OpenRheinMain Conference 2019

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”.

Thesis Presentation: Contrasted Data from Science and Web for Advanced Visual Trend Analytics

Where: TU Darmstadt / GRIS, Fraunhoferstr. 5, Room tba
Who: Rehman Ahmed Abdul (Author), Prof. Dr. Arjan Kuijper (Supervisor), Dipl.-Inf. Dirk Burkhardt (Advisor/Co-Supervisor)
What: Master Thesis – “Contrasted Data from Science and Web for Advanced Visual Trend Analytics”


With more publicly accessible digital libraries accessible, a plethora of digital research data is now available for gaining insights into actual and upcoming technology trends. These trends are essential to researchers, business analysts, and decision-makers for making strategic decisions and setting strategic goals. Appropriate processing and graphical analysis methods are required in order to extract meaningful information from the data. In particular, the combination of data mining approaches together with visual analytics leads to real beneficial applications to support decision making in e.g. innovation or technology management.
The data from digital libraries is only limited to research and overlooks the market aspects e.g if the trend is not important for key business players, it is irrelevant for the market. This importance of market aspects creates a demand for validation approaches based on market data. Most of the current market data can be found publically on websites and social networks, e.g. as news from enterprises or on tech review sites or on tech blogs. Therefore, it makes sense to consider this public and social media data as contrasting data to the research digital library data that can be used to validate technology trends.
The goal of this thesis is to enable trend analysis on public and social web data and compare it with retrieved trends based on research library data to enable validation of trends. To achieve this goal a model is proposed that acquires public/social web and digital library data based on user-defined scope called a “campaign”, which is then visually transformed from raw data into interactive visualizations passing through different stages of data management, enrichment, transformation, and visual mapping. These interactive visualizations can either be used in insight analysis to gain trend insights for an individual data source or they can be used in comparative analysis with the goal of validating trends from two contrasting data sources.

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

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


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.