MiR-EO: Middleware Reflexivo para la Emergencia Ontológica en Ambientes Inteligentes

In a Smart Environment (AmI), the devices that participate must exchange knowledge permanently, for which they must understand and manage a common language. The ontologies in an AmI are an ideal tool for this, making possible the communication between the intelligent objects that are part of the environment. These ontologies must be distributed, heterogeneous and dynamic, since they must adapt to the changes, needs and services of the AmI. This article proposes the implementation of a middleware that allows the ontological emergence, to manage all the knowledge that can be generated in an AmI. This middleware, called MiR-EO, is implemented as a reflective middleware, which manages its own ontological framework, made up of meta-ontologies that model the elements that must contain the ontologies of an AmI, andenables the ontological emergence process.  En un Ambiente Inteligente (AmI), los dispositivos que participan deben intercambiar conocimiento permanentemente, para lo cual deben entenderse y manejar un lenguaje común, para el logro de la interoperabilidad semántica. Las ontologías en un AmI constituyen una herramienta ideal para ello, posibilitando la comunicación entre los objetos inteligentes que forman parte del ambiente. Estas ontologías deben ser distribuidas, heterogéneas y dinámicas ya que deben adaptarse a los cambios, necesidades y servicios del AmI. Este artículo propone la implementación de un middleware que permite la emergencia ontológica, con el fin de gestionar todo el conocimiento que se puede generar en un AmI. El middleware, llamado MiR-EO, se implementa como un middleware reflexivo, que maneja su propio marco ontológico, conformado por meta-ontologías que modelan los elementos que deben contener las ontologías de un AmI, y posibilitan el proceso de emergencia ontológica. &nbsp

Proliferating Cloud Density through Big Data Ecosystem, Novel XCLOUDX Classification and Emergence of as-a-Service Era

Big Data is permeating through the bigger aspect of human life for scientific and commercial dependencies, especially for massive scale data analytics of beyond the exabyte magnitude. As the footprint of Big Data applications is continuously expanding, the reliability on cloud environments is also increasing to obtain appropriate, robust and affordable services to deal with Big Data challenges. Cloud computing avoids any need to locally maintain the overly scaled computing infrastructure that include not only dedicated space, but the expensive hardware and software also. Several data models to process Big Data are already developed and a number of such models are still emerging, potentially relying on heterogeneous underlying storage technologies, including cloud computing. In this paper, we investigate the growing role of cloud computing in Big Data ecosystem. Also, we propose a novel XCLOUDX {XCloudX, X…X} classification to zoom in to gauge the intuitiveness of the scientific name of the cloud-assisted NoSQL Big Data models and analyze whether XCloudX always uses cloud computing underneath or vice versa. XCloudX symbolizes those NoSQL Big Data models that embody the term “cloud” in their name, where X is any alphanumeric variable. The discussion is strengthen by a set of important case studies. Furthermore, we study the emergence of as-a-Service era, motivated by cloud computing drive and explore the new members beyond traditional cloud computing stack, developed over the last few years