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Kickoff of the Stategic Networking Project VisCOST

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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

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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

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

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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)

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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

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

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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

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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.

Events

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.

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

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On 29 October, the focus will be on research at the HAWs in Hesse: within the framework of the Research Day 2019, the research awards will be presented to HAW Hessen. To the agenda: After the welcome by President Prof. dr. Frank E.P. Dievernich speaks to Secretary of State Ayse Asar (HMWK). Prof. Dr. Dr. h.c. Volker Mosbrugger (Senckenberg Society for Natural Research, Polytechnic Society Frankfurt am Main) gives the keynote address on the topic “Science in transition – the new challenges”. Subsequently, the festive award ceremony takes place. After the break, scientists from the Frankfurt UAS present their research projects in the entertaining Pecha Kucha format. The event concludes with a panel discussion under the motto “Vision, Success, Appreciation – Applied Research Makes People Happy”. In addition, in the form of a poster exhibition “Research for the practice – 10 years of research campaign by HAW Hessen and HMWK” projects from ten years of research funding for the Hessian HAW are presented. We were invited to join to event as an active research contributor. We are going to present our latest results the Hessian funded research project AVARTIM toward Advanced Visual Analytical Reasoning for Technology and Innovation Management (please also note our summary poster) .

Pre-Conference Workshop “Automated data analysis as a tool of TIE research”

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This year we get the opportunity to give a keynote speech at the pre-conference workshop on “Automated data analysis as a tool of TIE research” (original German title: “Automated Datenanalyse als Werkzeug der TIE Forschung”). The talk will be about “Visual Analytics for early technology detection”. The TIE conference has always had a permanent place in the calendar of leading German and German-speaking scientists in the fields of technology and innovation management as well as entrepreneurship.

14th International Symposium on Visual Computing (ISVC 2019)

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We are attending the 14th International Symposium on Visual Computing (ISVC 2019) and presenting our accepted paper titled “A Visual Analytics Approach for Analyzing Technological Trends in Technology and Innovation Management”. More information about our paper and the conference you will find in the news. The program is available on the conference website

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.

OpenRheinMain Conference 2019

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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 to interlink researchers and industrial partner of the Rhein Main region. Therefore, the conference considers stakeholders from both in an appropriate proportion. At the conference we will present our insights to “Visual Text Analytics for Technology and Innovation Management”.

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