Hybrid approach for selective delivery of information streams in data-intensive monitoring systems. Issue 3 (August 2016) - Evchina, Yulia; Martinez Lastra, Jose Luis
Abstract: Background: Present day control and monitoring systems are equipped with a large number of heterogeneous devices and are operated by many users with different roles and responsibilities. The information generated by these devices, although preprocessed and filtered, is usually delivered to users regardless of their actual information needs, thus overwhelming cognitive capacities and potentially affecting safety of the system. Objectives: This work aims to reduce information load to the users of the data-intensive monitoring systems by delivering selected information to each user based on his/her roles in the system and responsibilities. Methods: The proposed approach combines Semantic Web (SW) technologies and Complex Event Processing (CEP) for configuration purposes and run-time analyzing. The approach is exemplified with implemented tools and feasibility study based on the performance tests. The paper describes principles of proposed approach, demonstrates illustrative scenarios from building automation domain, gives description of implemented tools, and presents results of the initial performance tests. Results: The combination of SW and CEP brings two major advantages: (1) the behavior of the system could be easily changed by configuring only underlying ontology and (2) utilization of CEP at runtime makes system event-driven and reactive to frequent changes in the environment. The performance tests demonstrated the response time of implemented tools within one second for 1000 updates per second (which corresponded to 10, 000 devices in the performed experiments). Conclusions: It is expected that the proposed approach is able to make monitoring systems personal oriented and thus safer during the operation. The results of the performance tests suggest feasibility of the approach for such systems as building and home automation, and non-critical industrial automation.