@ARTICLE{10.21494/ISTE.OP.2021.0684, 

TITLE={Towards Measurable Efficient and Effective Metrics for Quality and Cost of Context}, 
  
AUTHOR={Kanaka Sai Jagarlamudi, Arkady Zaslavsky, Seng W. Loke, Alireza Hassani, Alexey Medvedev, }, 
	
JOURNAL={Modeling and Using Context}, 
	
VOLUME={4}, 

NUMBER={CONTEXT-21 Special Issue}, 
	
YEAR={2021}, 

URL={https://openscience.fr/Towards-Measurable-Efficient-and-Effective-Metrics-for-Quality-and-Cost-of}, 
	
DOI={10.21494/ISTE.OP.2021.0684}, 
	
ISSN={2514-5711}, 
	
ABSTRACT={Despite the potential benefits of the context-driven intelligence delivered by Context Management Platforms (CMP), the lack of efficient and effective metrics for measuring Quality and Cost of Context (QoC and CoC) hinders them from uptake and commercialisation. Furthermore, the CMPs might have limited abilities to incorporate efficient QoC drivers and a suboptimal selection of QoC-aware context providers. This paper proposes QoC and CoC metrics and introduces a conceptual architecture to achieve the QoC and CoC awareness in CMPs, aiming to improve their efficiency and consumer experience.}}