**6.3. Personalised decision support tools**

306 Risk Management – Current Issues and Challenges

**6.2. Real-time risk detection tools** 

inconceivable.

The use of ontologies is related to the introduction of intelligence and reasoning on the information processing and the relation established between concepts that increase the relevance of such intelligence. Furthermore, this structures will be used for mapping data with information systems of the own company or potential external information systems [31]. The ontologies are considered therefore as the bridge between the heterogeneous

Inherent to the concept of proactive prevention is the aim of the system to predict a particular risk by evaluating all the variables of the worker, the working place and the environment. For being able to perform such evaluation the first requirement is to have tools that are able to process all the information in a complete, efficient and, at the same time, very light way. Currently, risk management is focused on monitoring the proposed actions after an evaluation process is periodically; e.g. yearly, performed in the companies. With nowadays facilities, there is no way to propose a 'real time' evaluation of the situation, due to the fact that the safety manager cannot control all the time what is happening in the shop-floor, the data of all the machines involved in the process and the information referred to the state of workers that are moving around the factory. This is

**Figure 13.** Continuous real-time risk management based on Complex Event Processing Technology

information ecosystem and the actual services implementing the FASyS system logic.

As it has become apparent from the previous sections FASyS provides the tools and models for being able to process as much as information in less time as possible. Thus, the enterprise safety and healthy manager can work with relevant information to make informed decisions. The aim of FASyS personalised decision support tools is not just to warn and make apparent a particular risk level but also to ease the decision process based on strong knowledge support. FASyS Monitoring and Control Human Macine Interface (HMI) has therefore being designed to provide highly visual interfaces about risk levels. Moreover, the system also makes suggestions of the most suitable procedures to be aplied when a risk situation is detected, so that the reaction time can be hugely reduced and the user can send a highly effective execution action plan immediately.

The decision support system works with risk patterns that require a human interaction, either to provide additional information or to select preferred option in front of a multiple selection.

### **6.4. Semantic solutions for services coordination**

In the context of the sensing enterprise, FASyS has to deal not only with the detection of risks but also has to support the actuation and deployment of the preventive actions selected by the safety and healthy manager through the personalised decision suppport tools. This implies that FASyS has envisaged a service oriented scenario, where the factory is populated by a large amount of services that exchange messages and perform

are coregraphed or orchestrated to perform the designed actions by means of smart objects.

Integrated and Personalised Risk Management in the Sensing Enterprise 309

**Figure 15.** FASyS processes choreographer

This paper has presented the rationale behind the development of a risk management framework for personalised risk management in the context of the sensing enterprise. The paper has presented the main dimensions proposed for the model and it has presented the

The paper has introduced the reference architecture and it has argued how this reference architecture is in complete alignment with European IoT movement currently under development. Moreover, the paper has provided evidence in terms of how the FASyS system is capable of providing a personalized and intelligent management of all the factors related, directly or indirectly, to the worker and its environment, in order to identify and detect warning situations, alerts and propose immediate actions required upon a worker, a

Agustín Moyano, Mikel Uriarte, Óscar López Etxahun Sánchez and Saioa Ros

Teresa Meneu, Juan Carlos Fernández-Llatas and Vicente Traver *Soluciones Tecnológicas para la Salud y el Bienestar (TSB), Spain* 

**7. Conclusions** 

machine or an area.

**Author details** 

*Nextel S.A., Spain* 

Óscar Lázaro and Alicia González

Benjamín Molina and Carlos Palau

*Universitat Politècnica de Valencia (UPV), Spain* 

*Innovalia Association, Spain* 

main technical components.

Therefore, in the FASyS platform, there is a huge amount of available services involved in risk management life cycle. In addition, those services have heterogeneous sources; they can become available, temporarely unavailable or even disappear suddenly; the availability of them can change anytime. In order to solve these situations, FASyS has proposed a highly effective service messaging and service management and coordination semantic solution that would use choreography techniques focused on browsing FASyS service topology [33], which is made using an ontology definition; e.g. through WSDL or USDL descriptions. With this solution, FASyS is able to adapt its reactions to available services at any time and ensure the best possible service performance based on the precedence of the risk to be addressed and the service load in the enterprise bus. Clustering techniques allow for optimum selection of services to be orchestrated otr coreographed to servce a particular aplication in the prevention workflow. Those services could be previously known or even configured at run-time.

All of FASyS technological developments and systems have a semantic service library that would ensure the availability of its own functionalities to the rest of systems. The access to those functionalities will be assured continuously [34].

**Figure 14.** FASyS Monitoring and Control Human-Machine Interface (HMI)

Integrated and Personalised Risk Management in the Sensing Enterprise 309

**Figure 15.** FASyS processes choreographer
