## 6 Conclusions  
In this article, we presented a CA middleware system that integrates context modeling and reasoning techniques. First, an adaptation and combination of the most prominent context categorization schemata was proposed, which formed the basis of the context model. The proposed model includes five core classes, namely, thing, location, time, activity and asserted context. A hybrid reasoning schema, which combines multiple techniques, was also proposed. Each individual reasoning technique was applied to solve problems that it is more suitable for, providing an efficient reasoning procedure that can output all types of inferences.  
In order to be assessed with respect to representation, reasoning veracity and performance, the proposed middleware system was employed in a cultural space, with the purpose of facilitating preventive conservation of artifacts. The evaluation results showed that each criterion is satisfied, that the system can accurately represent all required context and that the integrated reasoning procedure can efficiently address various types of contextual emergencies. Additionally, the performance tests showed a linear correlation between response time and volume of things, which proves the scalability of the proposed middleware.  
In the future, the proposed middleware and the underlying modeling and reasoning techniques will be employed in more activities inside cultural spaces, i.e., recommendation systems, personalized visits, management and optimization of exhibitions. Thus, the efficiency of building multiple CA applications on top of an existing middleware will be evaluated, analyzing the system performance and ability to address multiple context stakeholders and manage their possible conflicting requirements. Furthermore, since the current work does not address connectivity and network performance, such open research questions will be tackled in future work. More specifically, future evaluation of the proposed middleware will perform the testing of network performance metrics, such as latency and overload, including a comparison between the proposed KB installation (mongoDB cloud) and other solutions. In addition, advanced connectivity open research questions will be researched, including the automatic discovery, registration and configuration of sensors and smart objects inside a cultural ecosystem.  
Figure 12: Correlation between inference cycle time and number of things.  
**Author Contributions:** Conceptualization, K.M., Y.C. and G.C.; methodology, K.M. and Y.C.; software, K.M., G.C. and Y.V.; validation, K.M., Y.V. and P.M.; formal analysis, Y.C., G.C. and P.M.; investigation, G.C., Y.V. and P.M.; resources, Y.C.; data curation, Y.V. and P.M.; writing--original draft preparation, K.M., Y.C. and Y.V.; writing--review and editing, G.C. and P.M.; visualization, K.M.; supervision, G.C.; project administration, P.M.; funding acquisition, P.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was co-financed by the European Union and Greek national funds through the Competitiveness, Entrepreneurship and Innovation Operational Programme, under the Call \"Research-Create-Innovate\"; project title: \"Development of technologies and methods for cultural inventory data interoperability--ANTIKLEIA\"; project code: T1 EDK-01728; MIS code: 5030954.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.