Paving the Way for a Real-Time Context-Aware Predictive Architecture

Internet of Things society generates and needs to consume huge amounts of data in a demanding context-aware scenario. Such exponentially growing data sources require the use of novel processing methodologies, technologies and tools to facilitate data processing in order to detect and prevent situations of interest for the users in their particular context. To solve this issue, we propose an architecture which making use of emerging technologies and cloud platforms can process huge amounts of heterogeneous data and promptly alert users of relevant situations for a particular domain according to their context. Last, but not least, we will provide a graphical tool for domain experts to easily model, automatically generate code and deploy the situations to be detected and the actions to be taken in consequence. The proposal will be evaluated through a real case study related to air quality monitoring and lung diseases in collaboration with a doctor specialist on lung diseases of a public hospital

Dataset for a Microservice Architecture for Real-time IoT Data Processing: a Reusable Web of Things Approach for Smart Ports

This dataset provides the JAR files for the Smart Port microservices together with the EPL schema and patterns for the case study, the EPL schema and patterns used for the performance evaluation and the data collected from such evaluation for the paper entitled "A Microservice Architecture for Real-time IoT Data Processing: a Reusable Web of Things Approach for Smart Ports": - Smart Port microservices: it includes the JAR files for the three microservices (SmartPortTransformers, SmartPortCEP and SmartPortActions), the instructions for their deployment (readme.md) and the schema and patterns defined for the case study (SmartPortSchemaAndPatterns.txt).- Performance EPL schema and patterns: it includes the Esper EPL schema and patterns defined both for the short performance tests as well as for the long ones.- Performance evaluation results: it includes the spreadsheets response time values obtained from every performance test both for the short performance tests as well as for the long ones.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Dataset for paper MEdit4CEP-Gam: A Model-driven Approach for User-friendly Gamification Design, Monitoring and Code Generation in CEP-based Systems

The video shows step-by-step how to use MEdit4CEP-Gam

Dataset for CEPchain: A graphical model-driven solution for integrating complex event processing and blockchain

This dataset contains the files used and obtained in the vaccine delivery case study, which was conducted to test our CEPchain model-driven solution

Dataset for Atmosphere: Context and Situational-Aware Collaborative IoT Architecture for Edge-Fog-Cloud Computing

This dataset provides the event patterns for the case study of respiratory disease surveillance in hospitals proposed in the paper "Atmosphere: Context and Situational-Aware Collaborative IoT Architecture for Edge-Fog-Cloud Computing". These patterns were graphically modeled and automatically implemented through the MEdit4CEP tool (https://ucase.uca.es/medit4cep/). Additionally, it provides the results obtained during the performance evaluation

Dataset for the Paper MEdit4CEP-CPN: An Approach for Complex Event Processing Modeling by Prioritized Colored Petri Nets

These files should be considered as additional material for the paper "MEdit4CEP-CPN: An Approach for Complex Event Processing Modeling by Prioritized Colored Petri Nets". Its aim is to show how our MEdit4CEP-CPN approach works

Dataset for the Paper MEdit4CEP-CPN: An Approach for Complex Event Processing Modeling by Prioritized Colored Petri Nets

These files should be considered as additional material for the paper "MEdit4CEP-CPN: An Approach for Complex Event Processing Modeling by Prioritized Colored Petri Nets". Its aim is to show how our MEdit4CEP-CPN approach works

Dataset for paper: Real-time automatic analysis of temporal information in sensor networks

The event pattern implementation and the results of the whole sensor network described in the paper "Real-time automatic analysis of temporal information in sensor networks

Dataset for Paper: Un Editor Textual para el Modelado y la Generación de Código de Patrones de Eventos

El procesamiento de eventos complejos (CEP) es una tecnología que permite analizar y correlacionar grandes cantidades de datos con el propósito de detectar situaciones de interés en tiempo real. Para ello se requiere implementar patrones de eventos, especificando las condiciones que deben cumplirse para detectar dichas situaciones, con los lenguajes de procesamiento de eventos (EPL). A pesar de que los usuarios suelen tener un vasto conocimiento en el dominio para el que se necesitan definir ciertos patrones de eventos, suelen ser inexpertos tanto en EPL como en el lenguaje requerido para implementar las acciones a llevar a cabo tras la detección de los patrones. En este artículo presentamos un editor textual para el modelado y la generación de código de los patrones de eventos que se necesiten detectar en un dominio de aplicación. Gracias a este editor, el usuario solo tendrá que conocer un lenguaje textual para definir patrones de eventos, que podrán ser posteriormente transformados automáticamente al EPL soportado por el motor CEP en cuestión. Este editor complementa a MEdit4CEP, un editor que permite la definición gráfica e intuitiva de patrones sin necesidad de conocer ningún lenguaje de programación en particular