Towards Highly Performant Context Awareness in the Internet of Things

Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/GutachtenBeitrag in KonferenzbandBeigetragenBegutachtung

Abstract

Context awareness and adaption are an important requirement for data streaming applications in the Internet of Things (IoT). Concept drift arises under changing context and many solutions for drift detection and system adaption have been developed in the past. Even though the computing resources of these approaches are not negligible, performance aspects, e.g. runtime or memory usage and scalability have not been investigated adequately. The goal of this thesis is to fill this gap and to provide a mean for performance investigations of concept drift handling methods and other approaches that are developed in the research field Data Science. Therefore performance benchmarking of state-of-the-art solutions is conducted. Moreover the thesis discusses performance bottlenecks of the approaches and demonstrates possible improvements based on the knowledge gained by leveraging tools and methods from the performance analysis domain. Approaches are then implemented into an IoT application and deployed with stream processing engines for a final evaluation.

Details

OriginalspracheEnglisch
TitelDistributed Computing and Artificial Intelligence, Special Sessions, 19th International Conference
Redakteure/-innenJosé Manuel Machado, Pablo Chamoso, Guillermo Hernández, Grzegorz Bocewicz, Roussanka Loukanova, Roussanka Loukanova, Esteban Jove, Angel Martin del Rey, Michela Ricca
Herausgeber (Verlag)Springer Link
Seiten165-170
Seitenumfang6
Band583
PublikationsstatusVeröffentlicht - Feb. 2023
Peer-Review-StatusJa

Externe IDs

Scopus 85149683892
Mendeley 8558def1-e3fa-35ae-9662-92aa8ac4b7db

Schlagworte

Schlagwörter

  • Concept drift detection, Performance analysis, IoT