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Kickoff of the Stategic Networking Project VisCOST

The now started project VisCOST aims to create a European research network to promote innovation and technology management through the interdisciplinary use of methods of visual analysis, artificial intelligence, simulation, prediction and planning of new technologies and innovations for corporate purposes (smart manufacturing, early technology detection etc.), the optimization of government strategies (e-governance) and the involvement of European citizens in government decisions (e-participation) but also in research. The core targets on answering the research question “How can innovations be created and strengthened, and future technologies as well as possible scenarios be predicted by the approaches that are currently at the forefront of technology research in order to make strategic decisions in a more targeted manner?”. A very interdisciplinary research is necessary to answer this question. Knowledge from management, such as innovation, technology and information management, but also strategic planning and predictions in companies (corporate foresight) must be brought in from economics.

Further information

– More information to the project VisCOST

– More information to the build research network

– Website about COST-actions

Two succeeful Submissions to ICTE in Transportation and Logistics book

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

Research Day 2019 of the Hessian Universities of Applied Sciences (HAW)

October 29th was an important day for researchers in the applied research domain in Hesse, since they stayed in focus at the the Research Day 2019 of the Hessian Universities of Applied Sciences (HAW). On this day the spotlight of the he Hessen State Ministry for Higher Education, Research and the Arts layed on current results and achievements of researchers. Next to several awards for research results, it was also a great opportunity to show results and use the chance for networking.

Our team was invited to attend the event and presented ongoing results toward Advanced Visual Analytical Reasoning for Technology and Innovation Management, particularly the progress of the Hessen funded research project AVARTIM. As brief overview to the Visual Trend Analytics and Visual Business Analytics topic, we provided a poster with the summary of our current insights, which led to a variety of interesting discussions and new contacts for promising further collaboration actions – among other also for our planned European research network.

We really enjoyed the event and the opportunity to get connected with other Hessian researchers and look forward for some further collaborative actions – and, of course, the next Research Day of the Hessian Universities of Applied Sciences (HAW).

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

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

Insight on Visual Text Analytics for Technology and Innovation Management at OpenRheinMain Conference

We get the opportunity to give some insights to “Visual Text Analytics for Technology and Innovation Management”, based on our core Trend Analytics technology Scitics, on the OpenRheinMain Conference. We want to give insights on how Visual Analytics techniques can be used to enable effective technology and innovation management on behalf of external/web data as well as internal/company data.

The OpenRheinMain (ORM 2019) is the 1st edition of an annual IT conference on open source and emerging digital technologies. This includes, but is not limited to, Open-Source, Intelligent Automation and DevOps, Cloud Computing, and Internet of Things. The purpose of the conference is to interlink researchers and industrial partner of the Rhein Main region. Therefore, the conference considers stakeholders from both in an appropriate proportion. The conference will take place on September 13th, 2019 at Darmstadt University of Applied Science.

Due to heterogeneity of the event participants, the chances are high to strengthen the cooperation with local enterprises. We expect, that this will be relevant for further research actions to strengthen  the local region.

The extended abstract of the presentation is available under: https://dx.doi.org/10.5281/zenodo.3408391
More information on the event website: https://www.openrheinmain.org

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

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 ManagementAbstract: 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.

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