Deep integration of spatial query processing into native RDF triple stores

Abstract

Semantic Web technologies, most notably RDF, are wellsuited to cope with typical challenges in spatial data management including analyzing complex relations between entities, integrating heterogeneous data sources and exploiting poorly structured data, e.g., from web communities. Also, RDF can easily represent spatial relationships, as long as the location information is symbolic, i.e., represented by places that have a name. What is widely missing is support for geographic and geometric information, such as coordinates or spatial polygons, which is needed in many applications that deal with sensor data or map data. This calls for efficient data management systems which are capable of querying large amounts of RDF data and support spatial query predicates. We present a native RDF triple store implementation with deeply integrated spatial query functionality. We model spatial features in RDF as literals of a complex geometry type and express spatial predicates as SPARQL filter functions on this type. This makes it possible to use W3C's standardized SPARQL query language as-is, i.e., without any modifications or extensions for spatial queries. We evaluate the characteristics of our system on very large data volumes.

Strabon: A Semantic Geospatial DBMS

“…For both workloads, we compare the response time of Strabon on top of PostgreSQL (called Strabon PG from now on) with our closest competitor implementation in [3], the naive, baseline implementation described in Section 4, and the RDF store Parliament. To identify potential benefits from using a different DBMS as a relational backend for Strabon, we also executed the SQL queries produced by Strabon in a proprietary spatially-enabled DBMS (which we will call System X, and Strabon X the resulting combination).…”

“…The implementation of [3] is not a complete system like Strabon and does not support a full-fledged query language such as stSPARQL. In addition, the only way to load data in the system is the use of a generator which has been especially designed for the experiments of [3] thus it cannot be used to load other datasets in the implementation. Moreover, the geospatial indexing support of this implementation is limited to spatial selections.…”

“…These works have formed the basis for GeoSPARQL, a proposal for an Open Geospatial Consortium (OGC) standard which is currently at the "candidate standard" stage [1]. In addition, a number of papers have explored implementation issues for such languages [2,3]. In this paper we present our recent achievements in both of these research directions and make the following technical contributions.…”

“…Strabon can scale up to 500 million triples and answer complex stSPARQL queries involving the whole range of constructs offered by the language. We present our findings in Section 5, including a comparison between Strabon on top of PostgreSQL and a proprietary DBMS, the implementation presented in [3], the system Parliament [2], and a baseline implementation. Thus, we are the first to provide a systematic evaluation of RDF stores supporting languages like stSPARQL and GeoSPARQL and pointing out directions for future research in this area.…”

Investigating The Use Of Semantic Technologies In Spatial Mapping Applications

“…For instance, the effort in (Brodt et al 2010) reports on the implementation of a triple store that natively integrates spatial query processing (deep integration). In their implementation, spatial query predicates of the OpenGIS specification are supported via SPARQL filtering.…”