Resultados de búsqueda para Complex Event Processing

Blockchain provides an immutable distributed ledger for storing transactions. One of the challenges of blockchain is the particular processing of dynamic queries due to accumulating costs. Complex Event Processing (CEP) provides efficient and effective support for this in a way, however, that is difficult to integrate with blockchain. This paper addresses the research challenges of integrating blockchain with CEP. More specifically, we envision an effective development environment in which (i) event-driven smart contracts are modeled in a graphical way, which are, in turn, (ii) automatically transformed into complementary code that is deployed in both a CEP engine and a blockchain network, and then (iii) executed on off-chain CEP applications which, connected to different data sources and sinks, automatically invoke smart contracts when event pattern conditions are met. We follow a classic systems engineering approach for defining the concepts of our system, called CEPchain, which addresses the described requirements. CEPchain was evaluated using a real-world case study for vaccine delivery, which requires an unbroken cold chain. The results demonstrate that our approach can be applied without requiring experts on event processing and smart contract languages. Our contribution simplifies the design of integrated CEP and blockchain functionality by hiding implementation details and supporting efficient deployment.

The Internet of Things (IoT) is growing globally at a fast pace. However, the increase in IoT devices has brought with it the challenge of promptly detecting and combating the cybersecurity threats that target them. To deal with this problem, we propose an intelligent architecture that integrates Complex Event Processing (CEP) technology and the Machine Learning (ML) paradigm in order to detect different types of IoT security attacks in real time. In particular, such an architecture is capable of easily managing event patterns whose conditions depend on values obtained by ML algorithms. Additionally, a model-driven graphical tool for security attack pattern definition and automatic code generation is provided, hiding all the complexity derived from implementation details from domain experts. The proposed architecture has been applied in the case of a healthcare IoT network to validate its ability to detect attacks made by malicious devices. The results obtained demonstrate that this architecture satisfactorily fulfils its objectives.

Internet of Things (IoT) has radically transformed the world; currently, every device can be connected to the Internet and provide valuable information for decision-making. In spite of the fast evolution of technologies accompanying the grow of IoT, one of the remaining challenges in this scope is the design of a Service Oriented Architecture (SOA) for IoT, which facilitates the inclusion of data coming from several IoT devices as well the delivery of such data among system agents, real-time data processing and service provision to users. Furthermore, context-aware data processing and architec-tures still pose a challenge, regardless of being key requirements in order to get stronger IoT architectures. Besides, with the objective of sharing information across platforms, collaborative architectures for data sharing in the scope of the IoT are an essential re-quirement for giving additional value to any decision-making process. To sum up, IoT architectures should provide essential elements such as sensor devices, offered services, communication networks and event context processing; always promoting key features such as interoperability, reliability and scalability.
To face this challenge, we propose a COLLaborative ConText Aware Service Ori-ented Architecture (COLLECT), which facilitates: (1) Implementing reliable collabo-ration among several nodes through a collaborative Event Driven SOA. (2) Ensuring system scalability and interoperability through the opportunity of federating Enterprise Service Buses (ESB) in the cloud and through distributed Complex Event Processing (CEP). (3) Facilitating the task of processing information and publishing and subscrib-ing to distributed complex events of interest in the context of the application.

Air pollution generated by road traffic in large cities is a great concern in today’s society since pollution has an important impact on human health, even causing premature deaths. To address the problem, this paper presents an Intelligent
Transportation System model based on Complex Event Processing technology and Colored Petri Nets (CPNs). It takes into consideration the levels of environmental pollution and road traffic, according to the air quality levels accepted by the
international recommendations as well as the handbook emission factors for road transport methodology. This proposal, therefore, tackles a common problem in today’s large cities, where traffic restrictions must be applied due to environmental
pollution. CPNs are used in this work as a tool to make decisions about traffic regulations, so as to reduce pollution levels

AUTHORSAlejandro Calderón, Juan Boubeta-Puig & Mercedes RuizJOURNALInformation and Software Technology (vol. 95, pp. 238-264, 2018). IF: 2.627 (2017). Q1 (16/104) in “Computer Science, Software Engineering” category.DOIhttps://doi.org/10.1016/j.infsof.2017.11.009ABSTRACTContext: Gamification has been proven to increase engagement and motivation in multiple and different non-game contexts such as healthcare, education, workplace, and marketing, among others. However, many of these applications fail to achieve the desired benefits of gamification, mainly because of a poor design.Objective: This paper explores the conceptualization, implementation and monitoring phases of meaningful gamification strategies and proposes a solution for strategy experts that hides the implementation details and helps them focus only on what is crucial for the success of the strategy. The solution makes use of Model-Driven Engineering (MDE) and Complex Event Processing (CEP) technology.Method: An easy-to-use graphical editor is used to provide the high-level models that represent the design of the gamification strategy and its deployment and monitoring. These models contain the event pattern definitions to be automatically transformed into code. This code is then deployed both in a CEP engine to detect the conditions expressed in such patterns and in an enterprise service bus to execute the corresponding pattern actions.Results: The paper reports on the use of both a graphical modeling editor for gamification domain definition and a graphical modeling editor for gamification strategy design, monitoring and code generation in event-based systems. It also shows how the proposal can be used to design and automate the implementation and monitoring of a gamification strategy in an educational domain supported by a well-known Learning Management System (LMS) such as Moodle.Conclusion: It can be concluded that this unprecedented model-driven approach leveraging gamification and CEP technology provides strategy experts with the ability to graphically define gamification strategies, which can be directly transformed into code executable by event-based systems. Therefore, this is a novel solution for bringing CEP closer to any strategy expert, positively influencing the gamification strategy design, implementation and real-time monitoring processes.

RESUMEN: Software performance engineering is a mature field that offers methods to assess system performance. Process mining is a promising research field applied to gain insight on system processes. The interplay of these two fields opens promising applications in the industry. In this work, we report our experience applying a methodology, based on process mining techniques, for the performance assessment of a commercial data-intensive software application. The methodology has successfully assessed the scalability of future versions of this system. Moreover, it has identified bottlenecks components and replication needs for fulfilling business rules. The system, an integrated port operations management system, has been developed by Prodevelop, a medium-sized software enterprise with high expertise in geospatial technologies. The performance assessment has been carried out by a team composed by practitioners and researchers. Finally, the paper offers a deep discussion on the lessons learned during the experience, that will be useful for practitioners to adopt the methodology and for researcher to find new routes.

REFERENCIA: Simona Bernardi, Juan L. Domínguez, Abel Gómez, Christophe Joubert, José Merseguer, Diego Perez-Palacin, José I. Requeno, Alberto Romeu. A systematic approach for performance assessment using process mining: An industrial experience report. Empirical Software Engineering, 23 (6), pp. 3394–3441, 2018. https://doi.org/10.1007/s10664-018-9606-9
First Online: 21 March 2018. Publicado en diciembre 2018. Volumen 23, Issue 6, pp 3394–3441 (48 páginas). Disponible online en: http://rdcu.be/Jz3J

INDICIOS DE CALIDAD – EMPIRICAL SOFTWARE ENGINEERING: Factor de Impacto: 2.933 (IF 2017); Categoría, Posición y Cuartil: Computer Science, Software Engineering; 11/104 (Q1)

Internet of Things (IoT) has been paid increasingly attention by the government, academe and industry all over the world. One of the main drawbacks of the IoT systems is the amount of information they have to handle. This information arrives as events that need to be processed in real time in order to make correct decisions. Given that processing the data is crucial, testing the IoT systems that will manage that information is required. In order to test IoT systems, it is necessary to generate a huge number of events with specific structures and values to test the functionalities required by these systems. As this task is very hard and very prone to error if done by hand, this paper addresses the automated generation of appropriate events for testing. For this purpose, a general specification to define event types and its representation are proposed and an event generator is developed based on this definition. Thanks to the adaptability of the proposed specification, the event generator can generate events of an event type, or events which combine the relevant attributes of several event types. Results from experiments and real-world tests show that the developed system meets the demanded requirements.Journal of Systems and Software, JSS Special Issue on Software Reliability EngineeringImpact factor: 2,444 (Q1)Available online 20 June 2017DOI: https://doi.org/10.1016/j.jss.2017.06.037

Complex Event Processing (CEP) and Event-driven Architectures (EDA) are modern paradigms for processing data in form of events. Machine Learning (ML) methods offer additional sophisticated means for analyzing data. By combining these technologies it is possible to create even more comprehensive and powerful data analysis and processing systems. We analyze the feasibility of combining CEP and EDA with ML using the example of the application domain of computer networks. We present relevant aspects, a sample use case, an sample architecture, and results of performance benchmarks. Our results indicate that the combination of these technologies increases data processing capabilities and that it is feasible from a performance perspective as well.

Performance calculation is a key factor to match corporate goals between different partners in process execution. However, although, a number of standards protocols and languages have recently emerged to support business process services in the industry, there is no standard related to monitoring of performance indicators over processes in these systems. As a consequence, BPMS use propietary languages to define measures and calculate them over process execution. In this paper, we describe two different approaches to compute performance mea- sures on business process decoupled from specific Business Process Man- agement System (BPMS) with an existing BPMS-independent language (PPINOT) to define indicators over business processes. Finally, some optimization techniques are described to increase calculation performance based on computing aggregated measures incrementally.