Fresh Thinking Talks

What You Say is What You Get: Hands-Free Coding in 2023

Abstract: Software for interpreting and synthesizing natural language is used by millions of people every day who use smart home assistants or simply prefer dictating over typing on their mobile phones. But while hands-free interfaces have found widespread adoption among consumers, IT professionals still mostly consider them gimmicks or do not consider them at all for the purpose of software development – unjustly so! 

In this presentation, you will learn how to talk your computer into writing good software. We will start with basic controls and navigation before addressing how to code using just your voice (and eyes and facial expressions and more!), covering best practices and common pitfalls along the way. I will then share my personal experiences as a user, but also relate them to ongoing research and current developments in the industry. The talk will close with recommendations for getting started and a zero-cost setup for hands-free coding that you can use right away.

Wolfram Wingerath (University of Oldenburg)
Wolfram "Wolle" Wingerath is junior professor for data science at the University of Oldenburg, Germany. Wolle did his PhD on real-time databases at the University of Hamburg in 2019, before he was responsible for all matters relating to the analysis and continuous processing of data as head of data engineering and research at the company Baqend. Having used speech recognition tools for software development since 2011, Wolle has more than a decade of experience in the context of Handsfree Coding. With the eponymous GI initiative (handsfree-coding.gi.de), he aims to increase the visibility of hands-free coding within the computer science community and establish it as a means to increase efficiency as well as reduce barriers. Since Wolle likes to meet new faces and is always keen to exchange ideas with others, he is a regular speaker at developer and research conferences to present the things he is passionate about.

Between Data Lakes and Research Data Management – Data Engineering Tasks for the Next Decade

Abstract: "Data is the new gold" is the promise with which many Data Science initiatives have been launched. We all know the hurdles on the way to derive insights from data. In reality, we often face a heterogeneous data landscape, which is appropriately described by the name "data lake". The current initiatives in research data management address this difficulty, the large funding program NFDI aims to standardize data and processes in the various scientific fields. 

If such data standards exist in the future, several follow-up tasks for data engineering scientists exist. So what steps must be taken to translate heterogeneous, noisy, sometimes incomplete data into analysable research data? What does data engineering have to do for this? Which (of the often already known) methods can be used and how do we have to adapt them? And: why exactly can NFDI become a game changer? Some of the techniques that will be used are: monitoring of data preprocessing workflows, data cleaning, data transformation, data integration, metrics for evaluating data quality and automatic predictions based on collaborative filtering.

The talk will be almost exclusively about future work, about ideas, plans and possibilities for future joint development.

Meike Klettke (University of Regensburg)
Meike Klettke is professor for Data Engineering at the University of Regensburg. Before, she studied computer science at the University of Rostock, Germany, where she also received her doctorate and habilitation. Since April 2022, she is leading the Data Engineering group of the new founded Faculty for Computer Science and Data Science at the University of Regensburg. 

Her general research interests are all abstractions over data. Specific research tasks derived from this are the evolution of databases and database reverse engineering (schema inference and the extraction of semantic constraints from data). The current research topics of her group are data engineering pipelines for Data Science, in particular self-adaptive data engineering methods and processes, methods for automatic data curation, and the evaluation of data engineering processes.