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

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.

The post Thesis Presentation: Named-Entity Recognition on Publications and Raw-Text for Meticulous Insight at Visual Trend Analytics appeared first on Human-Computer Interaction & Visual Analyitics Reasearch Group (vis) at Darmstadt University of Applied Sciences (h_da).

Advanced Seminar toward Visual Analaytics in next Summer Semester at h_da/FBI (computer science)

In the upcoming summer semester 2020, our team provides an advanced seminar at the department of computer science at the Darmstadt University of Applied Sciences. The topic of the seminar will be toward “Visual Analytics for Smart Manufacturing and Trend Analysis”:

Due to the increasing digitization, analyzes are a constant necessity, which also applies to the identification of errors, problems or opportunities for improvement. With visual analytics, the combination of algorithmic and massive calculation opportunities of computer-based systems on the one side as well as the visual and cognitive skills of the users on the other side are used together. The seminar focuses on the use of visual analytics in the two current areas of smart manufacturing and trend analysis. On behalf of the given task, current approaches, methods and implementations should be researched and examined. The goal should be a scientific elaboration based on empirical and scientific standards.

Interested students are invited to register soon, since the number of places will be limited.

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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.

 

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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

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

I’m proud to announce that our paper titled “A Visual Analytics Approach for Analyzing Technological Trends in Technology and Innovation Management” is accepted at the high-class visual computing conference ISVC 2019 in Lake Tahoe, NV, USA. The purpose of the International Symposium on Visual Computing (ISVC) is to provide a common 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

 

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Best Paper Award at the iV 2019

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” will be given to the studies which will be selected by the committee among the papers/posters 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.

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.

Kickoff of the National AVARTIM Project

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.

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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 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.

Presentation of Two Accepted Papers at VARE 2018

At the current conference in Virtual and Augmented Reality in Education (VARE 2018), we are happy to present two of our accepted interesting papers. The conference takes place from Sep. 17, 2018 to Sep. 21, 2018 in Budapest, Hungary. The VARE is a biannual event that brings together trainers in all areas of knowledge and educational levels as well as researchers and scientists from virtual technologies as well data advanced visualization and virtualization. In general, main aim is to improve teaching and training, and data analysis through virtual and augmented technologies use and to discuss problems and solutions in mentioned areas to identify new issues, and to shape future directions for research. The accepted papers are:

#1 Juxtaposing Visual Layouts – An Approach for Solving Analytical and Exploratory Tasks through Arranging Visual Interfaces

Interactive visualization and visual analytics systems enables solving a variety of tasks. Starting with simple search tasks for outliers, anomalies etc. in data to analytical comparison

s, information visualizations may lead to a faster and more precise solving of tasks. There exist a variety of methods to support users in the process of task solving, e.g. superimposing, juxtaposing or partitioning complex visual structures. Commonly all these methods make use of a single data source that is visualized at the same time. We propose in this paper an approach that goes beyond the established methods and enables visualizing different databases, data-sets and sub-sets of data with juxtaposed visual interfaces. Our approach should be seen as an expandable method. Our main contributions are an in-depth analysis of visual task models and an approach for juxtaposing visual layouts as visual interfaces to enable solving complex tasks.

#2 Visualizing Law – A Norm-Graph Visualization Approach based on Semantic Legal Data

Laws or in general legal documents regulate a wide range of our daily life and also define the borders of business models and commercial services.

However, legal text and laws are almost hard to understand. From other domains it is already known that visualizations can help understanding

complex aspects easier. In fact, in this paper we introduce a new approach to visualize legal texts in a Norm-graph visualization. In the developed Norm-graph visualization it is possible to show major aspects of laws and make it easier for users to understand it. The Norm-graph is based on semantic legal data, a so called Legal-Concept-Ontology.

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