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Book appeared – Integrating Artificial Intelligence and Visualization

/in , , /by
Professor Kawa Nazemi edited together with colleagues from the London South Bank University, Instituto Superior de Engenharia de Lisboa, and the Central Washington University enhanced contributions of selected papers of the International Conference on Information Visualisation particularly on the intersection of artificial intelligence and visualization. The book will appear in the series Studies in Computational Intelligence by Springer Nature. The book “Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery” is devoted to the emerging field of integrated visual knowledge discovery that combines advances in artificial intelligence/machine learning and visualization/visual analytics. A long-standing challenge of artificial intelligence (AI) and machine learning (ML) is explaining models to humans, especially for live-critical applications like health care. A model explanation is a fundamentally human activity, not only an algorithmic one. As current deep learning studies demonstrate, it makes the paradigm based on the visual methods critically important to address this challenge. In general, visual approaches are critical for discovering explainable high-dimensional patterns in all types in high-dimensional data offering “n-D glasses,” where preserving high-dimensional data properties and relations in visualizations is a major challenge. The current progress opens a fantastic opportunity in this domain.
Read more

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

/in , , , , , , /by

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

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.

Book Chapter published in Student Handbook “Praxishandbuch Forschungsdatenmanagement”

/in , , , , /by

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.

Summer School: From Smart to Intelligent Cities – Concepts, Methods, Digital Transformation

/in , , /by

At the EFST Summer School 2018 (from 18th to 22nd of June) on the University of Split Prof. Nazemi gives lectures on “Intelligent Visualizations in Future Policy Modelling” and “Data Analysis for eGovernance and eParticipation”. The lectures are a summary of current researches in this field as well as gained results in national and European research project on e-Government actions for Cities.

The concepts of Smart and Intelligent Cities constitute a breakthrough in contemporary urban development and management. The Smart City notion has gained much popularity in the past years, but recently also the concept of Intelligent City – base on cognitive, learning and knowledge principles – has become en vogue. They prompted not also many debates but also many urban planning initiatives informed and influenced by rapid development of smart technologies and growing interest in digital transformation of public services. By presenting the most prominent challenges of these concepts and enabling technologies, participants will get an opportunity to understand and contribute to the challenges faced by modern cities and to explore more efficient solutions resulting from swift technology development and digital transformation initiatives in this specific context.

The Summer School offers a unique opportunity to learn and discuss about structures, actors, processes and challenges of regional and urban development and management in dialogue with international academic experts and practitioners. This will be achieved through lectures from leading academics and experts in the field.

The program can be downloaded here.

Talk about Intelligent Visualization on GI Rhein-Main (Society for Informatics)

/in , , , , , , , , /by

Prof. Dr. Kawa Nazemi gave a talk on Intelligent Visualization and Visual Analytics on the regional meeting of the Gesellschaft für Informatik (Society for Informatics). In his talk he outlined the main purposes and future directions of Information Visualization, Visual Analytics and Intelligent Visualizations. The talk was given in German. Further information about the talk can be found here.

Tag Archive for: Data Analysis

26th International Conference Information Visualization (iV 2022)

/in /by

We are co-organizing the International Symposium Visual Analytics and Data Science at the next International Information Visualisation Conference (iV 2022), 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 the science of analytical reasoning empowered by interactive visualizations. 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. In contrast, Visual Analytics allows direct manipulation of the underlying models through graphical representations commonly. By leveraging human perception of the visual space, patterns that might not otherwise be discovered emerge. Visual Analytics utilizes concepts from various 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.

 

The topics of interest include but are not limited to:

  • 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, geo-visualization and geographical visualization of spatial, temporal, and Spatio-temporal data
  • Visualization support for multi-criteria decision analysis related to multivariate and spatial data
  • Knowledge construction and management in Visual Analytics
  • Guidance in Visual Analytics
  • Intelligent approaches of Visual Analytics and Data Science
  • Adaptive Visual Analytics
  • HCI issues of geographical and Spatio-temporal 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:

26th International Conference Information Visualization (iV 2022)

/in /by

We are co-organizing the International Symposium Visual Analytics and Data Science at the next International Information Visualisation Conference (iV 2022), 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 the science of analytical reasoning empowered by interactive visualizations. 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. In contrast, Visual Analytics allows direct manipulation of the underlying models through graphical representations commonly. By leveraging human perception of the visual space, patterns that might not otherwise be discovered emerge. Visual Analytics utilizes concepts from various 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.

 

The topics of interest include but are not limited to:

  • 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, geo-visualization and geographical visualization of spatial, temporal, and Spatio-temporal data
  • Visualization support for multi-criteria decision analysis related to multivariate and spatial data
  • Knowledge construction and management in Visual Analytics
  • Guidance in Visual Analytics
  • Intelligent approaches of Visual Analytics and Data Science
  • Adaptive Visual Analytics
  • HCI issues of geographical and Spatio-temporal 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:

Book appeared – Integrating Artificial Intelligence and Visualization

/in , , /by
Professor Kawa Nazemi edited together with colleagues from the London South Bank University, Instituto Superior de Engenharia de Lisboa, and the Central Washington University enhanced contributions of selected papers of the International Conference on Information Visualisation particularly on the intersection of artificial intelligence and visualization. The book will appear in the series Studies in Computational Intelligence by Springer Nature. The book “Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery” is devoted to the emerging field of integrated visual knowledge discovery that combines advances in artificial intelligence/machine learning and visualization/visual analytics. A long-standing challenge of artificial intelligence (AI) and machine learning (ML) is explaining models to humans, especially for live-critical applications like health care. A model explanation is a fundamentally human activity, not only an algorithmic one. As current deep learning studies demonstrate, it makes the paradigm based on the visual methods critically important to address this challenge. In general, visual approaches are critical for discovering explainable high-dimensional patterns in all types in high-dimensional data offering “n-D glasses,” where preserving high-dimensional data properties and relations in visualizations is a major challenge. The current progress opens a fantastic opportunity in this domain.
Read more

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

/in , , , , , , /by

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

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.

Book Chapter published in Student Handbook “Praxishandbuch Forschungsdatenmanagement”

/in , , , , /by

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.

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.

Thesis Presentation: Visual Trend Analysis on Condensed Expert Data beside Research Library Data for Enhanced Insights

/in , /by

Where: TU Darmstadt / GRIS, Fraunhoferstr. 5, Room 073
Who: Muhammad Ali Riaz (Author), Prof. Dr. Arjan Kuijper (Supervisor), Dipl.-Inf. Dirk Burkhardt (Advisor/Co-Supervisor)
What: Master Thesis – “Visual Trend Analysis on Condensed Expert Data beside Research Library Data for Enhanced Insights”

Abstract:

In the present age of information, we live amidst seas of digital text documents including academic publications, white papers, news articles, patents, newspapers. To tackle the issue of the ever-increasing amount of text documents, researchers from the field of text mining and information visualization have developed tools and techniques to facilitate text analysis. In the context of visual trend analysis on text data, the use of well-structured patent data and public digital libraries are quite established. However, both sources of information have their limitations. For instance, the registration process for patents takes at least one year, which makes the extracted insights not suitable to research on present scenarios. In contrast to patent data, the digital libraries are up-to-date but provide high-level insights, only limited to broader research domains, and the data usage is almost restricted on meta information, such as title, author names and abstract, and they do not provide full text.
For a certain type of detailed analysis such as competitor analysis or portfolio analysis, data from digital libraries is not enough, it would also make sense to analyze the full-text. Even more, it can be beneficial to analyze only a limited dataset that is filtered by an expert towards a very specific field, such as additive printing or smart wearables for medical observations. Sometimes also a mixture of both digital library data and manually collected documents is relevant to be able to validate a certain trend, where one gives a big picture and other gives a very condensed overview of the present scenario.
The thesis aims, therefore, to focus on such manually collected documents by experts that can be defined as condensed data. So, the major goal of this thesis is to conceptualize and implement a solution that enables the creation and analysis of such a condensed data set and compensate therewith the limitations of digital library data analysis. As a result, a visual trend analysis system for analyzing text documents is presented, it utilizes the best of both state-of-the-art text analytics and information visualization techniques. In a nutshell, the presented trend analysis system does two things. Firstly, it is capable of extracting raw data from text documents in the form of unstructured text and meta-data, convert it into structured and analyzable formats, extract trends from it and present it with appropriate visualizations. Secondly, the system is also capable of performing gap-analysis tasks between two data sources, which in this case is digital library data and data from manually collected text documents (Condensed Expert Data). The proposed visual trend analysis system can be used by researchers for analyzing the research trends, organizations to identify current market buzz and industry trends, and many other use-cases where text data is the primary source of valuable information.

Summer School: From Smart to Intelligent Cities – Concepts, Methods, Digital Transformation

/in , , /by

At the EFST Summer School 2018 (from 18th to 22nd of June) on the University of Split Prof. Nazemi gives lectures on “Intelligent Visualizations in Future Policy Modelling” and “Data Analysis for eGovernance and eParticipation”. The lectures are a summary of current researches in this field as well as gained results in national and European research project on e-Government actions for Cities.

The concepts of Smart and Intelligent Cities constitute a breakthrough in contemporary urban development and management. The Smart City notion has gained much popularity in the past years, but recently also the concept of Intelligent City – base on cognitive, learning and knowledge principles – has become en vogue. They prompted not also many debates but also many urban planning initiatives informed and influenced by rapid development of smart technologies and growing interest in digital transformation of public services. By presenting the most prominent challenges of these concepts and enabling technologies, participants will get an opportunity to understand and contribute to the challenges faced by modern cities and to explore more efficient solutions resulting from swift technology development and digital transformation initiatives in this specific context.

The Summer School offers a unique opportunity to learn and discuss about structures, actors, processes and challenges of regional and urban development and management in dialogue with international academic experts and practitioners. This will be achieved through lectures from leading academics and experts in the field.

The program can be downloaded here.

Talk about Intelligent Visualization on GI Rhein-Main (Society for Informatics)

/in , , , , , , , , /by

Prof. Dr. Kawa Nazemi gave a talk on Intelligent Visualization and Visual Analytics on the regional meeting of the Gesellschaft für Informatik (Society for Informatics). In his talk he outlined the main purposes and future directions of Information Visualization, Visual Analytics and Intelligent Visualizations. The talk was given in German. Further information about the talk can be found here.

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