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Context. The article addresses the problem of causal events asynchrony which appears in the service-oriented information systems that does not guarantee that the events will be delivered in the order they were published. It may cause intermittent faults occurring at intervals, usually irregular, in a system that functions normally at other times. Objective. The goal of the work is the comparison and assessment of several existing approaches and providing a new approach for solving the causal events synchronization issue in application to the systems developed using Command Query Responsibility Segregation (CQRS) with Event Sourcing (ES) architecture approach. Methods. Firstly, the method of estimation of the likelihood of causal events occurring within the systems as the foundation for choosing the solution is suggested. Based on the results of the analysis of several projects based on CQRS with ES architecture it shows that the likelihood of critical causal events depends on the relationships among entities and the use-cases connected with the entities. Secondly, the Container of Events method, which represents a variation of event with full causality history, adapted to the needs of CQRS with ES architecture systems, was proposed in this work. The variants of its practical implementation have also been discussed. Also, the different solutions, such as Synchronous Event Queues and variation of Causal Barrier method were formalized and assessed. Thirdly, the methods described have been discussed and evaluated using performance and modification complexity criteria. To make the complexity-performance comparative assessment more descriptive the integrated assessment formula was also proposed. Results. The evaluation results show that the most effective solution of the issue is to use the Container of Events method. To implement the solution, it is proposed to make the modifications of the Event Delivery Subsystem and event handling infrastructure. Conclusions. The work is focused on the solution of the critical causal events issue for the systems based on CQRS with ES architecture. The method of estimation of the likelihood of critical causal events has been provided and different solutions of the problem have been formalized and evaluated. The most effective solution based on Container of Events method was suggested. [ABSTRACT FROM AUTHOR]

Digital transformation has become a key focus for organizations across various industries, and micro services architecture has emerged as a popular approach for developing digital transformation systems due to its scalability, modularity, and flexibility. This research work proposes an upgraded novel integral architecture for digital transformation systems based on the micro services technique, which incorporates features such as service mesh, event-driven architecture, server less computing, and DevOps practices. The proposed architecture provides a comprehensive and effective solution for developing digital transformation systems that are scalable, modular, and maintainable. The research work discusses various models and methods for designing micro services architectures of digital transformation and provides a structured approach for integrating digital transformation architecture models. The practical implementation of the proposed architecture involves several steps, including system design and development, implementation of service mesh, event-driven architecture, server less computing, and DevOps. The proposed architecture and methodology can be applied in real-world scenarios to achieve successful digital transformation. [ABSTRACT FROM AUTHOR]

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This paper proposes a novel event-driven architecture for enhancing edge-based vehicular systems within smart transportation. Leveraging the inherent real-time, scalable, and fault-tolerant nature of the Elixir language, we present an innovative architecture tailored for edge computing. This architecture employs MQTT for efficient event transport and utilizes Elixir's lightweight concurrency model for distributed processing. Robustness and scalability are further ensured through the EMQX broker. We demonstrate the effectiveness of our approach through two smart transportation case studies: a traffic light system for dynamically adjusting signal timing, and a cab dispatch prototype designed for high concurrency and real-time data processing. Evaluations on an Apple M1 chip reveal consistently low latency responses below 5 ms and efficient multicore utilization under load. These findings showcase the system's robust throughput and multicore programming capabilities, confirming its suitability for real-time, distributed edge computing applications in smart transportation. Therefore, our work suggests that integrating Elixir with an event-driven model represents a promising approach for developing scalable, responsive applications in edge computing. This opens avenues for further exploration and adoption of Elixir in addressing the evolving demands of edge-based smart transportation systems. [ABSTRACT FROM AUTHOR]

Data interoperability implies data exchanges among intra and inter enterprises collaborating with information systems (IS). The multiplicity of these exchanges and the increasing number of data exchanged generates complexity and brings out the needs for control to avoid dysfunctions with a negative impact on the overall performance of the systems. Indeed, actually, interoperability has become a necessary performance lever that thus requires particular attention. Being at a low level in the enterprise interoperability concerns, data interoperability is mainly automated, which leads us to question: Is it possible to evaluate data interoperability performance and security based on inspection and analysis of ongoing data exchanges? We therefore endeavored to answer this question by establishing monitoring and analysis systems. In this paper, we present a research work which addresses services provided by a messaging‐based communication system. In order to collect information on Information System interactions allowing one to assess their level of data interoperability, we propose a messaging metamodel that aggregates the collected information. It provides a single point of control and enables one to determine indicators of potential interoperability problems. The approach is validated on two case studies. An industrial case study of interactions among existing systems is presented to showcase the feasibility and interest of the approach. It is proposed on top of RabbitMQ and allows our partner to identify some issues in the studied information system. The second case study shows that the approach can integrate other protocols, by reading MQTT messages. The approach is implemented using Moose, a software analysis platform. [ABSTRACT FROM AUTHOR]

The New Observing Strategies Testbed (NOS‐T) is a digital engineering environment for enabling distributed space mission (DSM) technology demonstrations. Its event‐driven architecture enables users to orchestrate DSM test campaigns by developing applications that communicate state changes via messages. NOS‐T is motivated by requirements such as geographical distribution, cross‐boundary participation, wide applicability, and usability that make it unique in this field. This article introduces NOS‐T and describes its architecture in the context of an example DSM test suite, FireSat+. The scalability of NOS‐T is demonstrated with a performance assessment of its capabilities under a stress test of high message frequency and payload size, which are both related to the complexity of potential user‐generated test cases. Results show that message periodicity has no significant effect on median delay time over the ranges sampled; however, the message payload size induces linear growth in median delay time of approximately 1.5 ms per kB. Future NOS‐T applications can adjust the execution time scaling factor and message payload size to match operational constraints on allowable delay. [ABSTRACT FROM AUTHOR]

This paper focuses on Internet of Things (IoT) architectures and knowledge generation out of streams of events as the primary elements concerning the creation of user-centric IoT services. We provide a general, symmetrical IoT architecture, which enables two-way bidirectional communication between things and users within an application domain. We focus on two main components of the architecture (i.e., Event Engine and Process Engine) that handle event transformation by implementing parametric Complex Event Processing (CEP). More specifically, we describe and implement the transformation cycle of events starting from raw IoT data to their processing and transformation of events for calculating information that we need in an IoT-enabled application context. The implementation includes a library of composite transformations grouping the gradual and sequential steps for transforming basic IoT events into business events, which include ingestion, event splitting, and calculation of measurements' average value. The appropriateness and possibility of inclusion and integration of the implementation in an IoT environment are demonstrated by providing our implementation for a smart farming application domain with four scenarios that each reflect a user's requirements. Further, we discuss the quality properties of each scenario. Ultimately, we propose an IoT architecture and, specifically, a parametric CEP model and implementation for future researchers and practitioners who aspire to build IoT applications. [ABSTRACT FROM AUTHOR]

Currently, many governments are gearing up to promote the development of smart cities in their countries. A smart city is an urban area using different types of sensors to collect data, which will then be used to manage assets and resources efficiently. Through smart technology, the quality of living and performance of urban services are enhanced. Recent works addressed a set of platforms aimed to support the development of smart city applications. It seems that most of them involved dealing with collecting, managing, analyzing, and correlating data to extract new information useful to a city, but they do not integrate a diversified set of services and react to events on the fly. Moreover, the application development facilities provided by them seem to be limited and might even increase the complexity of this task. We propose an event-based architecture with components that meet important requirements for smart city platforms, supporting increased demand for scalability, flexibility, and heterogeneity in event processing. We implement such architecture and data representation models, handling different data formats, and supporting a semantics-based data model. Finally, we discuss the effectiveness of a S mart Event-based Middleware (SEMi) and present empirical results regarding a performance evaluation of SEMi. [ABSTRACT FROM AUTHOR]

This paper proposes a novel event-driven architecture for enhancing edge-based vehicular systems within smart transportation. Leveraging the inherent real-time, scalable, and fault-tolerant nature of the Elixir language, we present an innovative architecture tailored for edge computing. This architecture employs MQTT for efficient event transport and utilizes Elixir’s lightweight concurrency model for distributed processing. Robustness and scalability are further ensured through the EMQX broker. We demonstrate the effectiveness of our approach through two smart transportation case studies: a traffic light system for dynamically adjusting signal timing, and a cab dispatch prototype designed for high concurrency and real-time data processing. Evaluations on an Apple M1 chip reveal consistently low latency responses below 5 ms and efficient multicore utilization under load. These findings showcase the system’s robust throughput and multicore programming capabilities, confirming its suitability for real-time, distributed edge computing applications in smart transportation. Therefore, our work suggests that integrating Elixir with an event-driven model represents a promising approach for developing scalable, responsive applications in edge computing. This opens avenues for further exploration and adoption of Elixir in addressing the evolving demands of edge-based smart transportation systems.

Activities an organization performs can be viewed as a sequence of events and responses to them. Event-driven solutions have emerged for event management to help capture events and respond by triggering other events. Nevertheless, designing solutions which conforms with a problem domain is not trivial task. Therefore, holistic understanding of the problem domain is essential. To understand the problem domain, we use formalism of Topological Functioning Model which analyses an organization as a system from computation independent viewpoint and helps to represent holistically both structural and functional aspects of an organization. By applying case study analysis research method, the article discusses how the use of Topological Functioning Model can help to derive an event-driven solution which conforms with the problem domain of an organization. [ABSTRACT FROM AUTHOR]

Sleep monitoring using polysomnography (PSG) in hospitals can be considered expensive, so the preferable way is to use contactless and wearable sensors to monitor sleep daily by patients at home. In this study, the Internet-of-Things (IoT) platform was utilized for sleep monitoring with contactless or wearable sensors as an integrated system developed based on an event-driven and microservice architecture. Multiple services that respond to events are provided within the system. Electrocardiogram (ECG) data were used as the input in the sleep monitoring system. The combination of the weighted extreme learning machine (WELM) algorithm with particle swarm optimization (PSO) was used to process the ECG data, followed by fuzzy logic to measure sleep quality, then display the data on the dashboard. Based on the experimental results, the proposed architecture increased throughput by 34.76%, decreased response time by 55.85%, and reduced memory consumption by 37.26% per instance replication compared to the non-event-driven architecture. The accuracies of the sleep stage classification were 78.78% and 73.09% for the three and four classes, respectively, and the area under a receiver operating characteristic (ROC) curve (AUC) reached 0.89 for both the three and four class classifications.

Future manufacturing systems need to be more flexible, to embrace tougher and constantly changing market demands. They need to make better use of plant data, ideally utilising all data from the entire plant. Low-level data should be refined to real-time information for decision-making, to facilitate competitiveness through informed and timely decisions. The Line Information System Architecture (LISA), is presented in this paper. It is an event-driven architecture featuring loose coupling, a prototype-oriented information model and formalised transformation services. LISA is designed to enable flexible factory integration and data utilisation. The focus of LISA is on integration of devices and services on all levels, simplifying hardware changes and integration of new smart services as well as supporting continuous improvements on information visualisation and control. The architecture has been evaluated on both real industrial data and industrial demonstrators and it is also being installed at a large automotive company. This article is an extended and revised version of the paper presented at the 2015 IFAC Symposium on Information Control in Manufacturing (INCOM 2015). The paper has been restructured in regards to the order and title of the chapters, and additional information about the integration between devices and services aspects have been added. The introduction and the general structure of the paper now better highlight the contributions of the paper and the uniqueness of the framework. [ABSTRACT FROM AUTHOR]

Summary: We introduce trust‐based event‐driven (TBED) infrastructure for forest monitoring and reporting. TBED endows the real‐time local monitoring along with spatial monitoring. None of the work till now considers both local and spatial monitoring. To the best of our knowledge, TBED is the first attempt in this domain, where event‐related information is reported to the consumers along with the effective area. TBED is based on the event‐driven architecture in which device management and monitoring authorities, middleware event handler (MEH), and consumers are involved. Additionally, we present a trust mechanism to evaluate the sensor node trust for reliable information. At last, we present a case study of fire monitoring to depict the real‐life scenario. [ABSTRACT FROM AUTHOR]

This paper focuses on Internet of Things (IoT) architectures and knowledge generation out of streams of events as the primary elements concerning the creation of user-centric IoT services. We provide a general, symmetrical IoT architecture, which enables two-way bidirectional communication between things and users within an application domain. We focus on two main components of the architecture (i.e., Event Engine and Process Engine) that handle event transformation by implementing parametric Complex Event Processing (CEP). More specifically, we describe and implement the transformation cycle of events starting from raw IoT data to their processing and transformation of events for calculating information that we need in an IoT-enabled application context. The implementation includes a library of composite transformations grouping the gradual and sequential steps for transforming basic IoT events into business events, which include ingestion, event splitting, and calculation of measurements’ average value. The appropriateness and possibility of inclusion and integration of the implementation in an IoT environment are demonstrated by providing our implementation for a smart farming application domain with four scenarios that each reflect a user’s requirements. Further, we discuss the quality properties of each scenario. Ultimately, we propose an IoT architecture and, specifically, a parametric CEP model and implementation for future researchers and practitioners who aspire to build IoT applications.

Currently, many governments are gearing up to promote the development of smart cities in their countries. A smart city is an urban area using different types of sensors to collect data, which will then be used to manage assets and resources efficiently. Through smart technology, the quality of living and performance of urban services are enhanced. Recent works addressed a set of platforms aimed to support the development of smart city applications. It seems that most of them involved dealing with collecting, managing, analyzing, and correlating data to extract new information useful to a city, but they do not integrate a diversified set of services and react to events on the fly. Moreover, the application development facilities provided by them seem to be limited and might even increase the complexity of this task. We propose an event-based architecture with components that meet important requirements for smart city platforms, supporting increased demand for scalability, flexibility, and heterogeneity in event processing. We implement such architecture and data representation models, handling different data formats, and supporting a semantics-based data model. Finally, we discuss the effectiveness of a S mart Event-based Middleware (SEMi) and present empirical results regarding a performance evaluation of SEMi.

This article presents a 148-nW always-on wake-up system that drastically reduces the system power consumption of Internet of Things (IoT) sensor nodes while oftentimes operating in random-sparse-event (RSE) scenarios. To significantly reduce the long-term average (LTA) power consumption and realize multiapplication and intelligent event detection, three techniques are proposed: 1) In a three-stage pipelined event-driven architecture, a frame generator and a convolutional neural network intelligent inference engine (CNN IIE) in stage III are event-driven by the preliminary detectors in stage II, and the detectors are triggered by a level-crossing (LC) analog-to-digital converter (ADC), i.e., stage I, dramatically reducing the overall power consumption. 2) The clock-free pulse-based instant rate of change (IROC) feature extractor directly processes the asynchronous pulses of the LC-ADC outputs in the temporal domain instead of utilizing a conventional power-hungry frequency-domain method. 3) A reconfigurable IROC, the frame generator, and the CNN IIE provide adaptive intelligence for various IoT events, enhancing the accuracy of multipurpose detection with ultralow power. We demonstrate two artificial intelligence IoT (AIoT) applications at 0.6-V VDD. For electrocardiogram (ECG) recognition, one example works at a typical event rate (ER) of ~4800/h, with an active power of 1.68 $\mu \text{W}$ and a precision of up to 99%; the other is used for keyword spotting (KWS), where the chip achieves 378 nW at ~720/h ER and 94% accuracy. The LTA power is bounded to 148 nW, while the event-driven chip is on call and waiting for events; this chip dominates the AIoT device battery life in RSE scenarios. [ABSTRACT FROM AUTHOR]

This work presents an ultra-low-power software-defined always-on wake-up system to drastically decrease the system power of Internet of Things (IoTs) devices, which usually operate in random-sparse-event (RSE) scenarios. It mainly thanks to a clock-free time-shielding level-crossing ADC (TS-LCADC), software-defined clock-free multi-function detectors, and an asynchronous pipelined event-driven architecture. First, by quantifying RSE noisy signals with clock-free adaptive sampling in a signal-noise-rejecting manner, the proposed TS-LCADC reduces number of sampling points and power, and consumes only 41 nW when on-call waiting for IoT events. Second, the proposed clock-free multi-function detectors with offline and online programmability are able to character the signal features of versatile IoTs events and allow versatile and dynamic wake-up functions. Third, the proposed asynchronous pipelined event-driven architecture minimizes the system activity and thus power, because a power-hungry high-performance system (HPS) is only woken up when a detected parameter crosses its corresponding threshold. As such, the long-term average power (LTA-power) is dominated by the always-on circuits in RSE scenarios. The measurement results achieve 71–75 nW for three typical applications, i.e., heart rate, epilepsy, and keyword envelope detection. The LTA power is only 57 nW when waiting for RSE events, which is 30 × lower than a prior general-purpose wake-up chip. Compared with other works of dedicated voice and acoustic wake-up functions, this work consumes 2 × and 17 × less power, respectively, while featuring 16 × higher signal bandwidth and a broader versatility. [ABSTRACT FROM AUTHOR]

This paper presents a novel spiking neural network (SNN) classifier architecture for enabling always-on artificial intelligent (AI) functions, such as keyword spotting (KWS) and visual wake-up, in ultra-low-power internet-of-things (IoT) devices. Such always-on hardware tends to dominate the power efficiency of an IoT device and therefore it is paramount to minimize its power dissipation. A key observation is that the input signal to always-on hardware is typically sparse in time. This is a great opportunity that a SNN classifier can leverage because the switching activity and the power consumption of SNN hardware can scale with spike rate. To leverage this scalability, the proposed SNN classifier architecture employs event-driven architecture, especially fine-grained clock generation and gating and fine-grained power gating, to obtain very low static power dissipation. The prototype is fabricated in 65 nm CMOS and occupies an area of 1.99 mm2. At 0.52 V supply voltage, it consumes 75 nW at no input activity and less than 300 nW at 100% input activity. It still maintains competitive inference accuracy for KWS and other always-on classification workloads. The prototype achieved a power consumption reduction of over three orders of magnitude compared to the state-of-the-art for SNN hardware and of about 2.3X compared to the state-of-the-art KWS hardware.

This paper presents a novel spiking neural network (SNN) classifier architecture for enabling always-on artificial intelligent (AI) functions, such as keyword spotting (KWS) and visual wake-up, in ultra-low-power internet-of-things (IoT) devices. Such always-on hardware tends to dominate the power efficiency of an IoT device and therefore it is paramount to minimize its power dissipation. A key observation is that the input signal to always-on hardware is typically sparse in time. This is a great opportunity that a SNN classifier can leverage because the switching activity and the power consumption of SNN hardware can scale with spike rate. To leverage this scalability, the proposed SNN classifier architecture employs event-driven architecture, especially fine-grained clock generation and gating and fine-grained power gating, to obtain very low static power dissipation. The prototype is fabricated in 65 nm CMOS and occupies an area of 1.99 mm2. At 0.52 V supply voltage, it consumes 75 nW at no input activity and less than 300 nW at 100% input activity. It still maintains competitive inference accuracy for KWS and other always-on classification workloads. The prototype achieved a power consumption reduction of over three orders of magnitude compared to the state-of-the-art for SNN hardware and of about 2.3X compared to the state-of-the-art KWS hardware. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is enhancing the intelligence of the societies through a rapid transition to a smarter, automatic, responsive world due to the dramatic increase in the number of sensors deployed around the world. Collecting, modeling, and reasoning data generated by sensors play a crucial role in data analysis. Analyzing and interpreting real-time information transmitted through heterogeneous wireless networks are challenges that IoT applications encounter. Complex Event Processing (CEP) is a data stream tracking method used to extract the meaningful data obtained from the network results in real-time decision making. Instance data analysis, early diagnosis, and effective treatment of patients through the massive volume of data are considered indispensable parameters that have made the healthcare industry more reliant on real-time event processing than other industries. To achieve actionable insights, forecasting anomaly, and increasing healthcare quality, applying the CEP method is introduced in this area. In this paper, an event-driven IoT architecture is presented for data analysis of reliable healthcare applications, including context, event, and service layers. Dependability parameters are considered in each layer, and the CEP method as a novel solution and automated intelligence is applied in the event layer. Implementation results showed that the CEP method increased reliability, reduced costs, and improved healthcare quality. [ABSTRACT FROM AUTHOR]

An effective crisis response requires up-to-date information. The crisis cell must reach for new, external, data sources. However, new data lead to new issues: their volume, veracity, variety or velocity cannot be managed by humans only, especially under high stress and time pressure. This paper proposes (i) a framework to enhance situation awareness while managing the 5Vs of Big Data, (ii) general principles to be followed and (iii) a new architecture implementing the proposed framework. The latter merges event-driven and model-driven architectures. It has been tested on a realistic flood scenario set up by official French services. [ABSTRACT FROM AUTHOR]

Industry 4.0 paradigm envisions a new generation of collaborative manufacturing system where all the components are connected exploiting the Industrial Internet of Things (IIoT) protocol. Through this pervasive connection, sensors, machines, robots and other production equipment can communicate and share information with their surroundings resources. In order to contribute to realise such a vision, this paper introduces an event-driven framework that enables the interaction of heterogeneous distributed resources, according to the principles of Industry 4.0 paradigm. The overall aim of this research is providing new mechanisms to notify significant information produced within the factory towards enabled and interested production resources. In particular, combining various technologies such as IIoT, Semantic Web, and Multi-Agent based systems, the proposed framework allows the resources to be updated about changes occurred in their context. Under these conditions, the framework can be exploited in all the scenarios where the cooperation among resources is a strict requirement, thus contributing to support various ad-hoc services that drive modern factories towards a more sustainable, efficient, and competitive manufacturing system. Moreover, the validity of the framework is demonstrated leveraging the implementation of its instance within a real case study. [ABSTRACT FROM AUTHOR]

In Internet of Things (IoT), the main driving technologies are considered to be tiny sensory objects. These objects cannot host traditional preventive and detective technologies to provide protection against the increasing threat sophistication. Furthermore, these solutions are limited to analyzing particular contextual information, for instance network information or files, and do not provide holistic context for risk analysis and response. Analyzing a part of a situation may lead to false alarms and later to unnecessary and incorrect configurations. To overcome these concerns, we proposed an event-driven adaptive security (EDAS) model for IoT. EDAS aims to observe security events (changes) generated by various things in the monitored IoT environment, investigates any intentional or unintentional risks associated with the events and adapts to it autonomously. It correlates different events in time and space to reduce any false alarms and provides a mechanism to predict attacks before they are realized. Risks are responded to autonomically by utilizing a runtime adaptation ontology. The mitigation action is chosen after assessing essential information, such as the risk faced, user preferences, device capabilities and service requirements. Thus, it selects an optimal mitigation action in a particular adverse situation. The objective of this paper is to investigate EDAS feasibility and its aptitude as a real-world prototype in a remote patient monitoring context. It details how EDAS can be a practical choice for IoT-eHealth in terms of the security, design and implementation features it offers as compared to traditional security controls. We have explained the prototype’s major components and have highlighted the key technical challenges.

Using currently available infrastructure in pervasive environments it is possible to provide intelligent mechanisms that offer people help and guidance for organizing their daily activities. In this study, a framework providing such capabilities is proposed. This framework allows users to model their daily activities in the form of workflows, which are adaptable at run-time according to context information collected in pervasive environments. A workflow engine is used for modelling and management of workflows, while a separate rule engine with complex event processing (CEP) capability is incorporated into the framework for enhancing workflow adaptation and execution. The adaptation model in the framework allows for the modelling of activities in a hierarchical manner, from high level abstract activities to more detailed ones. An event-driven architecture (EDA) is utilized for loosely coupled interaction between the workflow engine and the rule engine, allowing these engines and other context sources to exchange data among themselves. Moreover, the EDA allows incorporation of context information into the workflow models without modifying the workflow language. A level of automation higher than the level supported by workflows is proposed by processing events in pervasive environments using CEP. A prototype implementation is developed and the framework is evaluated with some real life examples that demonstrate its applicability. [ABSTRACT FROM AUTHOR]

In order to remain competitive, more and more industries are embracing the concept of industry 4.0 and trying to improve their manufacturing systems. A modern manufacturing system is the one which provides the industry with flexibility to adopt to the constantly changing market demands. In order to be flexible, the manufacturing system should be able to make better use of the shop floor data. Data from the shop floor level should be available in real time to facilitate the decision making process. The process of implementation of an event driven architecture along with services in a real time production environment will be discussed in this paper. For this purpose, pedal car line at Scania Sodertalje is analyzed for the implementation of event driven architecture. The old hand held tools and controllers at the assembly stations were replaced with modern wireless power tools and the latest power focus 6000 controller. The data generated by the power tools was converted into a standard format so that it can easily be interpreted by a message broker. The messages were published on Kafka message bus where other systems were able to subscribe and consume the data. A couple of endpoints were created, one was a REST API that subscribed to the message bus and consumed the data from power tools and visualized key parameters for the operator at the shop floor in real time. Second service created was to calculate certain KPIs and visualize them in graphs form for the maintenance and production engineers. This implementation demonstrated that an event driven architecture such as LISA can be implemented on a real shop floor making real time data available to different systems. [ABSTRACT FROM AUTHOR]