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Simplity Talks Again at Renowned IEEE Big Data Conference

Graham Needham
January 6, 2022 | 4.5 min read
The 2021 IEEE International Conference on Big Data was held December 15-19 and Simplity contributed with two guest speakers, Torsten Priebe and Stefan Markus. We asked them a few questions about their “Finding Your Way through the Jungle of Big Data Architectures” paper which was presented during the Big Data Infrastructure/Management session.

Torsten Priebe is Head of Data Intelligence Research Group at St. Pölten University of Applied Sciences and also Simplity’s Chief Technology Officer. Stefan Markus is Head of Professional Services and Managing Director of Simplity AT.

In December 2021 Simplity was presenting at the renowned IEEE Big Data Conference. Can you tell us a bit about this conference?

Torsten Priebe: The IEEE International Conference on Big Data is one of the largest research conferences in the data and analytics field with more than a thousand participants from all over the world every year. It is run by the Computer Society of the Institute of Electrical and Electronics Engineers (IEEE). Usually only 17-19% of the submitted contributions are accepted for presentation, which indicates the high quality of the presentations. The conference takes place annually in different places across the United States. Simplity contributed in 2015 at Santa Clara, California, presenting our Business Data Modelling methodology. This year the planned venue was in Orlando, Florida, but due to the COVID-19 situation it was transformed to an online event.

What were the “hot topics” covered in this year’s conference?

Torsten Priebe: Besides the regular research papers, tutorials, and poster presentations there were a number of keynote speeches that probably indicate the current trends of the industry. For example, Jian Pei from the Simon Fraser University in Canada talked about how data science and artificial intelligence (AI) can be made more trustworthy. With more and more deep learning and neural network based solutions, which are basically a black box, it becomes increasingly difficult to explain how AI models come to their decisions. Volker Markl from the German Research Center for Artificial Intelligence (DFKI) presented the challenges around combining different data processing and advanced analytics capabilities as well as various data storage technologies in common easy-to-use infrastructures.

Isn’t Simplity’s contribution going in the same direction?

Stefan Markus: Yes, exactly. In our paper “Finding Your Way through the Jungle of Big Data Architectures” we reviewed the state-of-the-art for the various analytical data architectures that have been proposed over the recent years, representing them in a structured form, and hence making them more comparable as well as identifying the overlaps and dependencies. In order to be formally sound, we used the DAMA-DMBOK and ArchiMate in our representation of architecture paradigms such as Logical Data Warehouse, Data Fabric and Data Mesh.

This sounds very interesting from a scientific perspective, but how could practitioners such as Simplity’s customers benefit from this work?

Stefan Markus: Reviewing and structuring the different data architecture approaches was of course only the first step. We are currently extending this work towards something like a “pattern system” as known from software patterns in software engineering. This means using a common template with among others “context”, “problem” and “solution” descriptions to provide guidance for choosing the right architecture paradigm for the right business situation. We also want to identify the common building blocks of those architectures as a combination of different paradigms which may, in fact, be the most suitable approach rather than sticking to a single proposal.

The 2021 IEEE International Conference on Big Data was a great success and you can catch up with Torsten in his conference video on our new YouTube channel. You can view/download the paper in PDF format via this link.

Graham Needham
Content Editor