Adapting Web Services for Multiple Devices: a Model-Driven, Aspect-Oriented Approach

Mobile devices have become an essential element in our daily lives, even for connecting to the Internet. Web Services have become extremely important when offering services through the Internet. However, current Web Services are very inflexible as regards their invocation from different types of device, especially if we consider the need for them to be adaptable when being invoked from a mobile device. In this paper, we will propose several alternatives for the creation of flexible web services which can be invoked from different types of device, and compare the different proposed approaches. Aspect -Oriented Programming and Model-Driven Development have been used in all proposals to reduce the impact of service adaption, not only for the service developer, but also to maintain the correct code structure. This work has been developed thanks to the support of MEC (contract TIN2008-02985)

Proceedings of the Doctoral Consortium in Computer Science (JIPII 2021)

Actas de las Jornadas de Investigación Predoctoral en Ingeniería InformáticaThis volume contains the proceedings of the Primeras Jornadas de Investigación Predoctoral en Ingeniería Informática - First Doctoral Consortium in Computer Science, JIPII 2021, which was held online on June 15th, 2021. The aim of JIPII 2021 was to provide a forum for PhD students to present and discuss their research under the guidance of a panel of senior researchers. The advances in their PhD theses under development in the Doctoral Program in Computer Science were presented in the Consortium. This Doctoral Program belongs to the Doctoral School of the University of Cadiz (EDUCA). Different stages of research were covered, from the most incipient phase, such as the PhD Thesis plans (or even a Master’s Thesis), to the most advanced phases in which the defence of the PhD Thesis is imminent. We enjoyed twenty very nice and interesting talks, organized in four sessions. We had a total of fifty participants, including speakers and attendees, with an average of thirty-two people in the morning sessions and an average of twenty people in the afternoon sessions. Several people contributed to the success of JIPII 2021. We are grateful to the Academic Committee of the Doctoral Program in Computer Science and the School of Engineering for their support. We would like also to thank the Program Committee for their work in reviewing the papers, as well as all the students and supervisors for their interest and participation. Finally, the proceedings have been published by the Department of Computer Science and Engineering. We hope that you find the proceedings useful, interesting, and challenging

MEdit4CEP: A model-driven solution for real-time decision making in SOA 2.0

Organizations all around the world need to manage huge amounts of data from heterogeneous sources every day in order to conduct decision making processes. This requires them to infer what the value of such data is for the business in question through data analysis as well as acting promptly for critical or relevant situations. Complex Event Processing (CEP) is a technology that helps tackle this issue by detecting event patterns in real time. However, this technology forces domain experts to define these patterns indicating such situations and the appropriate actions to be executed in their information systems, generally based on Service-Oriented Architectures (SOAs). In particular, these users face the incommodity of implementing these patterns manually or by using editors which are not user-friendly enough. To deal with this problem, a model-driven solution for real-time decision making in event-driven SOAs is proposed and conducted in this paper. This approach allows the integration of CEP with this architecture type as well as defining CEP domain and event pattern through a graphical and intuitive editor, which also permits automatic code generation. Moreover, the solution is evaluated and its benefits are discussed. As a result, we can assert this is a novel solution for bringing CEP technology closer to any user, positively impacting on business decision making processes

ModeL4CEP: Graphical domain-specific modeling languages for CEP domains and event patterns

Complex event processing (CEP) is a cutting-edge technology that allows the analysis and correlation of large volumes of data with the aim of detecting complex and meaningful events through the use of event patterns, as well as permitting the inference of valuable knowledge for end users. Despite the great advantages that CEP can bring to expert or intelligent business systems, it poses a substantial challenge to their users, who are business experts but do not have the necessary knowledge and experience using this technology. The main problem these users have to face is precisely hand-writing the code for event pattern definition, which requires them to implement the conditions to be met to detect relevant situations for the domain in question by using a particular event processing language (EPL). In order to respond to this need, in this paper we propose both a graphical domain-specific modeling language (DSML) for facilitating CEP domain definitions by domain experts, and a graphical DSML for event pattern definition by non-technological users. The proposed languages provide high expressiveness and flexibility and are independent of event patterns and actions’ implementation code. This way, domain experts can define the relevant event types and patterns within their business domain, without having to be experts on EPL programming, nor on other complicated computer science technological issues, beyond an understandable and intuitive graphical definition. Furthermore, with these DSMLs, users will also be able to define the actions to be automatically taken once a pattern is detected in the system. Further benefits of these DSMLs are evaluated and discussed in depth in this paper

La enseñanza de arquitecturas distribuidas en la universidad para satisfacer la demanda del internet de las cosas

Teaching of distributed architectures in the computer engineering degrees has traditionally been based on contents related to well-known established software paradigms and architectures. However, over the last few years, new solutions in the field of distributed architectures have emerged, especially within the field of the Internet of Things (IoT). In this article we tackle a methodology for teaching distributed architectures for the IoT from this new perspective. In this context, the description, implementation and testing of distributed architectures for IoT is therefore proposed. The methodology includes the development and testing of a case study relevant for the scope of IoT and smart cities using emerging technologies. In particular, in this paper we show the procedure followed to implement and test a case study related to traffic regulation and emergency vehicles movement, enabling such vehicles to reach their destination in the minimum amount of time.La enseñanza de las arquitecturas distribuidas en los estudios universitarios de ciencias de la computación o ingeniería informática se ha basado tradicionalmente en contenidos relacionados con paradigmas y arquitecturas de software bien conocidos y establecidos. Sin embargo, en los últimos años han surgido nuevas soluciones en el campo de las arquitecturas distribuidas, especialmente en el campo del Internet de las Cosas (IoT). En este artículo abordamos una metodología para enseñar arquitecturas distribuidas para el IoT desde esta nueva perspectiva. En este contexto, se propone, por tanto, la descripción, aplicación y prueba de arquitecturas distribuidas para el IoT. La metodología incluye el desarrollo y ensayo de un caso de estudio relevante en el ámbito del IoT y las ciudades inteligentes mediante el uso de tecnologías emergentes. En particular, en este artículo mostramos el procedimiento seguido para implementar y probar un caso de estudio relacionado con la regulación del tráfico y el desplazamiento de vehículos de emergencia, permitiendo que dichos vehículos lleguen a su destino en el menor tiempo posible

COLLECT: COLLaborativE ConText-aware service oriented architecture for intelligent decision-making in the Internet of Things

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, we are still faced with the challenge of providing a service oriented architecture, which facilitates the inclusion of data coming together from several IoT devices, data delivery among a system’s agents, real-time data processing and service provision to users. Furthermore, context-aware data processing and architectures still pose a challenge, in spite of being key requirements in order to get stronger IoT architectures. To face this challenge, we propose a COLLaborative ConText Aware Service Oriented Architecture (COLLECT), which facilitates both the integration of IoT heterogeneous domain context data — through the use of a light message broker — and easy data delivery among several agents and collaborative participants in the system — making use of an enterprise service bus —. In addition, this architecture provides real-time data processing thanks to the use of a complex event processing engine as well as services and intelligent decision-making procedures to users according to the needs of the domain in question. As a result, COLLECT has a great impact on context-aware decentralized and collaborative reasoning for IoT, promoting context-aware intelligent decision making in such scope. Since context-awareness is key for a wide range of recommender and intelligent systems, the presented novel solution improves decision making in a large number of fields where such systems require to promptly process a variety of ubiquitous collaborative and context-aware data

CARED-SOA: A Context-Aware Event-Driven Service-Oriented Architecture

Actualmente, la conciencia del contexto se ha vuelto esencial en las aplicaciones y servicios de software debido a la alta demanda de los usuarios, especialmente para las aplicaciones de computación móvil. Esta necesidad de proporcionar conciencia del contexto requiere una infraestructura de software no solo para recibir información de contexto, sino también para hacer uso de ella de manera que proporcione servicios ventajosos que se puedan personalizar según las necesidades del usuario. En este artículo, proporcionamos una arquitectura orientada a servicios impulsada por eventos respaldada por un bus de servicio empresarial, que facilitará la incorporación de datos de Internet de las Cosas y proporcionará servicios conscientes del contexto en tiempo real. El resultado, que ha sido validado a través de un estudio de caso del mundo real, es una arquitectura consciente del contexto escalable que se puede aplicar en un amplio espectro de dominios.Currently, context awareness has become essential in software applications and services owing to the high demand by users, especially for mobile computing applications. This need to provide context awareness requires a software infrastructure not only to receive context information but also to make use of it so that it provides advantageous services that may be customized according to user needs. In this paper, we provide an event-driven service-oriented architecture supported by an enterprise service bus, which will facilitate the incorporation of Internet of Things data and provide real-time context-aware services. The result, which has been validated through a real-world case study, is a scalable context-aware architecture which can be applied in a wide spectrum of domains"This work was supported in part by the Spanish Ministry of Science and Innovation and the European Union FEDER Funds under Project TIN2015-65845-C3-3-R and in part by the University of Cádiz under Project UCA PR2016-032

Integrating complex event processing and machine learning: An intelligent architecture for detecting IoT security attacks

The Internet of Things (IoT) is growing globally at a fast pace: people now find themselves surrounded by a variety of IoT devices such as smartphones and wearables in their everyday lives. Additionally, smart environments, such as smart healthcare systems, smart industries and smart cities, benefit from sensors and actuators interconnected through the IoT. However, the increase in IoT devices has brought with it the challenge of promptly detecting and combating the cybersecurity attacks and threats that target them, including malware, privacy breaches and denial of service attacks, among others. To tackle this challenge, this paper proposes 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.El Internet de las Cosas (IoT) está creciendo a nivel global a un ritmo acelerado: las personas ahora se encuentran rodeadas de una variedad de dispositivos IoT como smartphones y wearables en su vida cotidiana. Además, los entornos inteligentes, como los sistemas de atención médica inteligentes, las industrias inteligentes y las ciudades inteligentes, se benefician de sensores y actuadores interconectados a través del IoT. Sin embargo, el aumento de los dispositivos IoT ha traído consigo el desafío de detectar y combatir rápidamente los ataques y amenazas de ciberseguridad que los tienen como objetivo, incluyendo malware, violaciones de privacidad y ataques de denegación de servicio, entre otros. Para abordar este desafío, este documento propone una arquitectura inteligente que integra la tecnología de Procesamiento de Eventos Complejos (CEP) y el paradigma de Aprendizaje Automático (ML) con el fin de detectar diferentes tipos de ataques de seguridad en IoT en tiempo real. En particular, dicha arquitectura es capaz de gestionar fácilmente patrones de eventos cuyas condiciones dependen de los valores obtenidos por los algoritmos de ML. Además, se proporciona una herramienta gráfica impulsada por modelos para la definición de patrones de ataque de seguridad y la generación automática de código, ocultando toda la complejidad derivada de los detalles de implementación a los expertos del dominio. La arquitectura propuesta ha sido aplicada en el caso de una red de IoT de atención médica para validar su capacidad para detectar ataques realizados por dispositivos maliciosos. Los resultados obtenidos demuestran que esta arquitectura cumple satisfactoriamente sus objetivos.This work was supported by the Spanish Ministry of Science, Innovation and Universities and the European Union FEDER Funds [grant numbers FPU 17/02007, RTI2018-093608-B-C33, RTI2018- 098156-B-C52 and RED2018-102654-T]. This work was also sup- ported by the JCCM [grant number SB-PLY/17/180501/ 0 0 0353

Improving Resource Consumption in Context- Aware Mobile Applications Through Alternative Architectural Styles

Over the last years, the Internet of Things has fostered a growing interest in context-aware mobile applications; this fact is mainly due to highly favoring information provision from multiple Internetconnected devices. To identify user context, these applications collect information from the user and his/her environment and typically lter app information, so that the user receives only the interesting and relevant information. However, such a task usually implies further resource consumption on user mobile devices, not only regarding battery usage but also in terms of network traf c. Accordingly, although context-aware applications can improve user experiences in their daily lives, they must ensure the maintenance of lowlevel resource consumption; otherwise, the applications are promptly replaced by less consuming ones, and therefore, removed from the mobile market. In this paper, we evaluate and discuss several architectural styles for context-aware mobile applications, as well as, providing a set of guidelines to decide on the right architecture for a particular app depending on its characteristics. The use of such guidelines when choosing the right architectural style can strongly in uence the resource consumption of context-aware mobile applications. Following these guidelines, user satisfaction of a context-aware mobile application may be improved, thus guaranteeing the app success

Facilitating the Quantitative Analysis ofComplexEvents through a Computational Intelligence Model-Driven Tool

Complex event processing (CEP) is a computational intelligence technology capable of analyzing big data streams for event pattern recognition in real time. In particular, this technology is vastly useful for analyzing multicriteria conditions in a pattern, which will trigger alerts (complex events) upon their fulfillment. However, one of the main challenges to be faced by CEP is how to define the quantitative analysis to be performed in response to the produced complex events. In this paper, we propose the use of the MEdit4CEP-CPN model-driven tool as a solution for conducting such quantitative analysis of events of interest for an application domain, without requiring knowledge of any scientific programming language for implementing the pattern conditions. Precisely, MEdit4CEP-CPN facilitates domain experts to graphically model event patterns, transform them into a Prioritized Colored Petri Net (PCPN) model, modify its initial marking depending on the application scenario, and make the quantitative analysis through the simulation and monitor capabilities provided by CPN tools