Semantic Mediation of Environmental Observation Datasets through Sensor Observation Services

A large volume of environmental observation data is being generated as a result of the observation of many properties at the Earth surface. In parallel, there exists a clear interest in accessing data from different data providers related to the same property, in order to solve concrete problems. Based on such fact, there is also an increasing interest in publishing the above data through open interfaces in the scope of Spatial Data Infraestructures. There have been important advances in the definition of open standards of the Open Geospatial Consortium (OGC) that enable interoperable access to sensor data. Among the proposed interfaces, the Sensor Observation Service (SOS) is having an important impact. We have realized that currently there is no available solution to provide integrated access to various data sources through a SOS interface. This problem shows up two main facets. On the one hand, the heterogeneity among different data sources has to be solved. On the other hand, semantic conflicts that arise during the integration process must also resolved with the help of relevant domain expert knowledge. To solve the problems, the main goal of this thesis is to design and develop a semantic data mediation framework to access any kind of environmental observation dataset, including both relational data sources and multidimensional arrays

An Integrated Smart City Platform

Smart Cities aim to create a higher quality of life for their citizens, improve business services and promote tourism experience. Fostering smart city innovation at local and regional level requires a set of mature technologies to discover, integrate and harmonize multiple data sources and the exposure of eective applications for end-users (citizens, administrators, tourists...). In this context, Semantic Web technologies and Linked Open Data principles provide a means for sharing knowledge about cities as physical, economical, social, and technical systems, enabling the development of smart city services. Despite the tremendous effort these communities have done so far, there exists a lack of comprehensive and effective platforms that handle the entire process of identication, ingestion, consumption and publication of data for Smart Cities. In this paper, a complete open-source platform to boost the integration, semantic enrichment, publication and exploitation of public data to foster smart cities in local and national administrations is proposed. Starting from mature software solutions, we propose a platform to facilitate the harmonization of datasets (open and private, static and dynamic on real time) of the same domain generated by dierent authorities. The platform provides a unied dataset oriented to smart cities that can be exploited to offer services to the citizens in a uniform way, to easily release open data, and to monitor services status of the city in real time by means of a suite of web applications

Smart Environmental Data Infrastructures: Bridging the Gap between Earth Sciences and Citizens

The monitoring and forecasting of environmental conditions is a task to which much effort and resources are devoted by the scientific community and relevant authorities. Representative examples arise in meteorology, oceanography, and environmental engineering. As a consequence, high volumes of data are generated, which include data generated by earth observation systems and different kinds of models. Specific data models, formats, vocabularies and data access infrastructures have been developed and are currently being used by the scientific community. Due to this, discovering, accessing and analyzing environmental datasets requires very specific skills, which is an important barrier for their reuse in many other application domains. This paper reviews earth science data representation and access standards and technologies, and identifies the main challenges to overcome in order to enable their integration in semantic open data infrastructures. This would allow non-scientific information technology practitioners to devise new end-user solutions for citizen problems in new application domainsThis research was co-funded by (i) the TRAFAIR project (2017-EU-IA-0167), co-financed by the Connecting Europe Facility of the European Union, (ii) the RADAR-ON-RAIA project (0461_RADAR_ON_RAIA_1_E) co-financed by the European Regional Development Fund (ERDF) through the Iterreg V-A Spain-Portugal program (POCTEP) 2014-2020, and (iii) the Consellería de Educación, Universidade e Formación Profesional of the regional government of Galicia (Spain), through the support for research groups with growth potential (ED431B 2018/28)S

Integrative Research: The EuroGEOSS Experience

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Enhancing Water Quality Data Service Discovery And Access Using Standard Vocabularies

There is a growing need for consistency across the publishing, discovering, integrating and access to scientific datasets, such as water quality data. Such datasets may have varying formats and service interfaces. The Network Common Data Form (NetCDF) is both a software package and a data format for producing array-oriented scientific data, which is commonly used to exchange data, including water quality data. NetCDF datasets are also published through service interfaces using the THREDDS data server. Alternatively water quality datasets can be encoded with standard XML formats such as WaterML 2.0, which can be published with services such as the Open Geospatial Consortium (OGC) community\u27s Web Feature Service interface standard (WFS). However, appropriate interpretation of the content, discovery and interoperability of data depends on common models, schemas and vocabularies, though these may not always be available. Using the water quality vocabulary we have developed, formalized using the Resource Description Framework (RDF) language, and published as Linked Data, we demonstrate the use of such standard vocabularies in existing data services for providing service capability metadata. We also present methods for augmenting existing metadata fields for water quality data specifically in formats such as NetCDF, WaterML 2.0 using standard vocabularies. We show how using standard vocabularies that are encoded and published using semantic technologies can enhance discovery, integration and access to existing data services delivering water quality datasets

A service-oriented middleware for integrated management of crowdsourced and sensor data streams in disaster management

The increasing number of sensors used in diverse applications has provided a massive number of continuous, unbounded, rapid data and requires the management of distinct protocols, interfaces and intermittent connections. As traditional sensor networks are error-prone and difficult to maintain, the study highlights the emerging role of “citizens as sensors” as a complementary data source to increase public awareness. To this end, an interoperable, reusable middleware for managing spatial, temporal, and thematic data using Sensor Web Enablement initiative services and a processing engine was designed, implemented, and deployed. The study found that its approach provided effective sensor data-stream access, publication, and filtering in dynamic scenarios such as disaster management, as well as it enables batch and stream management integration. Also, an interoperability analytics testing of a flood citizen observatory highlighted even variable data such as those provided by the crowd can be integrated with sensor data stream. Our approach, thus, offers a mean to improve near-real-time applications

Semantic linking of complex properties, monitoring processes and facilities in web-based representations of the environment

Where a virtual representation of the Earth must contain data values observed within the physical Earth system, data models are required that allow the integration of data across the silos of various Earth and environmental sciences domains. Creating a mapping between the well-defined terminologies of these silos is a stubborn problem. This paper presents a generalised ontology for use within Web 3.0 services, which builds on European Commission spatial data infrastructure models. The presented ontology acknowledges that there are many complexities to the description of environmental properties which can be observed within the physical Earth system. The ontology is shown to be flexible and robust enough to describe concepts drawn from a range of Earth science disciplines, including ecology, geochemistry, hydrology and oceanography. This paper also demonstrates the alignment and compatibility of the ontology with existing systems and shows applications in which the ontology may be deployed

Streaming MASSIF : cascading reasoning for efficient processing of iot data streams

In the Internet of Things (IoT), multiple sensors and devices are generating heterogeneous streams of data. To perform meaningful analysis over multiple of these streams, stream processing needs to support expressive reasoning capabilities to infer implicit facts and temporal reasoning to capture temporal dependencies. However, current approaches cannot perform the required reasoning expressivity while detecting time dependencies over high frequency data streams. There is still a mismatch between the complexity of processing and the rate data is produced in volatile domains. Therefore, we introduce Streaming MASSIF, a Cascading Reasoning approach performing expressive reasoning and complex event processing over high velocity streams. Cascading Reasoning is a vision that solves the problem of expressive reasoning over high frequency streams by introducing a hierarchical approach consisting of multiple layers. Each layer minimizes the processed data and increases the complexity of the data processing. Cascading Reasoning is a vision that has not been fully realized. Streaming MASSIF is a layered approach allowing IoT service to subscribe to high-level and temporal dependent concepts in volatile data streams. We show that Streaming MASSIF is able to handle high velocity streams up to hundreds of events per second, in combination with expressive reasoning and complex event processing. Streaming MASSIF realizes the Cascading Reasoning vision and is able to combine high expressive reasoning with high throughput of processing. Furthermore, we formalize semantically how the different layers in our Cascading Reasoning Approach collaborate

Geospatial information infrastructures to address spatial needs in health: Collaboration, challenges and opportunities

Most health-related issues such as public health outbreaks and epidemiological threats are better understood from a spatial–temporal perspective and, clearly demand related geospatial datasets and services so that decision makers may jointly make informed decisions and coordinate response plans. Although current health applications support a kind of geospatial features, these are still disconnected from the wide range of geospatial services and datasets that geospatial information infrastructures may bring into health. In this paper we are questioning the hypothesis whether geospatial information infrastructures, in terms of standards-based geospatial services, technologies, and data models as operational assets already in place, can be exploited by health applications for which the geospatial dimension is of great importance. This may be certainly addressed by defining better collaboration strategies to uncover and promote geospatial assets to the health community. We discuss the value of collaboration, as well as the opportunities that geographic information infrastructures offer to address geospatial challenges in health applications

Hydrologic Information Systems: Advancing Cyberinfrastructure for Environmental Observatories

Recently, community initiatives have emerged for the establishment of large-scale environmental observatories. Cyberinfrastructure is the backbone upon which these observatories will be built, and scientists\u27 ability to access and use the data collected within observatories to address research questions will depend on the successful implementation of cyberinfrastructure. The research described in this dissertation advances the cyberinfrastructure available for supporting environmental observatories. This has been accomplished through both development of new cyberinfrastructure components as well as through the demonstration and application of existing tools, with a specific focus on point observations data. The cyberinfrastructure that was developed and deployed to support collection, management, analysis, and publication of data generated by an environmental sensor network in the Little Bear River environmental observatory test bed is described, as is the sensor network design and deployment. Results of several analyses that demonstrate how high-frequency data enable identification of trends and analysis of physical, chemical, and biological behavior that would be impossible using traditional, low-frequency monitoring data are presented. This dissertation also illustrates how the cyberinfrastructure components demonstrated in the Little Bear River test bed have been integrated into a data publication system that is now supporting a nationwide network of 11 environmental observatory test bed sites, as well as other research sites within and outside of the United States. Enhancements to the infrastructure for research and education that are enabled by this research are impacting a diverse community, including the national community of researchers involved with prospective Water and Environmental Research Systems (WATERS) Network environmental observatories as well as other observatory efforts, research watersheds, and test beds. The results of this research provide insight into and potential solutions for some of the bottlenecks associated with design and implementation of cyberinfrastructure for observatory support