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Participation at IV 2022

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Our group presented at this year’s Information Visualisation Conference (iV 2022) two papers entitled “Visual Analytics for Systematic Reviews According to PRISMA” and “Visual Collaboration – An Approach for Visual Analytical Collaborative Research”. In addition, Professor Kawa Nazemi, the head of the research group, was a co-chair of the conference and chaired the program committee and various sessions and tracks. Due to the ease of COVID travel restrictions, we were able to participate in person Vienna. The iV 2022 is an international conference that aims to provide a foundation for integrating information visualization’s human-centered, technological and strategic aspects to promote international exchange, cooperation, and development.

Paper #1: Visual Analytics for Systematic Reviews According to PRISMA

Lead author: Lennart B. Sina

Abstract:
Systematic reviews play an essential role in various disciplines. Particularly, in biomedical sciences, systematic reviews according to a predefined schema and protocol are how related literature is analyzed. Although a protocol-based systematic review is replicable and provides the required information to reproduce each step and refine them, such a systematic review is time-consuming and may get complex. To face this challenge, automatic methods can be applied that support researchers in their systematic analysis process. The combination of artificial intelligence for automatic information extraction from scientific literature with interactive visualizations as a Visual Analytics system can lead to sophisticated analysis and protocoling of the review process. We introduce in this paper a novel Visual Analytics approach and system that enables researchers to visually search and explore scientific publications and generate a protocol based on the PRISMA protocol and the PRISMA statement.

Link to paper/fulltext: Soon available

Paper #2: Visual Collaboration – An Approach for Visual Analytical Collaborative Research

Lead author: Midhad Blazevic

Abstract:

Studies have shown that collaboration in scientific fields is rising and considered enormously important. However, collaboration has proved to be challenging for various reasons, among others, the requirements for human-machine workflows. The importance of scientific collaboration lies in the complexity of the challenges that are faced today. The more complex the challenge, the more scientists should work together. The current form of collaboration in the scientific community is not as intelligent as it should be. Scientists have to multitask with various applications, often losing cognitive focus. Collaboration itself is very nearsighted as it is usually conducted not solely based on expertise but instead on social or local networks. We introduce a single-source visual collaboration approach based on learning methods in this work. We use machine learning and natural language processing approaches to improve the traditional research and development process and create a system that facilitates and encourages collaboration based on expertise, enhancing the research collaboration process in many ways. Our approach combines collaborative Visual Analytics with enhanced collaboration techniques to support researchers from different disciplines

Link to paper/fulltext: Soon available

Book published– Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery

/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.

This book is a collection of 25 extended works of over 70 scholars presented at AI and visual analytics-related symposia at the recent International Information Visualization Conferences with the goal of moving this integration to the next level.  The sections of this book cover integrated systems, supervised learning, unsupervised learning, optimization, and evaluation of visualizations.

The intended audience for this collection includes those developing and using emerging AI/machine learning and visualization methods. Scientists, practitioners, and students can find multiple examples of the current integration of AI/machine learning and visualization for visual knowledge discovery. The book provides a vision of future directions in this domain. New researchers will find here an inspiration to join the profession and to be involved for further development. Instructors in AI/ML and visualization classes can use it as a supplementary source in their undergraduate and graduate classes.

Shahrukh Badar defended his Master Thesis on Process Mining for Workflow-Driven Assistance in Visual Trend Analytics

/in , , , /by

In his thesis, Shahrukh Badar created process-driven assistance that is applied to the visual trend analytics domain. The goal was, based on previous users interactions and solved tasks, to assist further users in their work. Therefore, a universal visual assistance model was defined and acts also as the main contribution, based on defined interaction event taxonomy. This concept was applied to the Visual Trend Analytics domain on the SciTics reference system. This “SciTics – Science Analytics” is connected with different data sources and provides analysis of scientific documents. The interaction model provides assistance in terms of recommendations, where the user has an option either to apply a recommendation or ignore it. The solution provided in this thesis is model-based and utilizes the potential of Process Mining and Discovery techniques. It is started by creating an event taxonomy by identifying all possible ways of user interactions on the “SciTic – Visual Trend Analytics” web application. Next, enable the “SciTic – Visual Trend Analytics” web application to start logging events chronologically based on predefined taxonomy. Later, these events log is converted into Process Mining log format. Next, it applies the Process Discovery algorithm “Heuristics Miner” on these log data to generate a process model, which shows the overall flow of user interaction along with the frequencies. Later, this process model is used to provide users with recommendations.

More Information:

26 April 2022

Thesis Presentation: Process Mining for Workflow-Driven Assistance in Visual Trend Analytics

Where: TU Darmstadt / GRIS, Zoom: https://tu-darmstadt.zoom.us/j/86845586436?pwd=RUJiWm1QdWJ4VDg3MU93WUNOWWFTQT09 Who: Shahrukh Badar (Author), Prof. Dr. Arjan Kuijper (Supervisor), Dipl.-Inf. Dirk Burkhardt (Advisor/Co-Supervisor) What: Master Thesis – “Process Mining for Workflow-Driven Assistance in […]

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TU Darmstadt / GRIS, Fraunhoferstraße 5
Darmstadt, Hessian 64283 Germany
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Sibgha Nazir defended her Master Thesis on Visual Analytics on Enterprise Reports for Investment and Strategical Analysis

/in , , , /by

In her thesis, Sibgha Nazir created a visual analytical approach to analyze annual financial reports in the perspective of investors’ interests. The goal of the thesis is to make use of visual analytics for the fundamental analysis of a business to support investors and business decision-makers. The idea is to collect the financial reports, extract the data and feed them to the visual analytics system. Financial reports are PDF documents published by public companies annually and quarterly which are readily available on companies’ websites containing the values of all financial indicators which fully and vividly paint the picture of a companies’ business. The financial indicators in those reports make the basis of fundamental analysis. The thesis focuses on those manually collected reports from the companies’ websites and conceptualizes and implements a pipeline that gathers text and facts from the reports, processes them, and feeds them to a visual analytics dashboard. Furthermore, the thesis uses state-of-the-art visualization tools and techniques to implement a visual analytics dashboard as the proof of concept and extends the visualization interface with interaction capability by giving them options to choose the parameter of their choice allowing the analyst to filter and view the available data. The dashboard fully integrates with the data transformation pipeline to consume the data that has been collected, structured, and processed and aims to display the financial indicators as well as allow the user to display them graphically. It also implements a user interface for manual data correction ensuring continuous data cleansing.

The presented application makes use of state-of-the-art financial analytics and information visualization techniques to enable visual trend analysis. The application is a great tool for investors and business analysts for gaining insights into the business and analyzing historical trends of its earnings and expenses and several other use-cases where financial reports of the business are a primary source of valuable information.

More Information:

28 March 2022

Thesis Presentation: Visual Analytics on Enterprise Reports for Investment and Strategical Analysis

Where: TU Darmstadt / GRIS, Zoom: https://tu-darmstadt.zoom.us/j/83620126004?pwd=a1hyUkprRWpMVXd3eEpNRTBVYk9tUT09 Who: Sibgha Nazir (Author), Prof. Dr. Arjan Kuijper (Supervisor), Dipl.-Inf. Dirk Burkhardt (Advisor/Co-Supervisor) What: Master Thesis – “Visual Analytics on Enterprise Reports for […]

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TU Darmstadt / GRIS, Fraunhoferstraße 5
Darmstadt, Hessian 64283 Germany
+ Google Map

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”.

Advisory Board of the EUt+ Academic Press

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Professor Kawa Nazemi was assigned as an advisory board member of the academic press of the European University of Technology (EUt+).

The EUt+ is an alliance of eight universities that make up the European University of Technology. The partners are Technical University of Sofia, Cyprus University of Technology, University of Technology Troyes, Hochschule Darmstadt, University of Applied Sciences, Technological University Dublin, Riga Technical University, Technical University of Cluj-Napoca, Technical University of Cartagena.

The EUT+ is part of the European initiative to build a European Education Area. The EUT+ Academic Press is an open access academic press. All material is free to read online. The academic press is committed to the principle that scholarship should be available to all without barriers or paywalls. It offers authors a global readership and thus contributes to the dissemination of knowledge. The academic press welcomes quality works of scholarship regardless of the subject area. The material is licensed under Creative Commons Attribution-Non-commercial-No Derivatives 4.0 International (CC BY-NC-ND 4.0) license We are committed to Gold Open Access which is the free, immediate and permanent online access to a publication’s version of record. Currently, there are no fees for authors.

Please find more information here.

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.
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Lennart Sina defended his Master Thesis on Visual Analytics for Unstructured Data and Scalable Data Models

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In the thesis, Lennart Sina conceptualized and implemented a visual analytics system that scales data through middleware to enable more efficient analysis. For this purpose, diverse approaches and systems were investigated, which led to a coherent concept. The concept was implemented and connected to an existing database, enabling real-world use of the system and real-world conditions. The scientific contribution of the present work is three-fold: (1) the concept of a visual analytics system to scale data, (2) a novel data model, and (3) a novel and a fully implemented visual dashboard that also enables reporting.

 

 

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: DOI: 10.1007/s11042-021-10972-3

Tag Archive for: Visual Analytics

Thesis Presentation: Process Mining for Workflow-Driven Assistance in Visual Trend Analytics

/in , /by

Where: TU Darmstadt / GRIS, Zoom: https://tu-darmstadt.zoom.us/j/86845586436?pwd=RUJiWm1QdWJ4VDg3MU93WUNOWWFTQT09
Who: Shahrukh Badar (Author), Prof. Dr. Arjan Kuijper (Supervisor), Dipl.-Inf. Dirk Burkhardt (Advisor/Co-Supervisor)
What: Master Thesis – “Process Mining for Workflow-Driven Assistance in Visual Trend Analytics”

Abstract:

In today’s data-driven world, a large amount of data is being generated daily. This data is generated by different sources, such as social networking platforms, industrial machinery, daily transactions, etc. The companies or businesses are not only generating data but also utilizing them to improve their processes, business decisions, etc. There are several applications and tools that help users to analyze this big data in-depth, by providing numerous ways to explore it, including different types of visualization, pivoting, filtering, grouping data, etc. The challenge with such applications is that it creates long and heavy learning curves for users, who need to work with such applications. Many systems are often designed for a specific purpose, and therewith to know how a single system works is not enough. To enable a better work entrance with such an analytical system, a kind of adaptive assistance would be helpful. So, the system would hint the users regarding his previous work and interaction, what next action might be useful. The thesis aims to face this challenge with process-driven assistance that is applied to the visual trend analytics domain. The goal is, based on previous users interactions and solved tasks, to assist further users in their work. Therefore, a universal visual assistance model is defined and acts also as the main contribution, based on defined interaction event taxonomy. This concept is applied on the Visual Trend Analytics domain on the SciTic reference system, This “SciTic – Visual Trend Analytics” is connected with different data sources and provides analysis of scientific documents. The interaction model provides assistance in terms of recommendations, where the user has an option either to apply a recommendation or ignore it. The solution provided in this thesis is model-based and utilizes the potential of Process Mining and Discovery techniques. It is started by creating an event taxonomy by identifying all possible ways of user interactions on the “SciTic – Visual Trend Analytics” web application. Next, enable the “SciTic – Visual Trend Analytics” web application to start logging events chronologically based on predefined taxonomy. Later, these events log is converted into Process Mining log format. Next, it applies the Process Discovery algorithm “Heuristics Miner” on these log data to generate a process model, which shows the overall flow of user interaction along with the frequencies. Later, this process model is used to provide users with recommendations.

Thesis Presentation: Visual Analytics on Enterprise Reports for Investment and Strategical Analysis

/in , /by

Where: TU Darmstadt / GRIS, Zoom: https://tu-darmstadt.zoom.us/j/83620126004?pwd=a1hyUkprRWpMVXd3eEpNRTBVYk9tUT09
Who: Sibgha Nazir (Author), Prof. Dr. Arjan Kuijper (Supervisor), Dipl.-Inf. Dirk Burkhardt (Advisor/Co-Supervisor)
What: Master Thesis – “Visual Analytics on Enterprise Reports for Investment and Strategical Analysis”

Abstract:

Given the availability of enormous data in today’s time, suitable analysis techniques and graphical tools are required to derive knowledge in order to make this data useful. Scientists and developers have come up with visual analytical systems that combine machine learning technologies, such as text mining with interactive data visualization, to provide fresh insights into the present and future trends. Data visualization has progressed to become a cutting-edge method for displaying and interacting with graphics on a single screen. Using visualizations, decision-makers may unearth insights in minutes, and teams can spot trends and significant outliers in minutes [1]. A vast variety of automatic data analysis methods have been developed during the previous few decades. For investors, researchers, analysts, and decision-makers, these developments are significant in terms of innovation, technology management, and strategic decision-making.

The financial business is only one of many that will be influenced by the habits of the next generation, and it must be on the lookout for new ideas. Using cutting-edge financial analytics tools will, of course, have a significant commercial impact. Visual analytics, when added to the capabilities, can deliver relevant and helpful insights. By collecting financial internal information from different organizations, putting them in one place, and incorporating visual analytics tools, financial analytics software will address crucial business challenges with unprecedented speed, precision, and ease.

The goal of the thesis is to make use of visual analytics for the fundamental analysis of a business to support investors and business decision-makers. The idea is to collect the financial reports, extract the data and feed them to this visual analytics system. Financial reports are PDF documents published by public companies annually and quarterly which are readily available on companies’ websites containing the values of all financial indicators which fully and vividly paint the picture of a companies’ business. The financial indicators in those reports make the basis of fundamental analysis. The thesis focuses on those manually collected reports from the companies’ websites and conceptualizes and implements a pipeline that gathers text and facts from the reports, processes them, and feeds them to a visual analytics dashboard. Furthermore, the thesis uses state-of-the-art visualization tools and techniques to implement a visual analytics dashboard as the proof of concept and extends the visualization interface with interaction capability by giving them options to choose the parameter of their choice allowing the analyst to filter and view the available data. The dashboard fully integrates with the data transformation pipeline to consume the data that has been collected, structured, and processed and aims to display the financial indicators as well as allow the user to display them graphically. It also implements a user interface for manual data correction ensuring continuous data cleansing.

The presented application makes use of state-of-the-art financial analytics and information visualization techniques to enable visual trend analysis. The application is a great tool for investors and business analysts for gaining insights into a business and analyzing historical trends of its earnings and expenses and several other use-cases where financial reports of the business are a primary source of valuable 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:

Participation at IV 2022

/in , , , /by

Our group presented at this year’s Information Visualisation Conference (iV 2022) two papers entitled “Visual Analytics for Systematic Reviews According to PRISMA” and “Visual Collaboration – An Approach for Visual Analytical Collaborative Research”. In addition, Professor Kawa Nazemi, the head of the research group, was a co-chair of the conference and chaired the program committee and various sessions and tracks. Due to the ease of COVID travel restrictions, we were able to participate in person Vienna. The iV 2022 is an international conference that aims to provide a foundation for integrating information visualization’s human-centered, technological and strategic aspects to promote international exchange, cooperation, and development.

Paper #1: Visual Analytics for Systematic Reviews According to PRISMA

Lead author: Lennart B. Sina

Abstract:
Systematic reviews play an essential role in various disciplines. Particularly, in biomedical sciences, systematic reviews according to a predefined schema and protocol are how related literature is analyzed. Although a protocol-based systematic review is replicable and provides the required information to reproduce each step and refine them, such a systematic review is time-consuming and may get complex. To face this challenge, automatic methods can be applied that support researchers in their systematic analysis process. The combination of artificial intelligence for automatic information extraction from scientific literature with interactive visualizations as a Visual Analytics system can lead to sophisticated analysis and protocoling of the review process. We introduce in this paper a novel Visual Analytics approach and system that enables researchers to visually search and explore scientific publications and generate a protocol based on the PRISMA protocol and the PRISMA statement.

Link to paper/fulltext: Soon available

Paper #2: Visual Collaboration – An Approach for Visual Analytical Collaborative Research

Lead author: Midhad Blazevic

Abstract:

Studies have shown that collaboration in scientific fields is rising and considered enormously important. However, collaboration has proved to be challenging for various reasons, among others, the requirements for human-machine workflows. The importance of scientific collaboration lies in the complexity of the challenges that are faced today. The more complex the challenge, the more scientists should work together. The current form of collaboration in the scientific community is not as intelligent as it should be. Scientists have to multitask with various applications, often losing cognitive focus. Collaboration itself is very nearsighted as it is usually conducted not solely based on expertise but instead on social or local networks. We introduce a single-source visual collaboration approach based on learning methods in this work. We use machine learning and natural language processing approaches to improve the traditional research and development process and create a system that facilitates and encourages collaboration based on expertise, enhancing the research collaboration process in many ways. Our approach combines collaborative Visual Analytics with enhanced collaboration techniques to support researchers from different disciplines

Link to paper/fulltext: Soon available

Book published– Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery

/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.

This book is a collection of 25 extended works of over 70 scholars presented at AI and visual analytics-related symposia at the recent International Information Visualization Conferences with the goal of moving this integration to the next level.  The sections of this book cover integrated systems, supervised learning, unsupervised learning, optimization, and evaluation of visualizations.

The intended audience for this collection includes those developing and using emerging AI/machine learning and visualization methods. Scientists, practitioners, and students can find multiple examples of the current integration of AI/machine learning and visualization for visual knowledge discovery. The book provides a vision of future directions in this domain. New researchers will find here an inspiration to join the profession and to be involved for further development. Instructors in AI/ML and visualization classes can use it as a supplementary source in their undergraduate and graduate classes.

Shahrukh Badar defended his Master Thesis on Process Mining for Workflow-Driven Assistance in Visual Trend Analytics

/in , , , /by

In his thesis, Shahrukh Badar created process-driven assistance that is applied to the visual trend analytics domain. The goal was, based on previous users interactions and solved tasks, to assist further users in their work. Therefore, a universal visual assistance model was defined and acts also as the main contribution, based on defined interaction event taxonomy. This concept was applied to the Visual Trend Analytics domain on the SciTics reference system. This “SciTics – Science Analytics” is connected with different data sources and provides analysis of scientific documents. The interaction model provides assistance in terms of recommendations, where the user has an option either to apply a recommendation or ignore it. The solution provided in this thesis is model-based and utilizes the potential of Process Mining and Discovery techniques. It is started by creating an event taxonomy by identifying all possible ways of user interactions on the “SciTic – Visual Trend Analytics” web application. Next, enable the “SciTic – Visual Trend Analytics” web application to start logging events chronologically based on predefined taxonomy. Later, these events log is converted into Process Mining log format. Next, it applies the Process Discovery algorithm “Heuristics Miner” on these log data to generate a process model, which shows the overall flow of user interaction along with the frequencies. Later, this process model is used to provide users with recommendations.

More Information:

26 April 2022

Thesis Presentation: Process Mining for Workflow-Driven Assistance in Visual Trend Analytics

Where: TU Darmstadt / GRIS, Zoom: https://tu-darmstadt.zoom.us/j/86845586436?pwd=RUJiWm1QdWJ4VDg3MU93WUNOWWFTQT09 Who: Shahrukh Badar (Author), Prof. Dr. Arjan Kuijper (Supervisor), Dipl.-Inf. Dirk Burkhardt (Advisor/Co-Supervisor) What: Master Thesis – “Process Mining for Workflow-Driven Assistance in […]

Find out more »
TU Darmstadt / GRIS, Fraunhoferstraße 5
Darmstadt, Hessian 64283 Germany
+ Google Map

Thesis Presentation: Process Mining for Workflow-Driven Assistance in Visual Trend Analytics

/in , /by

Where: TU Darmstadt / GRIS, Zoom: https://tu-darmstadt.zoom.us/j/86845586436?pwd=RUJiWm1QdWJ4VDg3MU93WUNOWWFTQT09
Who: Shahrukh Badar (Author), Prof. Dr. Arjan Kuijper (Supervisor), Dipl.-Inf. Dirk Burkhardt (Advisor/Co-Supervisor)
What: Master Thesis – “Process Mining for Workflow-Driven Assistance in Visual Trend Analytics”

Abstract:

In today’s data-driven world, a large amount of data is being generated daily. This data is generated by different sources, such as social networking platforms, industrial machinery, daily transactions, etc. The companies or businesses are not only generating data but also utilizing them to improve their processes, business decisions, etc. There are several applications and tools that help users to analyze this big data in-depth, by providing numerous ways to explore it, including different types of visualization, pivoting, filtering, grouping data, etc. The challenge with such applications is that it creates long and heavy learning curves for users, who need to work with such applications. Many systems are often designed for a specific purpose, and therewith to know how a single system works is not enough. To enable a better work entrance with such an analytical system, a kind of adaptive assistance would be helpful. So, the system would hint the users regarding his previous work and interaction, what next action might be useful. The thesis aims to face this challenge with process-driven assistance that is applied to the visual trend analytics domain. The goal is, based on previous users interactions and solved tasks, to assist further users in their work. Therefore, a universal visual assistance model is defined and acts also as the main contribution, based on defined interaction event taxonomy. This concept is applied on the Visual Trend Analytics domain on the SciTic reference system, This “SciTic – Visual Trend Analytics” is connected with different data sources and provides analysis of scientific documents. The interaction model provides assistance in terms of recommendations, where the user has an option either to apply a recommendation or ignore it. The solution provided in this thesis is model-based and utilizes the potential of Process Mining and Discovery techniques. It is started by creating an event taxonomy by identifying all possible ways of user interactions on the “SciTic – Visual Trend Analytics” web application. Next, enable the “SciTic – Visual Trend Analytics” web application to start logging events chronologically based on predefined taxonomy. Later, these events log is converted into Process Mining log format. Next, it applies the Process Discovery algorithm “Heuristics Miner” on these log data to generate a process model, which shows the overall flow of user interaction along with the frequencies. Later, this process model is used to provide users with recommendations.

Sibgha Nazir defended her Master Thesis on Visual Analytics on Enterprise Reports for Investment and Strategical Analysis

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In her thesis, Sibgha Nazir created a visual analytical approach to analyze annual financial reports in the perspective of investors’ interests. The goal of the thesis is to make use of visual analytics for the fundamental analysis of a business to support investors and business decision-makers. The idea is to collect the financial reports, extract the data and feed them to the visual analytics system. Financial reports are PDF documents published by public companies annually and quarterly which are readily available on companies’ websites containing the values of all financial indicators which fully and vividly paint the picture of a companies’ business. The financial indicators in those reports make the basis of fundamental analysis. The thesis focuses on those manually collected reports from the companies’ websites and conceptualizes and implements a pipeline that gathers text and facts from the reports, processes them, and feeds them to a visual analytics dashboard. Furthermore, the thesis uses state-of-the-art visualization tools and techniques to implement a visual analytics dashboard as the proof of concept and extends the visualization interface with interaction capability by giving them options to choose the parameter of their choice allowing the analyst to filter and view the available data. The dashboard fully integrates with the data transformation pipeline to consume the data that has been collected, structured, and processed and aims to display the financial indicators as well as allow the user to display them graphically. It also implements a user interface for manual data correction ensuring continuous data cleansing.

The presented application makes use of state-of-the-art financial analytics and information visualization techniques to enable visual trend analysis. The application is a great tool for investors and business analysts for gaining insights into the business and analyzing historical trends of its earnings and expenses and several other use-cases where financial reports of the business are a primary source of valuable information.

More Information:

28 March 2022

Thesis Presentation: Visual Analytics on Enterprise Reports for Investment and Strategical Analysis

Where: TU Darmstadt / GRIS, Zoom: https://tu-darmstadt.zoom.us/j/83620126004?pwd=a1hyUkprRWpMVXd3eEpNRTBVYk9tUT09 Who: Sibgha Nazir (Author), Prof. Dr. Arjan Kuijper (Supervisor), Dipl.-Inf. Dirk Burkhardt (Advisor/Co-Supervisor) What: Master Thesis – “Visual Analytics on Enterprise Reports for […]

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Thesis Presentation: Visual Analytics on Enterprise Reports for Investment and Strategical Analysis

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Where: TU Darmstadt / GRIS, Zoom: https://tu-darmstadt.zoom.us/j/83620126004?pwd=a1hyUkprRWpMVXd3eEpNRTBVYk9tUT09
Who: Sibgha Nazir (Author), Prof. Dr. Arjan Kuijper (Supervisor), Dipl.-Inf. Dirk Burkhardt (Advisor/Co-Supervisor)
What: Master Thesis – “Visual Analytics on Enterprise Reports for Investment and Strategical Analysis”

Abstract:

Given the availability of enormous data in today’s time, suitable analysis techniques and graphical tools are required to derive knowledge in order to make this data useful. Scientists and developers have come up with visual analytical systems that combine machine learning technologies, such as text mining with interactive data visualization, to provide fresh insights into the present and future trends. Data visualization has progressed to become a cutting-edge method for displaying and interacting with graphics on a single screen. Using visualizations, decision-makers may unearth insights in minutes, and teams can spot trends and significant outliers in minutes [1]. A vast variety of automatic data analysis methods have been developed during the previous few decades. For investors, researchers, analysts, and decision-makers, these developments are significant in terms of innovation, technology management, and strategic decision-making.

The financial business is only one of many that will be influenced by the habits of the next generation, and it must be on the lookout for new ideas. Using cutting-edge financial analytics tools will, of course, have a significant commercial impact. Visual analytics, when added to the capabilities, can deliver relevant and helpful insights. By collecting financial internal information from different organizations, putting them in one place, and incorporating visual analytics tools, financial analytics software will address crucial business challenges with unprecedented speed, precision, and ease.

The goal of the thesis is to make use of visual analytics for the fundamental analysis of a business to support investors and business decision-makers. The idea is to collect the financial reports, extract the data and feed them to this visual analytics system. Financial reports are PDF documents published by public companies annually and quarterly which are readily available on companies’ websites containing the values of all financial indicators which fully and vividly paint the picture of a companies’ business. The financial indicators in those reports make the basis of fundamental analysis. The thesis focuses on those manually collected reports from the companies’ websites and conceptualizes and implements a pipeline that gathers text and facts from the reports, processes them, and feeds them to a visual analytics dashboard. Furthermore, the thesis uses state-of-the-art visualization tools and techniques to implement a visual analytics dashboard as the proof of concept and extends the visualization interface with interaction capability by giving them options to choose the parameter of their choice allowing the analyst to filter and view the available data. The dashboard fully integrates with the data transformation pipeline to consume the data that has been collected, structured, and processed and aims to display the financial indicators as well as allow the user to display them graphically. It also implements a user interface for manual data correction ensuring continuous data cleansing.

The presented application makes use of state-of-the-art financial analytics and information visualization techniques to enable visual trend analysis. The application is a great tool for investors and business analysts for gaining insights into a business and analyzing historical trends of its earnings and expenses and several other use-cases where financial reports of the business are a primary source of valuable information.

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”.

Advisory Board of the EUt+ Academic Press

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Professor Kawa Nazemi was assigned as an advisory board member of the academic press of the European University of Technology (EUt+).

The EUt+ is an alliance of eight universities that make up the European University of Technology. The partners are Technical University of Sofia, Cyprus University of Technology, University of Technology Troyes, Hochschule Darmstadt, University of Applied Sciences, Technological University Dublin, Riga Technical University, Technical University of Cluj-Napoca, Technical University of Cartagena.

The EUT+ is part of the European initiative to build a European Education Area. The EUT+ Academic Press is an open access academic press. All material is free to read online. The academic press is committed to the principle that scholarship should be available to all without barriers or paywalls. It offers authors a global readership and thus contributes to the dissemination of knowledge. The academic press welcomes quality works of scholarship regardless of the subject area. The material is licensed under Creative Commons Attribution-Non-commercial-No Derivatives 4.0 International (CC BY-NC-ND 4.0) license We are committed to Gold Open Access which is the free, immediate and permanent online access to a publication’s version of record. Currently, there are no fees for authors.

Please find more information here.

Book appeared – Integrating Artificial Intelligence and Visualization

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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.
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26th International Conference Information Visualization (iV 2022)

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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


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