ANSWERING GEOSPARQL QUERIES OVER RELATIONAL DATA

In this paper we present the system Ontop-spatial that is able to answer GeoSPARQL queries on top of geospatial relational databases, performing on-the-fly GeoSPARQL-to-SQL translation using ontologies and mappings. GeoSPARQL is a geospatial extension of the query language SPARQL standardized by OGC for querying geospatial RDF data. Our approach goes beyond relational databases and covers all data that can have a relational structure even at the logical level. Our purpose is to enable GeoSPARQL querying on-the-fly integrating multiple geospatial sources, without converting and materializing original data as RDF and then storing them in a triple store. This approach is more suitable in the cases where original datasets are stored in large relational databases (or generally in files with relational structure) and/or get frequently updated

A Global Constraint for a Tractable Class of Temporal Optimization Problems

International audienceThis paper is originally motivated by an application where the objective is to generate a video summary, built using intervals extracted from a video source. In this application, the constraints used to select the relevant pieces of intervals are based on Allen's algebra. The best state-of-the-art results are obtained with a small set of ad hoc solution techniques, each specific to one combination of the 13 Allen's relations. Such techniques require some expertise in Constraint Programming. This is a critical issue for video specialists. In this paper, we design a generic constraint, dedicated to a class of temporal problems that covers this case study, among others. ExistAllen takes as arguments a vector of tasks, a set of disjoint intervals and any of the 2 13 combinations of Allen's relations. ExistAllen holds if and only if the tasks are ordered according to their indexes and for any task at least one relation is satisfied , between the task and at least one interval. We design a propagator that achieves bound-consistency in O(n + m), where n is the number of tasks and m the number of intervals. This propagator is suited to any combination of Allen's relations, without any specific tuning. Therefore, using our framework does not require a strong expertise in Constraint Programming. The experiments, performed on real data, confirm the relevance of our approach

Evaluating Conjunctive Triple Pattern Queries over Large Structured Overlay Networks

We study the problem of evaluating conjunctive queries com- posed of triple patterns over RDF data stored in distributed hash tables. Our goal is to develop algorithms that scale to large amounts of RDF data, distribute the query processing load evenly and incur little network traffic. We present and evaluate two novel query processing algorithms with these possibly conflicting goals in mind. We discuss the various tradeoffs that occur in our setting through a detailed experimental eval- uation of the proposed algorithms

SexTant: Visualizing Time-Evolving Linked Geospatial Data

We present SexTant, a Web-based system for the visualization and exploration of time-evolving linked geospatial data and the creation, sharing, and collaborative editing of "temporally-enriched" thematic maps which are produced by combining dierent sources of such data

From big data to big information and big knowledge: The case of Earth observation data

Some particularly important rich sources of open and free big geospatial data are the Earth observation (EO) programs of various countries such as the Landsat program of the US and the Copernicus programme of the European Union. EO data is a paradigmatic case of big data and the same is true for the big information and big knowledge extracted from it. EO data (satellite images and in-situ data), and the information and knowledge extracted from it, can be utilized in many applications with financial and environmental impact in areas such as emergency management, climate change, agriculture and security

GeoTriples: a Tool for Publishing Geospatial Data as RDF Graphs Using R2RML Mappings

In this paper we present the tool GeoTriples that allows the transformation of Earth Observation data and geospatial data into RDF graphs, by using and extending the R2RML mapping language to be able to deal with the specificities of geospatial data. GeoTriples is a semi-automated tool that transforms geospatial information into RDF following the state of the art vocabularies like GeoSPARQL and stSPARQL, but at the same time it is not tightly coupled to a specific vocabulary

Sextant: Visualizing time-evolving linked geospatial data

The linked open data cloud is constantly evolving as datasets get continuously updated with newer versions. As a result, representing, querying, and visualizing the temporal dimension of linked data is crucial. This is especially important for geospatial datasets that form the backbone of large scale open data publication efforts in many sectors of the economy (e.g., the public sector, the Earth Observation sector). Although there has been some work on the representation and querying of linked geospatial data that change over time, to the best of our knowledge, there is currently no tool that offers spatio-temporal visualization of such data. This is in contrast with the existence of many tools for the visualization of the temporal evolution of geospatial data in the GIS area. In this article, we present Sextant, a Web-based system for the visualization and exploration of time-evolving linked geospatial data and the creation, sharing, and collaborative editing of “temporally-enriched” thematic maps which are produced by combining different sources of such data. We present the architecture of Sextant, give examples of its use and present applications in which we have deployed it

GeoTriples: Transforming geospatial data into RDF graphs using R2RML and RML mappings

A lot of geospatial data has become available at no charge in many countries recently. Geospatial data that is currently made available by government agencies usually do not follow the linked data paradigm. In the few cases where government agencies do follow the linked data paradigm (e.g., Ordnance Survey in the United Kingdom), specialized scripts have been used for transforming geospatial data into RDF. In this paper we present the open source tool GeoTriples which generates and processes extended R2RML and RML mappings that transform geospatial data from many input formats into RDF. GeoTriples allows the transformation of geospatial data stored in raw files (shapefiles, CSV, KML, XML, GML and GeoJSON) and spatially-enabled RDBMS (PostGIS and MonetDB) into RDF graphs using well-known vocabularies like GeoSPARQL and stSPARQL, but without being tightly coupled to a specific vocabulary. GeoTriples has been developed in European projects LEO and Melodies and has been used to transform many geospatial data sources into linked data. We study the performance of GeoTriples experimentally using large publicly available geospatial datasets, and show that GeoTriples is very efficient and scalable especially when its mapping processor is implemented using Apache Hadoop