Event-driven Systems Research Articles

Event-Driven Data Pipelines : A Cloud-Based Approach to Real-Time Data Processing

This article comprehensively analyzes event-driven data pipelines in cloud computing environments, examining their architecture, implementation considerations, and real-world applications. The article explores the fundamental components of event-driven systems, from event sources and message brokers to processing layers, while evaluating their performance characteristics and reliability mechanisms. Through detailed analysis of system architectures, we investigate the integration of various cloud services and their role in enabling scalable, real-time data processing. The article demonstrates how modern event-driven architectures achieve sub-millisecond processing times and handle millions of events per second while maintaining system resilience. Our findings reveal significant improvements in operational efficiency across various industries, including financial services, marketing automation, and IoT monitoring solutions. The article also addresses critical implementation aspects, including security frameworks, integration protocols, and best practices for system optimization. These insights provide valuable guidance for organizations leveraging event-driven architectures in their digital transformation initiatives. The article concludes that event-driven data pipelines represent a crucial advancement in data processing technology, offering unprecedented capabilities in handling real-time data streams while maintaining scalability, reliability, and cost-effectiveness.

Asynchronous Pulse vs. Synchronous Rhythm: Choosing the Right System Architecture

This article explores the fundamental paradigms of event-driven and synchronous systems in software architecture, examining their key features, ideal use cases, benefits, and challenges. It delves into the asynchronous nature of event-driven systems, highlighting their scalability and flexibility, while also discussing the predictability and consistency offered by synchronous approaches. The article presents statistical data on adoption rates, performance metrics, and industry trends, drawing from reputable sources such as Microsoft Azure, Gartner, and IEEE studies. It further investigates the factors influencing architectural choices and introduces the concept of hybrid approaches that combine elements of both paradigms. By providing a comprehensive comparison, this article aims to equip software architects and developers with the knowledge to make informed decisions in designing modern, efficient, and scalable systems.

StateOS : A Memory Sufficient Hybrid Operating System for IOT Devices

The proliferation of Internet of Things (IoT) technology has spurred the need for efficient operating systems tailored to the constraints of IoT devices. This paper presents StateOS, a hybrid operating system designed to meet the demands of sensor-based IoT devices and wireless sensors. StateOS combines the memory efficiency of event-driven systems with the clarity of control flow provided by multithreaded approaches. By implementing a microkernel architecture, cross-layer network protocol design, and a hybrid task scheduler, StateOS offers real-time capabilities while minimizing memory footprint. Macro-based task APIs and state machine-based visual programming facilitate cooperative threaded programming, making StateOS suitable for both experienced developers and novices. The paper provides an in-depth overview of StateOS's architecture, kernel functionalities, code examples, implementation on various platforms, performance evaluation, and concludes by highlighting its potential as a valuable tool for modern IoT application development. Keywords — Hybrid operating system (OS), Internet of Things (IoT) and Memory efficiency.

Technologies that are Anchored on Event Driven Architecture and their Impact on the Digital Economy

Event-Driven Architecture (EDA) has emerged as a pivotal framework in the digital economy, revolutionizing how systems are designed, implemented, and operated. By focusing on the production, detection, and reaction to events, EDA enables systems to be more responsive, scalable, and flexible. This research paper explores various technologies anchored on Event-Driven Architecture and examines their profound impact on the digital economy. Key technologies such as artificial intelligence (AI), the Internet of things (IoT), big data, block chain, 5G, 3Dprinting, robotics and drones are analyzed to understand their roles in enhancing real-time data processing, enabling micro services, and facilitating seamless integration across diverse systems. The study highlights how these technologies contribute to operational efficiency, customer experience, and business agility, while also addressing challenges related to implementation and management. The findings provide insights into how leveraging EDA can drive innovation and competitiveness in the digital economy, offering a roadmap for organizations aiming to harness the full potential of event-driven systems.

A Framework for the Generation of Monitor and Plant Model From Event Logs Using Process Mining for Formal Verification of Event-Driven Systems

A Framework for the Generation of Monitor and Plant Model From Event Logs Using Process Mining for Formal Verification of Event-Driven Systems

Advancements in spiking neural network communication and synchronization techniques for event-driven neuromorphic systems

Neuromorphic event-driven systems emulate the computational mechanisms of the brain through the utilization of spiking neural networks (SNN). Neuromorphic systems serve two primary application domains: simulating neural information processing in neuroscience and acting as accelerators for cognitive computing in engineering applications. A distinguishing characteristic of neuromorphic systems is their asynchronous or event-driven nature, but even event-driven systems require some synchronous time management of the neuron populations to guarantee sufficient time for the proper delivery of spiking messages. In this study, we assess three distinct algorithms proposed for adding a synchronization capability to asynchronous event-driven compute systems. We run these algorithms on POETS (Partially Ordered Event-Triggered Systems), a custom-built FPGA-based hardware platform, as a neuromorphic architecture. This study presents the simulation speed of SNNs of various sizes. We explore essential aspects of event-driven neuromorphic system design that contribute to efficient computation and communication. These aspects include varying degrees of connectivity, routing methods, mapping techniques onto hardware components, and firing rates. The hardware mapping and simulation of up to eight million neurons, where each neuron is connected to up to one thousand other neurons, are presented in this work using 3072 reconfigurable processing cores, each of which has 16 hardware threads. Using the best synchronization and communication methods, our architecture design demonstrates 20-fold and 16-fold speedups over the Brian simulator and one 48-chip SpiNNaker node, respectively. We conclude with a brief comparison between our platform and existing large-scale neuromorphic systems in terms of synchronization, routing, and communication methods, to guide the development of future event-driven neuromorphic systems.

Анализ численных методов интегрирования жестких систем высокой размерности в SimInTech

When modeling complex dynamic processes, it becomes necessary to numerically solve the Cauchy problem for systems of ordinary differential equations (ODEs). The efficiency of the applied numerical methods depends on the degree of stiffness and dimension of the problem [1–3]. Depending on the task class, different methods behave differently.This article provides a comparative analysis of explicit adaptive and diagonal-implicit integration methods implemented in the SimInTech software [4]. The SimInTech software package is designed to simulate complex dynamic processes in systems of various classes. The system supports the ability to develop models in the form of block diagrams, as well as describe systems of differential equations using the built-in programming language and simulate event-driven systems and finite automata.It is shown that the most effective in solving problems of the considered class are the diagonal-implicit Runge-Kutta type integration methods – DIRK2 and DIRK4 from the SimInTech package. The DIRK3 method is inferior due to the large number of calculations of the Jacobian matrix. The preferred method is rather The DIRK2 method is preferable in this case, because it has a greater number of time steps with almost the same performance and does not increase the integration step so much with relatively low accuracy settings of the integration method. Of the explicit methods of the Runge-Kutta type with an adaptive numerical scheme, the “Adaptive-5” method is the most effective for solving problems of this class. For problems of this class, we can recommend the use of explicit integration methods “Adaptive-5”, “Adaptive-1” with a small system dimension.The traditional implicit Gear and Euler integration methods also effectively solve this problem, provided that the algorithm for calculating the Jacobian matrix is effectively implemented.

Net Learning.

Graph neural networks, which generalize deep learning to graph-structured data, have achieved significant improvements in numerous graph-related tasks. Petri nets (PNs), on the other hand, are mainly used for the modeling and analysis of various event-driven systems from the perspective of prior knowledge, mechanisms, and tasks. Compared with graph data, net data can simulate the dynamic behavioral features of systems and are more suitable for representing real-world problems. However, the problem of large-scale data analysis has been puzzling the PN field for decades, and thus, limited its universal applicability. In this article, a framework of net learning (NL) is proposed. NL contains the advantages of PN modeling and analysis with the advantages of graph learning computation. Then, two kinds of NL algorithms are designed for performance analysis of stochastic PNs, and more specifically, the hidden feature information of the PN is obtained by mapping net information to the low-dimensional feature space. Experiments demonstrate the effectiveness of the proposed model and algorithms on the performance analysis of stochastic PNs.

High Speed UART Implementation Using VHDL

Abstract: The Universal Asynchronous Receiver and Transmitter (UART) could be a custom designed circuit that allows serial communication between a laptop and a computer peripheral. The complex nature of combined circuit generation has made machine design time consuming at the gate and switch flop levels. As a results of this reality, the style designer made the choice to use hardware description language during the virtual machine layout process. VHDL could be a hardware description language that's accustomed model digital systems. It contains information that you just may find useful. It includes information which will be accustomed explain the virtual machine's behaviour shape, also because the ability to explicitly specify its timing. VHDL could be a difficult and verbose language with many complicated assembles that have complicated semantic meanings and is difficult to grasp initially. The language facilitates hierarchical machine modelling likewise as top don methodologies. It provides an easier method for checking the UART and assisting within the discovery of any discrepancies. It also allows for a more behavioural explanation of the module's characteristics. It makes the look implementation easier to read and understand, and it also provides the flexibility to simply describe dependencies among numerous procedures that arise in complex eventdriven systems. Thus, the layout employs VHDL as a layout language to reap the transmitter module. First, the running version of the transmitter is defined. Then, using VHDL, all of the transmitter's blocks are designed and defined.

A Survey of Cyber Attacks on Cyber Physical Systems: Recent Advances and Challenges

A cyber physical system (CPS) is a complex system that integrates sensing, computation, control and networking into physical processes and objects over Internet. It plays a key role in modern industry since it connects physical and cyber worlds. In order to meet ever-changing industrial requirements, its structures and functions are constantly improved. Meanwhile, new security issues have arisen. A ubiquitous problem is the fact that cyber attacks can cause significant damage to industrial systems, and thus has gained increasing attention from researchers and practitioners. This paper presents a survey of state-of-the-art results of cyber attacks on cyber physical systems. First, as typical system models are employed to study these systems, time-driven and event-driven systems are reviewed. Then, recent advances on three types of attacks, i.e., those on availability, integrity, and confidentiality are discussed. In particular, the detailed studies on availability and integrity attacks are introduced from the perspective of attackers and defenders. Namely, both attack and defense strategies are discussed based on different system models. Some challenges and open issues are indicated to guide future research and inspire the further exploration of this increasingly important area.

A State-Equation-Based Method to Non-Reachability Analysis of Ordinary Petri Nets With Token-Free Circuit-Based Subnets

Petri nets (PNs) can model various event-driven systems well and detect a fault via reachability analysis. Some state-equation-based approaches are designed for only subclasses of PNs with specific structures. A key issue for PNs’ reachability analysis approach based on state equation is that a non-negative integer solution (NIS) to a state equation is necessary but not sufficient for a marking’s reachability. In this paper, we propose a circuit-based subnet. For ordinary Petri nets with token-free circuit-based subnets, we present an algorithm to determine that the state equation has NISs but the marking is non-reachable. We adopt a divide-and-conquer strategy to consider each circuit-based subnet. The proposed method is tested on a PN-based flexible manufacturing system with unreliable resources. When unreliable resources are lost, the non-reachable markings can be determined effectively. Besides, simulations are done, and the results verify the efficiency of the proposed algorithm compared with the reachability-tree-based method.

Semi-Supervised Learning Combining Backpropagation and STDP: STDP Enhances Learning by Backpropagation with a Small Amount of Labeled Data in a Spiking Neural Network

A semi-supervised learning method for spiking neural networks is proposed. The proposed method consists of supervised learning by backpropagation and subsequent unsupervised learning by spike-timing-dependent plasticity (STDP), which is a biologically plausible learning rule. Numerical experiments show that the proposed method improves the accuracy without additional labeling when a small amount of labeled data is used. This feature has not been achieved by existing semi-supervised learning methods of discriminative models. It is possible to implement the proposed learning method for event-driven systems. Hence, it would be highly efficient in real-time problems if it were implemented on neuromorphic hardware. The results suggest that STDP plays an important role other than self-organization when applied after supervised learning, which differs from the previous method of using STDP as pre-training interpreted as self-organization.

Stateflow-Based Energy Management Strategy for Hybrid Energy System to Mitigate Load Shedding

This study investigates the potential application of Stateflow (SF) to design an energy management strategy (EMS) for a renewable-based hybrid energy system (HES). The SF is an extended finite state machine; it provides a platform to design, model, and execute complex event-driven systems using an interactive graphical environment. The HES comprises photovoltaics (PV), energy storage units (ESU) and a diesel generator (Gen), integrated with the power grid that experiences a regular load shedding condition (scheduled power outages). The EMS optimizes the energy production and utilization during both modes of HES operation, i.e., grid-connected mode and the islanded mode. For islanded operation mode, a resilient power delivery is ensured when the system is subjected to intermittent renewable supply and grid vulnerability. The contributions of this paper are twofold: first is to propose an integrated framework of HES to address the problem of load shedding, and second is to design and implement a resilient EMS in the SF environment. The validation of the proposed EMS demonstrates its feasibility to serve the load for various operating scenarios. The latter include operations under seasonal variation, abnormal weather conditions, and different load shedding patterns. The simulation results reveal that the proposed EMS not only ensures uninterrupted power supply during load shedding but also reduces grid burden by maximizing the use of PV energy. In addition, the SF-based adopted methodology is envisaged to be a useful alternative to the popular design method using the conventional software tools, particularly for event-driven systems.

Parametric Analysis of the Impact Level of Tank Truck Columns on Transport Facilities during Braking

Systemic numerical studies have been carried out to analyze the influence of the main traffic parameters at the moment of braking of a regular column of tank trucks in order to determine the maximum values of the dynamic impact on transport facilities. A refined design scheme of a unified oscillatory system, a mathematical model developed by the authors, and a unified numerical algorithm for nonlinear oscillations are used. The computer complex in the Simulink simulation package for dynamic and event-driven systems of the MATLAB system was adapted to solve the posed problem on the basis of expert models developed earlier by the authors. A parametric analysis of the impact level of tank truck columns on transport facilities when changing the mode of movement is carried out according to the results of computational experiments. When simulating the passage of a vehicle column, the parameters of the movement of the column, the position of the first vehicle on the transport facility at the time of the start of braking, as well as the characteristics of an individual tanker in the column were varied. The degree of influence of each parameter is determined, and their combinations in which there is a danger of excess dynamic impact of a moving load on transport facilities are identified. The expediency of separating such columns into a special class of moving load and the need to amend the regulatory documents are shown.

Learning Fuzzy Automaton's Event Transition Matrix When Post-Event State Is Unknown.

Compared to other system modeling techniques, the fuzzy discrete event systems (FDESs) methodology has the unique capability of modeling a class of event-driven systems as fuzzy automata with ambiguous state and event-invoked state transition. In two recent papers, we developed algorithms for online-supervised learning of the fuzzy automaton's event transition matrix using fuzzy states before and after the occurrence of fuzzy events. The post-event state was assumed to be readily available while the pre-event state was either directly available or estimatable through learning. This article is focused on algorithm development for learning the transition matrix in a different setting-when the pre-event state is available but the post-event state is not. We suppose the post-event state is described by a fuzzy set that is linked to a (physical) variable whose value is available. Stochastic-gradient-descent-based algorithms are developed that can learn the transition matrix plus the parameters of the fuzzy sets when the fuzzy sets are of the Gaussian type. Computer simulation results are presented to confirm the theoretical development.

The application of relational interactive logic to build logical triggers in event-driven systems

The paper proposes an approach to the building of logical triggers in event-driven systems using relational interactive logic. In general, a trigger is viewed as a system of logical equations and inequalities. The solution of the system, carried out by the execution of a SQL query, is the main one for the formation of alert or targeted notifications or for changing the parameters of the control action within a specific system. To automate the described approach, an example of a modular scalable architecture of an information system is proposed. It provides the creation of triggers based on systems of logical equations and inequalities, as well as their verification of their implementation within one or more data domains

A Hybrid Event Trigger Mechanism and Time-Delay Partitioning Are Applied to Event-Driven Control Systems

This paper proposes an idea of using time-delay partitioning to construct a Lyapunov–Krasovskii functional (LKF) to analyse event-driven network control systems (NCSs) with the H∞ performance. Firstly, select a mixed event-driven mechanism, in which an adjustable absolute trigger mechanism is added to the trigger condition. Trigger term can be indicated to use a delay model. Secondly, a suitable LKF is created, which makes use of time-delay partitioning. Based on Wirtinger inequality and linear matrix inequalities (LMI), the close system with H∞ performance index level is global uniform ultimate bounded. Finally, a numerical simulation example proves the effectiveness of the proposed method.

Evaluation of Stream Processing Frameworks

The increasing need for real-time insights in data sparked the development of multiple stream processing frameworks. Several benchmarking studies were conducted in an effort to form guidelines for identifying the most appropriate framework for a use case. In this article, we extend this research and present the results gathered. In addition to Spark Streaming and Flink, we also include the emerging frameworks Structured Streaming and Kafka Streams. We define four workloads with custom parameter tuning. Each of these is optimized for a certain metric or for measuring performance under specific scenarios such as bursty workloads. We analyze the relationship between latency, throughput, and resource consumption and we measure the performance impact of adding different common operations to the pipeline. To ensure correct latency measurements, we use a single Kafka broker. Our results show that the latency disadvantages of using a micro-batch system are most apparent for stateless operations. With more complex pipelines, customized implementations can give event-driven frameworks a large latency advantage. Due to its micro-batch architecture, Structured Streaming can handle very high throughput at the cost of high latency. Under tight latency SLAs, Flink sustains the highest throughput. Additionally, Flink shows the least performance degradation when confronted with periodic bursts of data. When a burst of data needs to be processed right after startup, however, micro-batch systems catch up faster while event-driven systems output the first events sooner.

Verification of Cyberphysical Systems

The value of verification of cyberphysical systems depends on the relationship between the state of the software and the state of the physical system. This relationship can be complex because of the real-time nature and different timelines of the physical plant, the sensors and actuators, and the software that is almost always concurrent and distributed. In this paper, we study different ways to construct a transition system model for the distributed and concurrent software components of a CPS. The purpose of the transition system model is to enable model checking, an established and widely used verification technique. We describe a logical-time-based transition system model, which is commonly used for verifying programs written in synchronous languages, and derive the conditions under which such a model faithfully reflects physical states. When these conditions are not met (a common situation), a finer-grained event-based transition system model may be required. We propose an approach for formal verification of cyberphysical systems using Lingua Franca, a language designed for programming cyberphysical systems, and Rebeca, an actor-based language designed for model checking distributed event-driven systems. We focus on the cyber part and model a faithful interface to the physical part. Our method relies on the assumption that the alignment of different timelines during the execution of the system is the responsibility of the underlying platforms. We make those assumptions explicit and clear.

Methodology for Development of Event-driven Software Systems using CIAO Specification Language

Event-driven software systems, belonging to the class of systems with complex behavior in the scientific literature, are reactive systems, which react to the same input effect in different ways depending on their state and background.
 It is convenient to describe such systems using state-transition models utilizing special language tools, both graphical and textual. Methodology for automated development of systems with complex behavior using the designed CIAO language (Cooperative Interaction of Automata Objects), which allows formally specifying the required behavior based on an informal description of the reacting system, is presented.
 An informal description of a reacting system can be provided verbally in a natural language or in another way adopted in a specific domain. Further, according to this specification in the CIAO language, a software system for interacting automata in the C++ programming language is generated with a special system.
 The generated program implements a behavior guaranteed to correspond to a given specification and original informal description. CIAO provides both graphical and textual notation. Graphic notation is based on an extended notation of state machine diagrams and component diagrams of the unified modeling language UML, which are well established in describing the behavior of event-driven systems.
 The text syntax of the CIAO language is described by context-free grammar in regular form. Automatically generated C++ code allows using of both library and any external functions written manually.
 At the same time, the evident correspondence of the formal specification and the generated code is preserved on conditions that the external functions conform to their specifications.
 As an example, an original solution to D. Knut's problem of a responsive elevator control system is proposed. The effectiveness of the proposed methodology is demonstrated, since the automaton-converter generating the C++ code is presented as a responsive system, is specified in the CIAO language and implemented by the bootstrapping. The proposed methodology is compared with other well-known formal methods for describing systems with complex behavior.