chapter Share on Geospatial RDF Stores Author: Theofilos Ioannidis View Profile Authors Info & Claims Geospatial Data Science: A Hands-on Approach for Building Geospatial Applications Using Linked Data TechnologiesJune 2023Pages 221–240https://doi.org/10.1145/3581906.3581920Published:14 June 2023Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

We present Strabo 2, a distributed geospatial RDF store able to process GeoSPARQL queries over massive RDF datasets. Strabo 2 is based on robust technologies, able to scale on TBs of data distributed on hundreds of nodes. Specifically, we use the Spark framework, enhanced with the geospatial library SEDONA, for distributed in-memory processing on Hadoop clusters, and Hive for compact persistent storage of RDF data. Strabo 2 employs a flexible design that can store and partition thematic RDF data using different relational schemas, and spatial data in a separate Hive table, by taking into consideration the GeoSPARQL vocabulary. Strabo 2 is cluster friendly both memory and disk-wise, since it compresses triples using a partial encoding technique in addition to Parquet data file format compression schemes. GeoSPARQL queries are translated into the Spark SQL dialect, enhanced with the spatial functions and predicates offered by SEDONA. During this process the system takes into consideration SEDONA’s capabilities for both spatial selections and spatial joins, in order to apply optimizations that result in efficient query processing. We experimentally test Strabo 2 on an award winning Hadoop based cluster environment and exhibit Strabo 2 ’s excellent scalability while handling massive synthetic and real world datasets. We also show that Strabo 2 clearly outperforms state of the art centralized engines in a single server setup, once the dataset size increases beyond few GBs.

ExtremeEarth is a three-year H2020 ICT research and innovation project. Its main objective is to develop Artificial Intelligence and big data technologies that scale to the large volumes of big Copernicus data, information and knowledge, and apply these technologies in two of the European Space Agency (ESA) Thematic Exploitation Platforms (TEP): Food Security and Polar.

Geospatial extensions of SPARQL, like GeoSPARQL and stSPARQL, have been defined since 2007, and while several geospatial RDF stores have implemented a substantial part of these extensions, other stores limited their support mostly on point geometry features. A parallel process with the above was that RDF frameworks evolved in an interesting way by presenting a more mature set of geospatial features, such as GeoSPARQL support and including the latest indexing technologies. As a logical consequence, a shift in the use of RDF frameworks is to be expected, from base platforms that users extend to create more complete geospatial RDF stores, to attractive finished RDF solutions for many geospatial applications. Alongside with the ever-increasing size of linked geospatial data that semantic stores need to handle, all the above provided our group the motivation to improve our single-node systems benchmark Geographica, originally defined in 2013. Geographica 2 is more comprehensive, because it now includes new geospatial RDF stores and frameworks, big real-world datasets of many hundred million triples with up to 50 million features of complex geometries, new tests and queries that reveal the scalability of these systems. The augmented and revised real-world workload of Geographica 2 tests the efficiency of primitive spatial functions in RDF stores, their performance in the geocoding scenario against the new Census dataset in addition to many other real use case scenarios and finally includes computation of statistics for geospatial datasets. A more detailed and systematic evaluation is performed using the synthetic workload. The new scalability workload aims at discovering the limits of centralized geospatial RDF stores of various architectures. It employs a set of six well-balanced real-world datasets with highly complex geometries covering many European countries and compares three RDF stores in terms of storage space, bulk loading and query response time. In addition, a special version of the benchmark has been created for systems with limited geospatial functionality and two more systems of this category are introduced along the six systems of the main benchmark, all stressed against point-only subsets of the workloads. Three out of the eight systems use an RDBMS for the persistence layer, while some of them offer a variety of persistence options.

Copernicus is the European programme for monitoring the Earth. It consists of a set of systems that collect data from satellites and in-situ sensors, process this data and provide users with reliab ...

Large amounts of geospatial data have been made available recently on the linked open data cloud and on the portals of many national cartographic agencies (e.g., OpenStreetMap data, administrative geographies of various countries, or land cover/land use data sets). These datasets use various geospatial vocabularies and can be queried using SPARQL or its OGC-standardized extension GeoSPARQL. In this paper we go beyond these approaches to offer a question answering service on top of linked geospatial data sources. Our system has been implemented as re-usable components of the Qanary question answering architecture to provide benefits for future research tasks. We give a detailed description of the architecture of the system, its underlying algorithms and its evaluation using a set of 201 natural language questions.