

      24rd International Conference Information Visualization (iV 2020)

      /in /by

         The event has been modified and is now organised virtual. Please note the new annouced event under Online @ iV2020!

      We are co-organizing the International Symposium Visual Analytics and Data Science at the next International Information Visualisation Conference (iV 2020) in Vienna, Austria. 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 scope of the conference covers the following topics:

      • Combining visual and computational methods of Data Analysis, Machine Learning and Artificial Intelligence
      • Visual Analytics models and approaches
      • Novel Visual Analytics applications
      • Visual Trend Analytics
      • Visual Analytics of spatial, temporal, and spatio-temporal data
      • Knowledge construction and management in Visual Analytics
      • Guidance in Visual Analytics
      • Intelligent approaches of Visual Analytics and Data Science
      • Adaptive Visual Analytics
      • Cognitive approaches and explanations for Visual Analytics
      • Visual Analytics for explaining AI
      • Visualization of Data Mining algorithms
      • Empirical performance studies
      • Evaluation of Visual Data Mining methods
      • Collaborative Visual

      Please note the iV2020 Call for Papers (CfP).

      The Visual Analytics and Data Science track is organized via the 12th International Symposium Visual Analytics and Data Science.
      Visual Analytics is viewed as the science of analytical reasoning empowered by interactive visualizations. It combines interactive visualizations with models and approaches of machine learning and artificial intelligence, enabling solving complex analytical tasks by uncovering hidden patterns in data.
      The research on Visual Analytics is closely related to that of Data Science. Both areas seek to enhance the knowledge discovery process using machine learning, data mining and artificial intelligence methods, whereas Visual Analytics allows commonly a direct manipulation of the underlying models through graphical representations. By leveraging human perception of the visual space, patterns that might not otherwise be discovered. Visual Analytics utilizes concepts from a wide variety of disciplines, including Computer Graphics, Information Visualization, Machine Learning, Artificial Intelligence, Knowledge Discovery, Cognition and Visual Perception.

      The scope of the VA track covers the following topics:

      • Combining visual and computational methods of Data Analysis, Machine Learning and Artificial Intelligence
      • Visual Analytics models and approaches
      • Novel Visual Analytics applications
      • Visual Trend Analytics
      • Visual Analytics of spatial, temporal, and spatio-temporal data
      • Knowledge construction and management in Visual Analytics
      • Guidance in Visual Analytics
      • Intelligent approaches of Visual Analytics and Data Science
      • Adaptive Visual Analytics
      • Cognitive approaches and explanations for Visual Analytics
      • Visual Analytics for explaining AI
      • Visualization of Data Mining algorithms
      • Empirical performance studies
      • Evaluation of Visual Data Mining methods
      • Collaborative Visual Analytics and Data Science
      • Computational steering for long-running Data Mining applications
      • Reviews and surveys of related literature


      Related news for further information:

      Thesis Presentation: Named-Entity Recognition on Publications and Raw-Text for Meticulous Insight at Visual Trend Analytics

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      Where: TU Darmstadt / GRIS, Fraunhoferstr. 5 (Darmstadt), Room tba

      !!!!! Due to the Corona crisis and the accompanying restrictions at the TU Darmstadt, the exam will be non-public! !!!!!

      Who: Ubaid Rana (Author), Prof. Dr. Arjan Kuijper (Supervisor), Dipl.-Inf. Dirk Burkhardt (Advisor/Co-Supervisor)
      What: Master Thesis – “Named-Entity Recognition on Publications and Raw-Text for Meticulous Insight at Visual Trend Analytics”

      Abstract:

      In the modern data-driven era, a massive amount of research documents are available from publicly accessible digital libraries in the form of academic papers, journals and publications. This plethora of data does not lead to new insights or knowledge. Therefore, suitable analysis techniques and graphical tools are needed to derive knowledge in order to get insight of this big data. To address this issue, researchers have developed visual analytical systems along with machine learning methods, e.g text mining with interactive data visualization, which leads to gain new insights of current and upcoming technology trends. These trends are significant for researchers, business analysts, and decision-makers for innovation, technology management and to make strategic decisions.
      Nearly every existing search portal uses the traditional meta-information e.g only about the author and title to find the documents that match a search request and overlook the opportunity of extracting content-related information. It limits the possibility of discovering most relevant publications, moreover it lacks the knowledge required for trend analysis. To collect this very concrete information, named entity recognition must be used to be able to better identify the results and trends. The state-of-the-art systems use static approach for named entity recognition which means that upcoming technologies remain undetected. Modern techniques like distant supervision methods leverage big existing community-maintained data sources, such as Wikipedia, to extract entities dynamically. Nonetheless, these methods are still unstable and have never been tried on complex scenarios such as trend analysis before.
      The aim of this thesis is to enable entity recognition on both static tables and dynamic community updated data sources like Wikipedia & DBpedia for trend analysis. To accomplish this goal, a model is suggested which enabled entity extraction on DBpedia and translated the extracted entities into interactive visualizations. The analysts can use these visualizations to gain trend insights, evaluate research trends or to analyze prevailing market moods and industry trends.

      Two succeeful Submissions to ICTE in Transportation and Logistics book

      /in , , , /by

      We could successfully submit two chapters to the current ICTE in Transportation and Logistics book. The goal of the book “ICTE in Transportation and Logistics” is an interdisciplinary annual issue published by Springer Nature Switzerland AG on the edge between transportation, logistics, economy and computer science highlighting sociotechnical aspects of any real sustainable system. The issue would be the announcing area of successful research projects giving possibilities for fast dissemination the information about new findings. The book will be covered by Scopus and Web of Science.

      #1 Visual Analytics in Mobility, Transportation and Logistics

      Mobility, transportation and logistics are more and more influenced by a variety of indicators such as new technological developments, ecological and economic changes, political decisions and in particular humans’ mobility behavior. These indicators will lead to massive changes in our daily live with regards to mobility, transportation and logistics. 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 provides both, the analytical approaches by including machine learning approaches and interactive visualizations to enable such analytical tasks. In this paper the main indicators for Visual Analytics in the domain of mobility transportation and logistics are discussed and followed by exemplary 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: doi: 10.1007/978-3-030-39688-6_12

      #2 Process Support and Visual Adaptation to Assist Visual Trend Analytics in Managing Transportation Innovations

      In the domain of mobility and logistics, a variety of new technologies and business ideas are arising. Beside technologies that aim on ecologically and economic transportation, such as electric engines, there are also fundamental different approaches like central packaging stations or deliveries via drones. Yet, there is a growing 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. Commonly adaptive systems investigate only the users’ behavior, while a process-related supports could assist to solve an analytical task more efficient and effective. In this article an approach that enables non-experts to perform visual trend analysis through an advanced process support based on process mining is described. This allow us to calculate a process model based on events, which is the baseline for process support feature calculation. These features and the process model enable to assist non-expert users in complex analytical tasks.

      Link to paper: doi: 10.1007/978-3-030-39688-6_40

      Research Day 2019 of the Hessian Universities of Applied Sciences (HAW)

      /in , , , , /by

      October 29th was an important day for researchers in the applied research domain in Hesse, since they stayed in focus at the the Research Day 2019 of the Hessian Universities of Applied Sciences (HAW). On this day the spotlight of the he Hessen State Ministry for Higher Education, Research and the Arts layed on current results and achievements of researchers. Next to several awards for research results, it was also a great opportunity to show results and use the chance for networking.

      Our team was invited to attend the event and presented ongoing results toward Advanced Visual Analytical Reasoning for Technology and Innovation Management, particularly the progress of the Hessen funded research project AVARTIM. As brief overview to the Visual Trend Analytics and Visual Business Analytics topic, we provided a poster with the summary of our current insights, which led to a variety of interesting discussions and new contacts for promising further collaboration actions – among other also for our planned European research network.

      We really enjoyed the event and the opportunity to get connected with other Hessian researchers and look forward for some further collaborative actions – and, of course, the next Research Day of the Hessian Universities of Applied Sciences (HAW).

      Research Day 2019 of the Hessian Universities of Applied Sciences (HAW)

      /in , , , /by
      On 29 October, the focus will be on research at the HAWs in Hesse: within the framework of the Research Day 2019, the research awards will be presented to HAW Hessen. To the agenda: After the welcome by President Prof. dr. Frank E.P. Dievernich speaks to Secretary of State Ayse Asar (HMWK). Prof. Dr. Dr. h.c. Volker Mosbrugger (Senckenberg Society for Natural Research, Polytechnic Society Frankfurt am Main) gives the keynote address on the topic “Science in transition – the new challenges”. Subsequently, the festive award ceremony takes place. After the break, scientists from the Frankfurt UAS present their research projects in the entertaining Pecha Kucha format. The event concludes with a panel discussion under the motto “Vision, Success, Appreciation – Applied Research Makes People Happy”. In addition, in the form of a poster exhibition “Research for the practice – 10 years of research campaign by HAW Hessen and HMWK” projects from ten years of research funding for the Hessian HAW are presented. We were invited to join to event as an active research contributor. We are going to present our latest results the Hessian funded research project AVARTIM toward Advanced Visual Analytical Reasoning for Technology and Innovation Management (please also note our summary poster) .

      14th International Symposium on Visual Computing (ISVC 2019)

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      We are attending the 14th International Symposium on Visual Computing (ISVC 2019) and presenting our accepted paper titled “A Visual Analytics Approach for Analyzing Technological Trends in Technology and Innovation Management”. More information about our paper and the conference you will find in the news. The program is available on the conference website

      Virtual and Augmented Reality in Education (VARE 2019) Conference

      /in , /by
      We are attending this year’s Virtual and Augmented Reality in Education conference (VARE 2019) and presenting our accepted paper titled “A Mobile Visual Analytics Approach for Instant Trend Analysis in Mobile Contexts”. More information about our paper and the conference you will find in the news. Also note the conference program.

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