@inproceedings{221053,

title = {Autonomous convergence mechanisms for collaborative crowd-sourced data-modeling},

author = {Christian L\"{u}bben and Marc-Oliver Pahl},

url = {http://XXXXX/221053.pdf},

year  = {2022},

date = {2022-04-01},

booktitle = {NOMS 2022 - Full and short papers ()},

abstract = {Interoperability remains a central challenge of the Internet of Things (IoT). Standardized data representation can solve this problem. Data model convergence prevents redundancy and fosters reuse. The growth of the IoT demands a high number of data models. Collaborative approaches allow the creation of numerous data models. The question to investigate is: Can assisted distributed model creation improve model convergence? This paper presents an approach to unify IoT data models during creation. It analyzes existing models to find similarities to a new model candidate. Similar models shall be reused or extended to prevent information redundancy. Challenges are the accuracy of the similarity analysis and scalability. The evaluation shows linear scalability and high accuracy using a data set containing 1200 automatically converted data models from today's most relevant IoT data modeling initiatives: Project Haystack, IoTSchema, and BrickSchema.},

keywords = {Internet of Things (IoT); Data service management; IT service management; Distributed management},

pubstate = {published},

tppubtype = {inproceedings}

}