VESPA: A Benchmark for Vector Spatial Databases

Paton, Norman W.; Williams, M. Howard; Dietrich, Kurt; Liew, O.; Dinn, Andrew; Patrick, Anubhav

doi:10.1007/3-540-45033-5_7

Cited by 16 publications

(24 citation statements)

References 14 publications

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“…Base type in PostgreSQL "generally correspond to what are often known as abstract data types." 4 No matter whether the user defined type itself has structure or not, "internally, PostgreSQL regards base type as a 'blob of memory'." 5 That is to say, users can not access or operate a part of base type with SQL statements, and the only way to operate on such types is through user defined functions.…”

Section: Disadvantages In Current Spatial Databases Based On Postgresqlmentioning

confidence: 99%

“…Suppose huanghe is a linestring object that represents the Yellow River. We can access the 4th point of it with an expression such as "huanghe.point [4]". To extend geometry types with the array of composite types can expose the inner structure of the spatial data.…”

Section: Use Arrays Of Composite Types To Register Geometry Types In mentioning

confidence: 99%

“…PostgreSQL is a completely open source ORDBMS, which is released under a BSD-style license by the University of California at Berkeley (Paton, 2000). Supporting user-defined data types and functions is one of the key characters of ORDB (Stonebraker and Brown, 1999).…”

Section: Introductionmentioning

confidence: 99%

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On defining structured geometry types in PostgreSQL to implement a spatial database

Huang

Gao

2009

SPIE Proceedings

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PostgreSQL is a widely used open source object-relational database system, on which the PostGIS spatial database is built. By defining geometry types in PostgreSQL according to the OGC WKB standard, geographic features are usually defined as a base type which is stored as general binary data blocks without internal structures. But the unstructured representation of the structured geometry data model brings the problem of inefficient data access. In this paper, we propose a new approach that uses an array of structured composite types to replace the unstructured base types to store geometry types. Then PostgreSQL 8.3 is extended to support spatial data management in this proposed approach following the standard of OGC's Simple Feature Specification. A contrastive experiment proves the advantages and efficiency of this method.

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Section: Disadvantages In Current Spatial Databases Based On Postgresqlmentioning

confidence: 99%

Section: Use Arrays Of Composite Types To Register Geometry Types In mentioning

confidence: 99%

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On defining structured geometry types in PostgreSQL to implement a spatial database

Huang

Gao

2009

SPIE Proceedings

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“…VESPA [20] is a vector based spatial database benchmark that includes a range of query and update tasks that could be executed over synthetic data sets. It was used to compare PostgreSQL with the Rock & Roll deductive object oriented database [21].…”

Section: B Spatial Database Benchmarksmentioning

confidence: 99%

Jackpine: A benchmark to evaluate spatial database performance

Ray

Simion

Brown

2011

2011 IEEE 27th International Conference on Data Engineering

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The volume of spatial data generated and consumed is rising exponentially and new applications are emerging as the costs of storage, processing power and network bandwidth continue to decline. Database support for spatial operations is fast becoming a necessity rather than a niche feature provided by a few products. However, the spatial functionality offered by current commercial and open-source relational databases differs significantly in terms of available features, true geodetic support, spatial functions and indexing. Benchmarks play a crucial role in evaluating the functionality and performance of a particular database, both for application users and developers, and for the database developers themselves. In contrast to transaction processing, however, there is no standard, widely used benchmark for spatial database operations.In this paper, we present a spatial database benchmark called Jackpine. Our benchmark is portable (it can support any database with a JDBC driver implementation) and includes both micro benchmarks and macro workload scenarios. The micro benchmark component tests basic spatial operations in isolation; it consists of queries based on the Dimensionally Extended 9-intersection model of topological relations and queries based on spatial analysis functions. Each macro workload includes a series of queries that are based on a common spatial data application. These macro scenarios include map search and browsing, geocoding, reverse geocoding, flood risk analysis, land information management and toxic spill analysis. We use Jackpine to evaluate the spatial features in 2 open source databases and 1 commercial offering.

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“…The above advances to the state of the art in query languages and implemented systems has not so far been matched with much work on the evaluation and benchmarking of implemented geospatial RDF stores. Although there are various benchmarks for spatially-enabled RDBMS [17,13,3,14,15,11], there is only one paper in the literature that proposes a benchmark for geospatial data expressed in RDF [5]. However, since this work has preceded the proposal of GeoSPARQL and stSPARQL, it does not cover much of the features available in these languages.…”

Section: Introductionmentioning

confidence: 99%

Geographica: A Benchmark for Geospatial RDF Stores (Long Version)

Garbis

Kyzirakos

Koubarakis

2013

Advanced Information Systems Engineering

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Geospatial extensions of SPARQL like GeoSPARQL and stSPARQL have recently been defined and corresponding geospatial RDF stores have been implemented. However, there is no widely used benchmark for evaluating geospatial RDF stores which takes into account recent advances to the state of the art in this area. In this paper, we develop a benchmark, called Geographica, which uses both real-world and synthetic data to test the offered functionality and the performance of some prominent geospatial RDF stores.

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VESPA: A Benchmark for Vector Spatial Databases

Cited by 16 publications

References 14 publications

On defining structured geometry types in PostgreSQL to implement a spatial database

On defining structured geometry types in PostgreSQL to implement a spatial database

Jackpine: A benchmark to evaluate spatial database performance

Geographica: A Benchmark for Geospatial RDF Stores (Long Version)

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