A5031786598- Data Quality and Management
- Privacy-Preserving Technologies in Data
- Cloud Computing and Resource Management
- Big Data and Business Intelligence
- Information Systems Theories and Implementation
- Cloud Data Security Solutions
- Software System Performance and Reliability
- Service and Product Innovation
- Technology Adoption and User Behaviour
Technische Universität Berlin
2024
IBM (Germany)
2024
Karlsruhe Institute of Technology
2022
With the increasing importance of data and artificial intelligence, organizations strive to become more data-driven. However, current architectures are not necessarily designed keep up with scale scope analytics use cases. In fact, existing often fail deliver promised value associated them. Data mesh is a socio-technical, decentralized, distributed concept for enterprise management. As still novel, it lacks empirical insights from field. Specifically, an understanding motivational factors...
Smart physical products increasingly shape a connected world and serve as boundary objects for the formation of ‘smart service systems’. While these systems bear potential to co-create value between partners in various industries, IS research still struggles fully capture phenomenon support successful digital innovation IoT settings. In our work, we analyze smart taking an affordance-actualization perspective. Based on qualitative content analysis multi-case study, identify elements...
Performance modeling for large-scale data analytics workloads can improve the efficiency of cluster resource allocations and job scheduling. However, performance these is influenced by numerous factors, such as inputs assigned resources. As a result, models require significant amounts training data. This be obtained exchanging runtime metrics between collaborating organizations. Yet, not all organizations may inclined to publicly disclose metadata. We present privacy-preserving approach...
Performance modeling for large-scale data analytics workloads can improve the efficiency of cluster resource allocations and job scheduling. However, performance these is influenced by numerous factors, such as inputs assigned resources. As a result, models require significant amounts training data. This be obtained exchanging runtime metrics between collaborating organizations. Yet, not all organizations may inclined to publicly disclose metadata.