Knowledge management techniques have been long used to address heterogeneity, even though they generally lack the capability of managing dynamic data flows in decentralized settings. Although the area of stream reasoning has advanced towards continuous processing of semantic streams in the last decade , there is an important research gap regarding decentralized reasoning over data streams, and even more so for organization and cooperation mechanisms among different stream processors. Even though there have been recent efforts towards semantization of streaming data in the form of dynamic Knowledge Graphs, current models and implemented systems lack the ability to manage and reason over rapidly changing knowledge in a highly distributed and fully decentralized environment. Furthermore, stream reasoners can hardly be combined in order to share computing duties or aggregate streaming data results, given the diversity of their underlying processing models, and their lack of orchestration capabilities.
This project addresses these challenges, by proposing the theoretical and technological foundations of an approach for decentralized processing of streaming knowledge graphs, where autonomous reasoners may combine individual and collective processing of continuous data. These decentralized stream processors shall be capable of sharing not only data stream knowledge, but also processing duties, using collaboration and negotiation protocols. Moreover, commonly agreed semantic vocabularies will be used to address the high dynamicity of reasoners’ knowledge and goals. The approach proposed in this project goes beyond previous works on stream reasoning, enabling the self-organization and coordination among distributed stream reasoners, based on techniques and principles inspired by Multi-Agent systems. On the one hand, it adds the ability to explicate processing goals, capabilities and knowledge, while on the other it exploits potential ways of interconnecting them in ways that expand their combined capacity/efficacy for managing highly dynamic flows of streaming knowledge. Through this approach, efficient local stream processors on the edge can establish cooperative processing schemes, respecting data privacy restrictions and data locality requirements through the exchange of streaming Knowledge Graphs.