The development of the Internet of Things (IoT) is closely related to a considerable increase in the number and variety of devices connected to the Internet. Sensors have become a regular component of our environment, as well as smart phones and other devices that continuously collect data about our lives even without our intervention. With such connected devices, a broad range of applications has been developed and deployed, including those dealing with massive volumes of data. In this paper, we introduce a Distributed Data Service (DDS) to collect and process data for IoT environments. One central goal of this DDS is to enable multiple and distinct IoT middleware systems to share common data services from a loosely-coupled provider. In this context, we propose a new specification of functionalities for a DDS and the conception of the corresponding techniques for collecting, filtering and storing data conveniently and efficiently in this environment. Another contribution is a data aggregation component that is proposed to support efficient real-time data querying. To validate its data collecting and querying functionalities and performance, the proposed DDS is evaluated in two case studies regarding a simulated smart home system, the first case devoted to evaluating data collection and aggregation when the DDS is interacting with the UIoT middleware, and the second aimed at comparing the DDS data collection with this same functionality implemented within the Kaa middleware.
Modeling is one of the most important steps in developing a database. In traditional databases, the Entity Relationship (ER) and Unified Modeling Language (UML) models are widely used. But how are NoSQL databases being modeled? We performed a systematic mapping review to answer three research questions to identify and analyze the levels of representation, models used, and contexts where the modeling process occurred in the main categories of NoSQL databases. We found 54 primary studies where we identified that conceptual and logical levels received more attention than the physical level of representation. The UML, ER, and new notation based on ER and UML were adapted to model NoSQL databases, in the same way, formats such as JSON, XML, and XMI were used to generate schemas through the three levels of representation. New contexts such as benchmark, evaluations, migration, and schema generation were identified, as well as new features to be considered for modeling NoSQL databases, such as the number of records by entities, CRUD operations, and system requirements (availability, consistency, or scalability). Additionally, a coupling and co-citation analysis was carried out to identify relevant works and researchers.
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