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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

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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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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

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

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

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.

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.

Tag Archive for: Digital Libraries

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

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

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

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

Abstract:

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

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

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

Abstract:

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

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