The importance of geo-spatial data in critical applications such as emergency response, transportation, agriculture etc., has prompted the adoption of recent GeoSPARQL standard in many RDF processing engines. In addition to large repositories of geo-spatial data -- e.g., LinkedGeoData, OpenStreetMap, etc. -- spatial data is also routinely found in automatically constructed knowledgebases such as Yago and WikiData. While there have been research efforts for efficient processing of spatial data in RDF SPARQL, very little effort has gone into building end-to-end systems that can holistically handle complex SPARQL queries along with spatial filters. In this paper, we present Streak, a RDF data management system that is designed to support a wide-range of queries with spatial filters including complex joins, top-k, higher-order relationships over spatially enriched databases. Streak introduces various novel features such as a careful identifier encoding strategy for spatial and non-spatial entities, the use of a semantics-aware Quad-tree index that allows for early-termination and a clever use of adaptive query processing with zero plan-switch cost. We show that Streak can scale to some of the largest publicly available semantic data resources such as Yago3 and LinkedGeoData which contain spatial entities and quantifiable predicates useful for result ranking. For experimental evaluations, we focus on top-k distance join queries and demonstrate that Streak outperforms popular spatial join algorithms as well as state of the art end-to-end systems like Virtuoso and PostgreSQL.
Parliament @cite_13 is a spatial RDF engine built on top of Jena @cite_4 . Parliament supports GeoSPARQL features and uses R-tree as the spatial index. Additional techniques to enhance spatial queries, as adopted by Geo-store @cite_44 and in RDF engine proposed by @cite_17 involve encoding spatial data by employing Hilbert space-filling curves for preserving spatial locality.
@cite_44 @cite_13 @cite_4 @cite_17
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The RDF data model has recently been extended to support representation and querying of spatial information (i.e., locations and geometries), which is associated with RDF entities. Still, there are limited efforts towards extending RDF stores to efficiently support spatial queries, such as range selections (e.g., find entities within a given range) and spatial joins (e.g., find pairs of entities whose locations are close to each other). In this paper, we propose an extension for RDF stores that supports efficient spatial data management. Our contributions include an effective encoding scheme for entities having spatial locations, the introduction of on-the-fly spatial filters and spatial join algorithms, and several optimizations that minimize the overhead of geometry and dictionary accesses. We implemented the proposed techniques as an extension to the opensource RDF-3X engine and we experimentally evaluated them using real RDF knowledge bases. The results show that our system offers robust performance for spatial queries, while introducing little overhead to the original query engine. As the amount of Linked Open Data on the web increases, so does the amount of data with an inherent spatial context. Without spatial reasoning, however, the value of this spatial context is limited. Over the past decade there have been several vocabularies and query languages that attempt to exploit this knowledge and enable spatial reasoning. These attempts provide varying levels of support for fundamental geospatial concepts. GeoSPARQL, a forthcoming OGC standard, attempts to unify data access for the geospatial Semantic Web. As authors of the Parliament triple store and contributors to the GeoSPARQL specification, we are particularly interested in the issues of geospatial data access and indexing. In this paper, we look at the overall state of geospatial data in the Semantic Web, with a focus on GeoSPARQL. We first describe the motivation for GeoSPARQL, then the current state of the art in industry and research, followed by an example use case, and finally our implementation of GeoSPARQL in the Parliament triple store. A common approach to providing persistent storage for RDF is to store statements in a three-column table in a relational database system. This is commonly referred to as a triple store. Each table row represents one RDF statement. For RDF graphs with frequent patterns, an alternative storage scheme is a property table. A property table comprises one column containing a statement subject plus one or more columns containing property values for that subject. In this approach, a single table row may store many RDF statements. This paper describes a property table design and implementation for Jena, an RDF Semantic Web toolkit. A design goal is to make Jena property tables look like normal relational database tables. This enables relational database tools such as loaders, report writers and query optimizers to work well with Jena property tables. This paper includes a basic performance analysis comparing a triple store with property tables for dataset load time and query response time. Depending on the application and data characteristics, Jena property tables may provide a performance advantage compared to a triple store for large RDF graphs with frequent patterns. The disadvantage is some loss in flexibility. The semantic web data and the SPARQL query language allow users to write precise queries. However, the lack of spatial information limits the use of the semantic web data on position-oriented query. In this paper, we introduce spatial SPARQL, a variant of SPARQL language, for querying spatial information integrated RDF data. Besides, we design a novel index SS-tree for evaluating the spatial queries. Based on the index, we propose a search algorithm. The experimental results show the effectiveness and the efficiency of our approach.