Keynote auf Business Data Analytics (BDA’18) Workshop in Estland

Universität Ulm

Manfred Reichert war eingeladener Keynote-Sprecher des 1st International Workshop on Business Data Analytics (BDA'18), der im Rahmen der 30th International Conference on Advanced Information Systems Engineering (CAiSE'18) im Juni 2018 in Tallinn, Estland stattfand.

In seinem Vortrag ging Manfred auf verschiedene DBIS-Projekte ein, die sich mit der Sammlung von großen Datenmengen in mobilen und verteilten Umgebungen befassen. Hierzu demonstrierte er einerseits von DBIS entwickelte Technologien für die mobile Erfassung, Verarbeitung und Speicherung von Daten sowie für weltweites Mobile Crowdsensing. Andererseits setzte er sich mit komplexen Datenerfassungsszenarien in Lieferketten und Produktionsumgebungen auseinander.  

Manfred Reichert:
Process-Driven Data Collection in Mobile and Distributed Environments: Challenges, Methods, Technologies Keynote des BDA’18 Workshops im Rahmen der CAiSE’18 Fachtagung, Tallinn, Estland, 11. Juni 2018


Zusammenfassung (in Englisch):

Data collection is the process of gathering and measuring information on targeted variables in a systematic manner, which then shall enable researchers to answer specific questions and to evaluate outcomes. Regardless of the field of study, accurate and honest data collection is crucial for maintaining the integrity of research. Both the selection of appropriate data collection instruments and clearly delineated instructions for their correct use (i.e. workflows) are essential. Due to the emergence of smart mobile devices, in addition, mobile crowdsensing has become an appealing method to collect data in the large scale. Finally, data collection increasingly draws on sensor data available through the Internet of Things. The goal for all kinds of data collection is to capture quality evidence such that data analyses lead to convincing and credible answers to the respective research questions. This keynote presentation deals with sophisticated data collection processes and data analysis scenarios from the real world (e.g., healthcare, Industry 4.0, and sustainability). It discusses characteristic challenges of these real-world applications and gives insights into selected technologies and methods (e.g., process-driven data collection instruments, mobile crowdsensing) for the support of advanced data collection processes.

Sunset over Tallinn by Ilya Khamushkin (CC BY-SA 2.0)