A model of provenance applied to biodiversity datasets

Nowadays, the Web has become one of the main sources of biodiversity information. An increasing number of biodiversity research institutions add new specimens and their related information to their biological collections and make this information available on the Web. However, mechanisms which are currently available provide insufficient provenance of biodiversity information. In this paper, we propose a new biodiversity provenance model extending the W3C PROV Data Model. Biodiversity data is mapped to terms from relevant ontologies, such as Dublin Core and GeoSPARQL, stored in triple stores and queried using SPARQL endpoints. Additionally, we provide a use case using our provenance model to enrich collection data

On Reasoning with RDF Statements about Statements using Singleton Property Triples

The Singleton Property (SP) approach has been proposed for representing and querying metadata about RDF triples such as provenance, time, location, and evidence. In this approach, one singleton property is created to uniquely represent a relationship in a particular context, and in general, generates a large property hierarchy in the schema. It has become the subject of important questions from Semantic Web practitioners. Can an existing reasoner recognize the singleton property triples? And how? If the singleton property triples describe a data triple, then how can a reasoner infer this data triple from the singleton property triples? Or would the large property hierarchy affect the reasoners in some way? We address these questions in this paper and present our study about the reasoning aspects of the singleton properties. We propose a simple mechanism to enable existing reasoners to recognize the singleton property triples, as well as to infer the data triples described by the singleton property triples. We evaluate the effect of the singleton property triples in the reasoning processes by comparing the performance on RDF datasets with and without singleton properties. Our evaluation uses as benchmark the LUBM datasets and the LUBM-SP datasets derived from LUBM with temporal information added through singleton properties

Geospatial Data Management Research: Progress and Future Directions

Without geospatial data management, today´s challenges in big data applications such as earth observation, geographic information system/building information modeling (GIS/BIM) integration, and 3D/4D city planning cannot be solved. Furthermore, geospatial data management plays a connecting role between data acquisition, data modelling, data visualization, and data analysis. It enables the continuous availability of geospatial data and the replicability of geospatial data analysis. In the first part of this article, five milestones of geospatial data management research are presented that were achieved during the last decade. The first one reflects advancements in BIM/GIS integration at data, process, and application levels. The second milestone presents theoretical progress by introducing topology as a key concept of geospatial data management. In the third milestone, 3D/4D geospatial data management is described as a key concept for city modelling, including subsurface models. Progress in modelling and visualization of massive geospatial features on web platforms is the fourth milestone which includes discrete global grid systems as an alternative geospatial reference framework. The intensive use of geosensor data sources is the fifth milestone which opens the way to parallel data storage platforms supporting data analysis on geosensors. In the second part of this article, five future directions of geospatial data management research are presented that have the potential to become key research fields of geospatial data management in the next decade. Geo-data science will have the task to extract knowledge from unstructured and structured geospatial data and to bridge the gap between modern information technology concepts and the geo-related sciences. Topology is presented as a powerful and general concept to analyze GIS and BIM data structures and spatial relations that will be of great importance in emerging applications such as smart cities and digital twins. Data-streaming libraries and “in-situ” geo-computing on objects executed directly on the sensors will revolutionize geo-information science and bridge geo-computing with geospatial data management. Advanced geospatial data visualization on web platforms will enable the representation of dynamically changing geospatial features or moving objects’ trajectories. Finally, geospatial data management will support big geospatial data analysis, and graph databases are expected to experience a revival on top of parallel and distributed data stores supporting big geospatial data analysis

Geospatial Workflows and Trust: a Use Case for Provenance

At first glance the Astronomer by Vermeer, Tutankhamun’s burial mask, and a geospatial workflow may appear to have nothing in common. However, a commonality exists; each of these items can have a record of provenance detailing their history. Provenance is a record that shows who did what to an object, where this happened, and how and why these actions took place. In relation to the geospatial domain, provenance can be used to track and analyze the changes data has undergone in a workflow, and can facilitate scientific reproducibility. Collecting provenance from geospatial workflows and finding effective ways to use this provenance is an important application. When using geospatial data in a workflow it is important to determine if the data and workflow used are trustworthy. This study examines whether provenance can be collected from a geospatial workflow. Each workflow examined is a use case for a specific type of geospatial problem. In addition to this, the collected provenance is then used to determine workflow trust and content trust for each of the workflows examined in this study. The results of this study determined that provenance can be collected from a geospatial workflow in such a way as to be of use to additional applications, such as provenance interchange. From this collected provenance, content trust and workflow trust can be estimated. The simple workflow had a content trust value of .83 (trustworthy) and a workflow trust value of .44 (untrustworthy). Two additional workflows were examined for content trust and workflow trust. The methods used to calculate content trust and workflow trust could also be expanded to other types of geospatial data and workflows. Future research could include complete automation of the provenance collection and trust calculations, as well as examining additional techniques for deciding trust in relation to workflows

Generación y publicación de Linked Data para el monitoreo de la calidad ambiental del agua

El agua dulce resulta un recurso de vital importancia para la salud humana, la sostenibilidad del medio ambiente y la prosperidad económica. Por tanto, hace necesario el desarrollo de sistemas de información que permitan el monitoreo de este recurso, teniendo en cuenta aspectos como cantidad y calidad. Para facilitar su monitoreo se propone adoptar Linked Data, que consiste en un conjunto de buenas prácticas que no solo buscan la publicación de información estructurada en la Web, sino que también apuesta por la interconexión entre distintas fuentes de datos, estandarización e interoperabilidad. La adopción de estas buenas prácticas (Linked Data), también involucran el uso de vocabularios (ontologías), para permitir dar significado a la información contenida y que ésta se convierta en conocimiento una vez se ha puesto en contexto. De igual manera, la adopción de estas buenas prácticas brinda soporte para la interoperabilidad, especialmente semántica, de los sistemas con información geoespacial de tipo ambiental (contaminación hídrica). Este trabajo propone una solución que permita poner dentro del contexto legal vigente información correspondiente a características físico-químicas y microbiológicas de cuerpos hídricos y, al mismo tiempo, facilitar la conexión de estos datos con otras fuentes de información mediante los principios de Linked Data. Asimismo, esta propuesta busca promover el aprovechamiento de ontologías existentes e, incluso, de recursos no ontológicos, conforme a las recomendaciones presentes en el estado del arte, para llevar a cabo la definición del tipo de agua, datos y usos asociados, lo que conducirá al desarrollo de un caso de estudio para la interpretación eficiente de los datos hídricos de la cuenca del río Bogotá, indicando la peligrosidad y el potencial del uso del agua de acuerdo a la legislación que le aplique en el contexto requerido y su uso.Abstract: Fresh water is a resource of vital importance for human health, the sustainability of the environment and economic prosperity. Therefore, it is necessary to develop information systems that allow the monitoring of this resource, taking into account aspects such as quantity and quality. In order to facilitate its monitoring, we propose to adopt Linked Data, which consists of a set of good practices that not only seek the publication of structured information on the Web, but also achieves interconnection between different data sources, standardization and interoperability. The adoption of these good practices (Linked Data), also involves the use of vocabularies (ontologies), to allow giving meaning to the contained information and that this becomes knowledge once it has been put into context. In the same way, the adoption of these good practices provides support for the interoperability, especially semantics, of the systems with geospatial information of environmental type (water pollution). This work proposes a solution that allows putting within the current legal context information corresponding to physical-chemical and microbiological characteristics of water bodies and, at the same time, facilitate the connection of these data with other sources of information through the guidelines of Linked Data. In addition, this proposal seeks to promote the use of existing ontologies and even non-ontological resources, in accordance with the recommendations present in the state of the art, to carry out the definition of the type of water, data and associated uses. This scenario is developed in a case study for the efficient interpretation of water data of the Bogotá river basin, indicating the danger and potential of water use according to the regulation that applies to it in the required context and its use.Maestrí