SREQP: A Solar Radiation Extraction and Query Platform for the Production and Consumption of Linked Data from Weather Stations Sensors

Table 1

Comparison of analyzed models and architectures for sensor Web data.

Research	Model or architecture as alternative solution	SREQP
A great amount of scientific data are not stored in the cloud or on the Web, but in multi-institutional data centers [12].	Scenario focused on the use of ontologies and formal machine-readable descriptions of domain to facilitate search and discovery of this scientific data.	Focused on the extraction, dissemination on Linked Data format, and analysis of solar radiation data from pyranometers.
Authors addressed important challenges such as the increase of sensor streams (Internet of Things) and the observation and measurement that data provided via these streams [13].	Semantic modeling scheme, a naming convention, and a data distribution mechanism for sensor streams.	The design of a conceptual data model inspired by the Linked Data principles [17] with the aim of generating a set of RDF triples containing information about solar radiation data extracted from multiple sources, such as sensors, platforms, and databases.
Authors considered that sensor networks are a major source of information for the vision of Digital Earth, which requires more dynamic information systems and stronger capabilities for their integration [14].	Linked Data model and RESTful proxy for the OGC’s Sensor Observation Service to improve integration and interlinkage of data observation for the Digital Earth.
Authors considered that it was necessary to overcome current problems of information integration and direct geographical information to the next decade scenario, that is, “Linked Digital Earth” [15].	Process based on Linked Data principles to combine different sources (heterogeneous, multidisciplinary, multitemporal, multiresolution, and multilingual) to enable the integration of geographical information.
A language needed to be developed to help overcome key issues that users experience with SensorML language [16].	StarFL language presents a more restrictive approach and incorporates concepts from the recently published Semantic Sensor Network Ontology.
Authors addressed the benefits of using SSN ontology produced by the W3C Semantic Sensor Network Incubator Group [18].	The SSN ontology can describe sensors sensing the measurement capabilities of other sensors, resulting observations, and deployments in which sensors are used.	An internal taxonomy that extends SSN and AWS ontologies by providing concrete subclasses and detailing aspects, observations, and measures of sensors.
Authors analyzed challenges in the Internet of Things architecture and those concerning application of large-scale sensor networks, federating sensor networks, sensors data and related context capturing techniques, as well as sensors cloud-based management [19].	Data streams coming from connected devices to the Internet of Things will challenge the traditional approaches to data management and will contribute to the emerging paradigm of Big Data.	It is architecture to extract solar radiation information from different external sources and merge it on a single and unique platform inspired by the Linked Data principles [17]. Furthermore, this initiative provides access to solar radiation data stored in a Linked Data Repository through a SPARQL endpoint to query all data stored in the platform.
Sensors in the physical world should be connected into a network to detect and measure various physical phenomena (e.g., temperature, humidity, and pollution). Then, they should be introduced as Web resources to the end-users [20].	A SOA-based sensor Web architecture providing an easy approach to integrate sensor providers’ services with information provider services and enable the users to access it as a single, integrated, and searchable service.
Authors believed that an infrastructural platform should be designed to enable the integration of semantic-based sensor networks [21].	A new architecture design that enabled the integration of semantic-based sensor networks. This architecture provided a scalable platform capable of supporting huge amounts of sensor data and large numbers of users.