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Paper accepted at 14th International Symposium on Visual Computing (ISVC 2019)

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Our paper entitled “A Visual Analytics Approach for Analyzing Technological Trends in Technology and Innovation Management” is accepted at the visual computing conference ISVC 2019 in Lake Tahoe, NV, USA. The purpose of the International Symposium on Visual Computing (ISVC) is to provide a first-class forum for researchers, scientists, engineers and practitioners throughout the world to present their latest research findings, ideas, developments and applications in visual computing. ISVC seeks papers describing contributions to the state of the art and state of the practice in the four central areas of visual computing: computer vision, computer graphics, virtual reality, and visualization. The paper reflects some new on-going advancements of our innovative Trend Analytics solution Scitics.

Paper: A Visual Analytics Approach for Analyzing Technological Trends in Technology and Innovation Management

Abstract:
Visual Analytics provides with a combination of automated techniques and interactive visualizations huge analytics possibilities in technology and innovation management. Thereby not only the use of machine learning data mining methods plays an important role. Due to the high interaction capabilities, it provides a more user-centered approach, where users are able to manipulate the entire analysis process and get the most valuable information. Existing Visual Analytics systems for Trend Analytics and technology and innovation management do not really make use of this unique feature and do not include the human in the analysis process. Outcomes from research on information search, information visualization and technology management can lead to more sophisticated Visual Analytics systems that involved the human in the entire analysis process. We propose in this paper a new interaction approach for Visual Analytics in technology and innovation management with a special focus on technological trend analytics.

Link to paper/fulltext: https://dx.doi.org/10.1007/978-3-030-33723-0_23
Link to poster: https://dx.doi.org/10.5281/zenodo.3473065

More information about the technology: Scitics for Visual Trend Analytics

Best Paper Award at the iV 2019

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We are proud to announce that our last paper “Visual Analytics for Analyzing Technological Trends from Text” at the iV2019 conference in Paris was honored with “The Best Paper Award” for the innovative contribution in terms of originality of concepts and application. The “Best Paper Awards” is given to contributions which will be selected by the committee among the papers presented in iV2019 and applied for the award. Study’s relevance to the symposium’s scope, its scientific contribution, writing/presentation style will be considered in the evaluation process as well.

A short interview can be found here (German).

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.

Paper accepted at 23rd Internation Conference Information Visualization (iV 2019)

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We are very glad to be accepted for presenting our paper titled “Visual Analytics for Analyzing Technological Trends from Text” at the high-class conference iV 2019 in Paris, France. 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.

Paper: Visual Analytics for Analyzing Technological Trends from Text

Abstract:
The awareness of emerging technologies is essential for strategic decision making in enterprises. Emerging and decreasing technological trends could lead to strengthening the competitiveness and market positioning. The exploration, detection and identification of such trends can be essentially supported through information visualization, trend mining and in particular through the combination of those. Commonly, trends appear first in science and scientific documents. However, those documents do not provide sufficient information for analyzing and identifying emerging trends. It is necessary to enrich data, extract information from the integrated data, measure the gradient of trends over time and provide effective interactive visualizations. We introduce in this paper an approach for integrating, enriching, mining, analyzing, identifying and visualizing emerging trends from scientific documents. Our approach enhances the state of the art in visual trend analytics by investigating the entire analysis process and providing an approach for enabling human to explore undetected potentially emerging trends.

Link to paper/fulltext: https://dx.doi.org/10.1109/IV.2019.00041

More information about the technology: Scitics for Visual Trend Analytics

Demo video:

Visual Trend Analytics@Information Science Trends: Search Engines and Information Retrieval

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The event “Information Science Trends: Search Engines and Information Retrieval”, organized and hosted by the European Chapter of the Association for Information Science and Technology, focuses on certain research topics introduced by invited speeches from research and industry. The major covered research areas are:

  • Retrieval research on web search engines, web services, professional information services, social media
  • Information seeking behavior
  • Information retrieval
  • Discovery systems
  • People interacting with search systems
  • Searching in the library and other scientific contexts
  • Understanding user behavior

Additionally, they will provide networking space to energetically share ideas and interests from the fields of information science and related disciplines.

At this event Prof. Dr. Nazemi gives a speech on “Visual Trend Analytics in Digital Libraries”, in particular based on the experiences with the own developed Scitics solution. Here he gives an overview about current research actions and challenges toward visual trend analysis, but also on opportunities and benefits for companies. In particular the digitalization challenges a number of small and medium sized enterprises to be aware of modern technologies and trends that revolute the market or impacts the current businesses

Show full event video on YouTube

The event takes places on April 26, 2019, at the Hamburg University of Applied Sciences in Hamburg, Germany.
More information about the event can be found on the event website or on the organizers website.

Kickoff of the National AVARTIM Project

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Today is the kickoff of the national project AVARTIM. The project has to goal of the early recognition of technological trends and the targeted handling of market and technology signals are becoming increasingly important for companies. As part of the project, “AVARTIM” is to be used to develop a software-supported process for recognizing and evaluating trends, market and technology signals in order to sustainably support the process of innovation and technology management. As part of the project, an infrastructure will be set up at Darmstadt University of Applied Sciences, which is modular and thus able to react quickly to technological changes. The infrastructure to be developed here serves as preliminary research and initial technology both for industrial use by and with the SME partners as well as for the application for joint projects. First of all, participation in the LOEWE funding line 3 of the state of Hessen and subsequently in the tender for LEIT-ICT / Big Data technologies of the EU is aimed at.

More about the project you will find on project description site. The work on the infrastructure as well as the developments are all made on our novel Scitics solution.

Leading Article to „Visual Analytics for Early Technology Trend Detection”

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Prof. Nazemi wrote an interesting leading article for the Research Center of Applied Informatics about his research toward visual analytics technologies for early technology trend detection. In this article he stated the relevance of noticing early indicators for the detection of new and, above all, socially and economically important technologies that can provide scientific publications. For this purpose, he and his team developed a trend analytics solution that covers a data processing pipeline beginning with the collection and transformation of unstructured text to a final visual interactive interface that enables analysts to elaborate trends, leaders and further relevant business aspects. Please note the link to the full article: https://fzai.h-da.de/2019/02/prof-dr-kawa-nazemi-visual-analytics-zur-technologiefrueherkennung/.

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: Visual Trend Analysis on Digital Semantic Library Data for Innovation Management

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When: 12/11/2018 16:00 Where: Frauhofer IGD, Fraunhoferstr. 5, Room 0.73 Who: Ranveer Purey (Author), Dipl.-Inf. Dirk Burkhardt (Coordinator/Co-Supervisor), Prof. Dr. Arjan Kuijper (Supervisor) What: Master Thesis – “Visual Trend Analysis on Digital Semantic Library Data for Innovation Management” Abstract: The amount of scientific data published online has been witnessing massive growth in the recent years. This has led to exponential growth in the amount of data stored in digital libraries (DLs) such as springer, eurographics, digital bibliography and library Project (dblp), etc. One of the major challenges is to prevent users from getting lost in irrelevant search results, when they try to retrieve information in order to get meaningful insights from these digital libraries. This problem is known as information overload. Other challenge is the quality of data in digital libraries. A quality of data can be affected by factors such as missing information, absence of links to external databases or data is not well structured, and the data is not semantically annotated. Apart from data quality, one more challenge is the fact that, there are tools available which help users in retrieving and visualizing the information from large data sets, but these tools lack one or the other basic requirements like data mining, visualizations, interaction techniques etc. These issues and challenges have led to increase in the research in the field of visual analytics, it is a combination of data processing, information visualization, and human computer interaction disciplines. The main goal of this thesis is to overcome the information overload problem and the challenges mentioned above. This can be achieved by using digital library named SciGraph by springer, which serves as a very rich source of semantically annotated data. The data from SciGraph can be used in combination with data integration, data mining and information visualization techniques in order to aid users in decision making process and perform visual trend analysis on digital semantic library data. This concept would be designed and developed as a part of innovation management process, which helps transforming innovative ideas into reality using a structured process. In this thesis, a conceptual model for performing visual trend analysis on digital semantic library data as part of innovation management process had been proposed and implemented. In order to create the conceptual model, several disciplines such as human computer interaction, trend detection methods, user centered design, user experience and innovation management have been researched upon. In addition, evaluation of various information visualization tools for digital libraries has been carried out in order to find out and address the challenges faced by these tools. The conceptual model proposed in this thesis, combines the usage of semantic data with information visualization process and also follows structured innovation management process, in order to ensure that the concept and implementation (proof of concept) are valid, usable and valuable to the user.

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

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

Events

24rd International Conference Information Visualization (iV 2020)

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We are co-organizing track International Symposium Visual Analytics and Data Science at the next International Information Visualisation Conference (iV 2020) in Vienna, Austria. The Information Visualisation Conference (iV) is an international conference that aims to provide a foundation for integrating the human-centered, technological and strategic aspects of information visualization to promote international exchange, cooperation and development. The scope of the conference covers the following topics:
  • Combining visual and computational methods of Data Analysis, Machine Learning and Artificial Intelligence
  • Visual Analytics models and approaches
  • Novel Visual Analytics applications
  • Visual Trend Analytics
  • Visual Analytics of spatial, temporal, and spatio-temporal data
  • Knowledge construction and management in Visual Analytics
  • Guidance in Visual Analytics
  • Intelligent approaches of Visual Analytics and Data Science
  • Adaptive Visual Analytics
  • Cognitive approaches and explanations for Visual Analytics
  • Visual Analytics for explaining AI
  • Visualization of Data Mining algorithms
  • Empirical performance studies
  • Evaluation of Visual Data Mining methods
  • Collaborative Visual
Please note the iV2020 Call for Papers (CfP).
The Visual Analytics and Data Science track is organized via the 12th International Symposium Visual Analytics and Data Science. Visual Analytics is viewed as the science of analytical reasoning empowered by interactive visualizations. It combines interactive visualizations with models and approaches of machine learning and artificial intelligence, enabling solving complex analytical tasks by uncovering hidden patterns in data. The research on Visual Analytics is closely related to that of Data Science. Both areas seek to enhance the knowledge discovery process using machine learning, data mining and artificial intelligence methods, whereas Visual Analytics allows commonly a direct manipulation of the underlying models through graphical representations. By leveraging human perception of the visual space, patterns that might not otherwise be discovered. Visual Analytics utilizes concepts from a wide variety of disciplines, including Computer Graphics, Information Visualization, Machine Learning, Artificial Intelligence, Knowledge Discovery, Cognition and Visual Perception. The scope of the VA track covers the following topics:
  • Combining visual and computational methods of Data Analysis, Machine Learning and Artificial Intelligence
  • Visual Analytics models and approaches
  • Novel Visual Analytics applications
  • Visual Trend Analytics
  • Visual Analytics of spatial, temporal, and spatio-temporal data
  • Knowledge construction and management in Visual Analytics
  • Guidance in Visual Analytics
  • Intelligent approaches of Visual Analytics and Data Science
  • Adaptive Visual Analytics
  • Cognitive approaches and explanations for Visual Analytics
  • Visual Analytics for explaining AI
  • Visualization of Data Mining algorithms
  • Empirical performance studies
  • Evaluation of Visual Data Mining methods
  • Collaborative Visual Analytics and Data Science
  • Computational steering for long-running Data Mining applications
  • Reviews and surveys of related literature

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