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.
Another framework, S-Store @cite_36 , extends state-of-the-art RDF store gStore @cite_33 , by indexing data based on their structure. Subsequently, queries are accelerated by pruning based on both spatial and semantic constraints in the query. Yet another spatiotemporal storage framework is g @math -Store @cite_2 , which is an extension of gStore @cite_33 . It processes spatial queries using its tree-style index ST-tree, with a top-down search algorithm.
@cite_36 @cite_33 @cite_2
2400703691 2095542621 84343632 1770795006
In this paper, we present a spatiotemporal information integrated RDF data management system, called g st -Store. In g st -Store, some entities have spatiotemporal features, and some statements have valid time intervals and occurring locations. We introduce some spatiotemporal assertions into the SPARQL query language to answer the spatiotemporal range queries and join queries. Some examples are listed to demonstrate our demo. The techniques of utilizing spatial data on the Semantic Web have attracted more and more interest from researchers due to the rapidly increasing applications based on geographic information. However, there are currently limited solutions providing efficient spatial query evaluation based on Semantic Web data. In this demonstration, we present Geo-Store, a novel spatially-augmented SPARQL evaluation system. By extending the standard SPARQL query language with spatial query filters, Geo-Store is able to process complex spatial queries with common spatial constraints. These spatial filters are designed based on our Spatially Aware Mapping (SAM) scheme. With SAM, spatial data are pre-processed and encoded with their Hilbert values by employing the Hilbert curve, resulting in more efficient spatial query processing than the existing approaches. The Geo-Store demonstration includes both a server and a web browser-based client. Triple stores have long provided RDF storage as well as data access using expressive, formal query languages such as SPARQL. The new end users of the Semantic Web, however, are mostly unaware of SPARQL and overwhelmingly prefer imprecise, informal keyword queries for searching over data. At the same time, the amount of data on the Semantic Web is approaching the limits of the architectures that provide support for the full expressivity of SPARQL. These factors combined have led to an increased interest in semantic search, i.e. access to RDF data using Information Retrieval methods. In this work, we propose a method for effective and efficient entity search over RDF data. We describe an adaptation of the BM25F ranking function for RDF data, and demonstrate that it outperforms other state-of-the-art methods in ranking RDF resources. We also propose a set of new index structures for efficient retrieval and ranking of results. We implement these results using the open-source MG4J framework. 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.