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2nd Project Review in E2COMATION

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Last week we had our 2nd project review in the now three-year ongoing European research project E2COMATION. As usual in such projects, the last view month required a lot of attention to complete the last work actions, deliverables, and of course the administrative reporting. And now it was to show the achievements to the EU project officer and the reviewers.

After the preparation day, we had a full day to show all our achievements and answer questions. As might be noted, in such a big project with big industrial actors as use-cases there were a lot of challenges to be solved and also alternative strategies and solutions to be elaborated.

As Software AG we are responsible for the two tasks. The first was about real-time analytics together with our solution APAMA, which my colleague presented. The second part was presenting the self-analytics results for what we use TrendMiner and this was on me. The goal of this task is to give production workers, who are not data scientists, the ability to analyze IoT data from the shopfloor and identify problems, solutions, and optimizations. Together with some principle architectural information I also gave a demo to show how the solution is practically used in the use cases.

E2COMATION Year-end Race

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The final steps in the race to the end of the year in the E2COMATION project are underway and the major milestones have been reached. Time to take a deep breath and review the results of the project.

I started working on the E2COMATION project about 1.5 years ago and since then there have been many challenges to overcome, first and foremost a common understanding of how all the technologies paired with the know-how behind them must ultimately work together. It started with the first executable individual small demos, then extended demos, then the first live connections, and now there are finished prototypes in the project. It is always exciting to see how the work, especially in the final phases, increasingly comes together to form an overall picture and how the functional possibilities increase exponentially.

I am particularly pleased to see how much Software AG‘s technologies, especially APAMA and Trendminer, create great benefits here. This is always getting very clear when application partners use the technologies and approach you with specific feature questions. You can see that the technologies are in practical use and now it is time to turn the technology functions into production results. Which is also the outlook for the coming year in the project.

I would like to take this opportunity to wish the entire E2COMATION team a peaceful Christmas season and a happy New Year.

3rd Year E2COMATION Project Meeting in Milan

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Last week we had our 3rd year E2COMATION project meeting in Milano. The focus laid on finalizing the implementation in the four use cases. Meanwhile, the SAG’s self-analytics solution Trendminer is used in the three use-cases KEAS, MAHOU, and ROYO to have a sensorial overview of what’s happened at the production, but also to identify possible opportunities to optimize the production and to identify anomalies, which might be solved, corrected or at least send warnings automatically with the SAG’s real-time analytics solution APAMA.

As a significant milestone, Trendminer is already fully connected and configured to consume the KEAS plant data, where we already have started discussing and preparing analysis views for certain behaviors that require some optimization and cause analysis. It is impressive to see how a graphical tool like Trendminer with simplified user-friendly analytical functions could help to understand certain behaviors at the plant and in the IoT date and find potential optimization strategies together with the domain experts of the company.

E2COMATION Self-Analytics Platform with TrendMiner

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As a response to our E2COMATION project’s masterclass workshop at hub.berlin last week, I wanted to share some shown features of TrendMiner to analyze IoT data for Smart Manufacturing. As a tool that is also designed for non-data scientists, it gives a lot of analytics capabilities to production experts and engineers and simplifies the use of production data on a practical level.

For more information about TrendMiner analytics platform, please visit: https://www.trendminer.com.
For more information about the E2COMATION project, please visit: https://e2comation.eu.

Book Chapter published in Student Handbook “Praxishandbuch Forschungsdatenmanagement”

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We are glad to announce that our chapter to the student handbook “Praxishandbuch Forschungsdatenmanagement” (Engl.: practice handbook research data management) was accepted and got printed at De Gruyter. Our chapter addresses the foundations of Data Visualization, which is explained on behalf of selected examples.

The book covers nowadays societal research challenges in pespective of data management and how current approaches in research can helo to handle it. Therewith, events such as the entry into force of the code “Guidelines for Safeguarding Good Scientific Practice” of the German Research Foundation (DFG) or the establishment of the National Research Data Infrastructure (NFDI) and the European Open Science Cloud (EOSC) put providers, producers and users of research data in front of specialist, technical, legal and organizational challenges. The practical handbook for research data management comprehensively covers all relevant aspects of research data management and the current framework conditions in the data ecosystem.

In particular, the practical implications of data policy and law, the respective data market, data culture, personal qualification, data management and “FAIR” data transfer and reuse are examined. The practical handbook also provides an overview of projects, developments and challenges in Research data management.

The post Book Chapter published in Student Handbook “Praxishandbuch Forschungsdatenmanagement” appeared first on Human-Computer Interaction & Visual Analyitics Reasearch Group (vis) at Darmstadt University of Applied Sciences (h_da).

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.

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).