Context-aware workflow management in eHealth applications

Workflows are a technology to structure work in functional, non-overlapping steps. They define not only the order of execution of the steps, and describe whether steps are executed in parallel, they also specify who or what tool has to fulfill which step. Workflows offer the possibility to automate work, to increase the understandability of processes, and they ease the control of process execution. The tools to manage workflows, so called workflow management systems (WfMSs), are traditionally rigid as they separate workflow definition done at build time from workflow execution done at run time. This makes them ill-suited for managing flexible and unstructured workflows. In this thesis, we focus on the support of flexible processes in eHealth, which are affected by more foreseen than unforeseen events. To bridge the gap between rigid WfMSs and flexible workflows, we developed a concept for dynamic and context-aware workflow management called Flexwoman. Although our focus lies on flexible eHealth processes, Flexwoman is a generic approach that can be applied to several different application domains. Flexwoman supports the usage of context information to adapt processes automatically at run time to foreseen events. Processes can also be manually adapted to handle unforeseen events. To achieve this flexibility, context information from different sensors is unified and thus can be analyzed in the same way. The analysis and adaptation of workflows is executed with a rule engine. A rule engine can store, reason about and apply knowledge automatically and efficiently. Rules and application logic are separated, thus, rules can be changed during run time without affecting application logic or process description. Workflows are internally described by Hierarchical Colored Petri nets (HCPNs) and executed by a HCPN execution engine. HCPNs allow for a deterministic execution of workflows and can represent workflows on different levels of detail. In summary, in Flexwoman, significant context changes (events) trigger automated adaptations that replace parts of the workflow by sub workflows, which can in turn be adapted. The adaptations and the rules for context-aware adaptation are saved in the organizational memory for later reuse. Flexwoman’s event based behavior facilitates proactive adaptations instead of only allowing for adaptations while entering or leaving a task. Replacements are not bound to special places defined at build time but each part of the workflow, which has not been executed yet, can be replaced at run time. We implemented and evaluated the concept. The evaluations show i) that all required functionality is available, ii) that the system scales with a growing number of rules, and iii) that the system correctly handles failure situations

Implementation of context-aware workflows with Multi-agent Systems

Systems in Ambient Intelligence (AmI) need to manage workflows that represent users’ activities. These workflows can be quite complex, as they may involve multiple participants, both physical and computational, playing different roles. Their execution implies monitoring the development of the activities in the environment, and taking the necessary actions for them and the workflow to reach a certain end. The context-aware approach supports the development of these applications to cope with event processing and regarding information issues. Modeling the actors in these context-aware workflows, where complex decisions and interactions must be considered, can be achieved with multi-agent systems. Agents are autonomous entities with sophisticated and flexible behaviors, which are able to adapt to complex and evolving environments, and to collaborate to reach common goals. This work presents architectural patterns to integrate agents on top of an existing context-aware architecture. This allows an additional abstraction layer on top of context-aware systems, where knowledge management is performed by agents.This approach improves the flexibility of AmI systems and facilitates their design. A case study on guiding users in buildings to their meetings illustrates this approach

Characterization of Multi-agent systems: A systematic mapping study

Multi-agent systems have gained a lot of interest in articles, conferences and magazines. However, it is still a recent issue which is not understood or the scope of it is unknown. The objective of this work is to investigate in more detail the articles and to build a preliminary classification system to structure the field of Multi-agent systems. As a result, an overview of this area of research can be obtained through the response to the research questions posed. Resumen. Los sistemas multi-agente han ganado un interés muy grande en artículos, conferencias y revistas. Sin embargo sigue siendo un tema reciente el cual no se comprende ni se conoce los alcances del mismo. Este trabajo tiene como objetivo investigar con más detalle los artículos y construir un sistema de clasificación preliminar para estructurar el campo de los sistemas Multi-agente. Como resultado, se espera proporcionar una visión general de esta área de investigación a través de responder a las preguntas de investigación planteadas. Creemos que el presente trabajo será una herramienta que encontrará ámbitos todavía aun no investigados

Automatic generation of meta classifiers with large levels for distributed computing and networking

This paper is devoted to a case study of a new construction of classifiers. These classifiers are called automatically generated multi-level meta classifiers, AGMLMC. The construction combines diverse meta classifiers in a new way to create a unified system. This original construction can be generated automatically producing classifiers with large levels. Different meta classifiers are incorporated as low-level integral parts of another meta classifier at the top level. It is intended for the distributed computing and networking. The AGMLMC classifiers are unified classifiers with many parts that can operate in parallel. This make it easy to adopt them in distributed applications. This paper introduces new construction of classifiers and undertakes an experimental study of their performance. We look at a case study of their effectiveness in the special case of the detection and filtering of phishing emails. This is a possible important application area for such large and distributed classification systems. Our experiments investigate the effectiveness of combining diverse meta classifiers into one AGMLMC classifier in the case study of detection and filtering of phishing emails. The results show that new classifiers with large levels achieved better performance compared to the base classifiers and simple meta classifiers classifiers. This demonstrates that the new technique can be applied to increase the performance if diverse meta classifiers are included in the system

Stochastic petri-net models to predict the degradation of ceramic claddings

A stochastic Petri-net formalism is proposed to predict the degradation of ceramic claddings over time in order to understand how different environmental exposure conditions contribute to the overall degradation of these claddings. For that purpose, the degradation condition of 195 ceramic claddings located in Lisbon, Portugal, is evaluated through in situ visual inspections. In the first part of the study, a stochastic deterioration Petri-net model is proposed for the entire sample. In the second part, the original sample is divided according to the environmental exposure conditions, evaluating the influence of these conditions on the deterioration process of ceramic claddings. Four main degradation agents are analyzed: exposure to moisture; distance from the sea; orientation; and wind–rain action. The results reveal that Petri nets can accurately describe the deterioration process of ceramic claddings, providing relevant information regarding the performance of these claddings through their life cycle and according to the environmental exposure conditions to which they are subject. These results are extremely relevant for different practitioners: the approach allows the adoption of more sustainable and durable solutions at the design stage, as well as improving the durability of the ceramic claddings by performing optimized maintenance plans and strategies

Enhancing Smart City Services with AI: A Field Experiment in the Context of Industry 5.0

The practical effects of incorporating artificial intelligence (AI) into Industry 5.0 smart city services are made evident by this empirical research. The use of AI-powered smart traffic management yields a noteworthy 32.94% rise in traffic volume, signifying a noteworthy progression towards improved urban mobility. AI waste management optimization results in a 5.71% increase in collection efficiency, highlighting the importance of operational effectiveness and resource conservation. The control of energy use shows an 8.57% decrease, confirming AI's importance in sustainable energy practices. AI-enhanced public safety offers dependable event prediction, indicating safer cityscapes. These results highlight AI's revolutionary potential and establish smart cities as safe, secure, and sustainable urban environments

Innovations in Smart Manufacturing: An Experimental Assessment of Emerging Technologies

With an emphasis on machine learning and artificial intelligence (AI), the Internet of Things (IoT), robotics, and data analytics, this research offers a methodical empirical evaluation of cutting-edge technologies in the field of smart manufacturing. The findings indicate notable progress in the abilities of the employees. Employee 2 had an astounding 30% gain in machine learning competence, while Employee 3 demonstrated a 50% growth in robotics proficiency. Production Line Efficiency showed scope for development; Line B showed a 0.7% gain in efficiency, indicating that there is still opportunity for process improvements. Analyzing sensor data highlights the need of ongoing maintenance and monitoring to guarantee optimum machine functioning. Data from quality control indicated that stricter guidelines were required to lower product faults. With implications for increased productivity and quality, this study advances our knowledge of the revolutionary potential of smart manufacturing technologies, including workforce development, technology adoption, and process optimization