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

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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.

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

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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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TU Darmstadt / GRIS, Fraunhoferstraße 5
Darmstadt, Hessian 64283 Germany
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Kick-off Meeting of the Project “Digitallabor Groß-Gerau – Dozenturio”

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Digitization and digital education are playing an increasingly important role in career guidance, career preparation, and vocational education. Various open technologies and didactic approaches are available that can be used for digital teaching in vocational education.

However, basic digital education and knowledge about the possibilities of digital teaching are often lacking. The VIS-Group is cooperating with the Groß-Gerau district and the Center for Applied Computer Science at the Darmstadt University of Applied Sciences to provide a single-source of learning materials and learning technologies in the “Dozenturio” learning system.

The intermediate results of the project were introduced in December 15th 2021 by the VIS-Group. The event started with a welcome by the District Administrator Thomas Will followed by the main goals and conceptual structure by Nicole Möhlenkamp from the Department of Education and Schools – Youth Vocational Assistance, Qualification, and Employment. Among others, representatives of the adult education centers of the district and the city of Rüsselsheim am Main, the municipal job center of the district of Groß-Gerau, the state education authority and the IT center of the district were present. District Administrator Thomas Will and First District Deputy Walter Astheimer also took part in the panel discussion.

The project website already includes various video training courses on communication systems, various selected OER learning platforms (OER: Open Educational Resources), and a variety of technologies to enable both the digital transformation and the didactical transformation of digital content. The platform is accessible to anyone who wants to learn about digital teaching and communication through www.dozenturio.de.

The goal of the project is to develop qualification modules for digital teaching, learning, advising, and communicating. In addition, demand-oriented training offers for employees of the district as well as for regional educational institutions are planned. As part of the training and qualification budget of the state of Hesse. The district of Groß-Gerau has been provided with funds for the implementation of digital learning offerings for the period from September 1, 2020 to August 31, 2022, which will be used for the project described. With this funding program, the district of Groß-Gerau is taking advantage of the opportunities offered by digitization and opening up access opportunities for multipliers, teachers, and advisors.

Further information

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.

Midhad Blazevic defended his Master Thesis on Visual Search and Exploration for Scientific Publications through Similarity

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In his thesis, Midhad Blazevic alyzes exploratory search systems which use sophisticated features, visualizations and similarity-based algorithms to enhance exploratory searches. Examining how similarity algorithms are currently used in combination with elements from information retrieval, natural language processing and visualizations, but also examine what exploratory search is, what the requirements are and what makes it so special in modern times. Furthermore, the user himself or herself will be analyzed as user behavior during exploratory searches is a key factor that has to be taken into consideration when looking to optimize the exploratory search process overall. Based on these aspects, means of improvement will be developed and showcased, which will be used to determine if there is an improvement in comparison to other well-known systems. The outcome of this thesis will present a prototype of an exploratory search system along with a practical use case.

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

Thesis Presentation: User-Centered Scientific Publication Research and Exploration in Digital Libraries

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When: 10/12/2018 15:30
Where: Frauhofer IGD, Fraunhoferstr. 5, Room 220
Who: Namitha Chandrashekara (Author), Dipl.-Inf. Dirk Burkhardt (Advisor/Co-Supervisor), Prof. Dr. Arjan Kuijper (Supervisor)

What: Master Thesis – “User-Centered Scientific Publication Research and Exploration in Digital Libraries”

Abstract:

Scientific research is the basis for innovations. Surveying the research papers is an essential step in the process of research. It is vital to elaborate the intended writing of state of the art. Due to the rapid growth in scientific and technical discoveries, there is an increasing availability of publications. The traditional method of publishing the research papers includes physical libraries and books. These become hard to document with the rise in the number of publications produced. Due to the above mentioned problem, online archives for scientific publications have become more prominent in the scientific community. The availability of the search engines and digital libraries help the researchers in identifying the scientific publications. However, they provide limited search capabilities and visual interface. Most of the search engines have a single field to search and provides basic filtering of the data. Therefore, even with popular search engines, it is hard for the user to survey the research papers as it limits the user to search based on simple keywords. The relationships across multiple fields of the publications are also not considered such as to find the related papers and papers based on the citations or references.
The main aim of the thesis is to develop a visual access to the digital libraries based on the scientific research and exploration. It helps the user in writing scientific papers. A scientific research and exploration model is developed based on the previous information visualization model for visual trend analysis with digital libraries, and with consideration of the research process. The principles from Visual Seeking Mantra are incorporated to have an interactive user interface that enhances the user experience.
In the scope of this work, a research on Human Computer Interaction, particularly considering the aspects of user interface design are done. An overview of the scientific research, its types and various aspects of data analysis are researched. Different research models, existing approaches and tools that help the researchers in literature survey are also researched. The architecture and the implementation details of scientific research and exploration that provides visual access to digital libraries are presented.

Thesis Presentation: Automated User Evaluation Analysis for a Simplified and Continuous Software Development

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When: 26/06/2018 16:00
Where: Frauhofer IGD, Fraunhoferstr. 5, Room 324
Who: Akshay Madhav Deshmukh (Author), Dipl.-Inf. Dirk Burkhardt (Coordinator/Co-Supervisor), Prof. Dr. Arjan Kuijper (Supervisor)

What: Master Thesis – “Automated User Evaluation Analysis for a Simplified and Continuous Software Development“

Abstract:

In today’s world, computers are tightly coupled with the internet and play a vital role in the development of business and various aspects of human lives. Hence, developing a quality user-computer interface has become a major challenge. Well-designed programs that are easily usable by users are moulded through a regress development life cycle. To ensure a user friendly interface, the interface has to be well designed and need to support smart interaction features. User interface can become an Archilles heel in a developed system because of the simple design mistakes which causes critical interaction problems which eventually leads to massive loss of attractiveness in the system. To overcome this problem, regular and consistent user evaluations have to be carried out to ensure the usability of the system.
The importance of an evaluation for the development of a system is well known. Most of the today’s existing approaches necessitate the users to carry out an evaluation in a laboratory. Evaluators are compelled to dedicate the time in informing the participants about the evaluation process and providing a clear understanding of the questionnaires during the experiment. At the post experiment phase, evaluators have to invest a huge amount of time in generating a result report. On the whole, most of the today’s existing evaluation approaches hogs up too much of time for most developments.
The main aim of this thesis is to develop an automated evaluation management and result analysis, based on a previous developed web-based evaluation system, which enables to elaborate the evaluation results and identify required changes on the developed system. The major idea is that an evaluation can be prepared once and repeated in regular time intervals with different user groups. The automated evaluation result analysis allows to easily check if the continued development lead to better results and if a bunch of given task could be better solved e.g. by added new functions or through enhanced presentation.
Within the scope of this work, Human-Computer Interaction (HCI) was researched, in particular towards User-Centered Design (UCD) and User Evaluation. Different approaches for an evaluation were researched in particular towards an evaluation through expert analysis and user participation. Existing evaluation strategies and solutions, inclined towards distributed evaluations in the form of practical as well as survey based evaluation methods were researched. A proof of concept of an automated evaluation result analysis that enables an easy detection of gaps and improvements in the system was implemented. Finally, the results of the research project Smarter Privacy were compared with the manual performed evaluation.

Tag Archive for: User-Centered Design

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)

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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


Related news for further information:

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

/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.

Kick-off Meeting of the Project “Digitallabor Groß-Gerau – Dozenturio”

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Digitization and digital education are playing an increasingly important role in career guidance, career preparation, and vocational education. Various open technologies and didactic approaches are available that can be used for digital teaching in vocational education.

However, basic digital education and knowledge about the possibilities of digital teaching are often lacking. The VIS-Group is cooperating with the Groß-Gerau district and the Center for Applied Computer Science at the Darmstadt University of Applied Sciences to provide a single-source of learning materials and learning technologies in the “Dozenturio” learning system.

The intermediate results of the project were introduced in December 15th 2021 by the VIS-Group. The event started with a welcome by the District Administrator Thomas Will followed by the main goals and conceptual structure by Nicole Möhlenkamp from the Department of Education and Schools – Youth Vocational Assistance, Qualification, and Employment. Among others, representatives of the adult education centers of the district and the city of Rüsselsheim am Main, the municipal job center of the district of Groß-Gerau, the state education authority and the IT center of the district were present. District Administrator Thomas Will and First District Deputy Walter Astheimer also took part in the panel discussion.

The project website already includes various video training courses on communication systems, various selected OER learning platforms (OER: Open Educational Resources), and a variety of technologies to enable both the digital transformation and the didactical transformation of digital content. The platform is accessible to anyone who wants to learn about digital teaching and communication through www.dozenturio.de.

The goal of the project is to develop qualification modules for digital teaching, learning, advising, and communicating. In addition, demand-oriented training offers for employees of the district as well as for regional educational institutions are planned. As part of the training and qualification budget of the state of Hesse. The district of Groß-Gerau has been provided with funds for the implementation of digital learning offerings for the period from September 1, 2020 to August 31, 2022, which will be used for the project described. With this funding program, the district of Groß-Gerau is taking advantage of the opportunities offered by digitization and opening up access opportunities for multipliers, teachers, and advisors.

Further information

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


Related news for further information:

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.

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:

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