Jackpine: A benchmark to evaluate spatial database performance

Ray, Suprio; Simion, Bogdan; Brown, Angela Demke

doi:10.1109/icde.2011.5767929

Cited by 57 publications

(35 citation statements)

References 16 publications

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“…This use case is in line with the concept of the map browsing macro benchmark scenario within the Jackpine spatial database benchmark methodology [6], which consists of a series of queries fetching geometries inside bounding boxes. Covering the study area in full was necessary so that the benchmark is comprehensive in terms of reflecting LU/LC regional variability.…”

Section: Use Casementioning

confidence: 80%

Should EU land use and land cover data be managed with a NOSQL document store?

Navarro-Carrión¹,

Zaragozí²,

Ramon-Morte³

et al. 2016

Int. J. DNE

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Land cover (LC) is a scientific landscape classification based on physical properties of earth materials. This information is usually retrieved through remote sensing techniques (e.g. forest cover, urban, clay content, among others). In contrast, Land use (LU) is defined from an anthropocentric point of view. It describes how a specific area is used (e.g. it is usual to indicate whether a territory supports an intensive, extensive use or it is unused). Both geospatial layers are essential inputs in many socio-economic and environmental studies. The INSPIRE directive provides technical data specifications for harmonization and sharing of voluminous LU/ LC datasets across all countries of the EU. The INSPIRE initiative proposes Object-Oriented Modelling as a data modelling methodology. However, the most used Geographic Information Systems (GIS) are built upon relational databases. This may jeopardize LU/LC data usability, since GIS practitioners will eventually face the object-relational impedance mismatch. In this paper, the authors introduce the SIOSE database (Spanish Land Cover and Land Use Information System), which was the first implementation of an object-oriented land cover and Land-use datamodel, in line with the recommendation of the INSPIRE Directive, separating both themes. SIOSE data can be downloaded as relational database files, where information describing each single LU/LC object is divided among several related tables, so database queries can be complex and time consuming. The authors show these technical complexities through a computational experience, comparing SQL and NoSQL databases for querying spatial data downloaded from SIOSE. Finally, the authors conclude that NoSQL geodatabases deserve to be further explored because they could scale for LU/LC data, both horizontally and vertically, better than relational geodatabases, improving usability and making the most of the EU harmonization efforts. Keywords: Land Use, Land Cover, document store, SIOSE, geodatabase, PostgreSQL, jsonb LAND USE AND LAND COVER DATABASES IN THE EULand is a limited resource and its mismanagement is one of the main drivers of global change, with significant effects on ecosystem functions, goods and services [1]. There are complex environmental problems such as the over-exploitation of natural resources, biodiversity loss or climate change that require a long-term management perspective of natural resources. Many studies agree that these problems can be aggravated by land-use changes, so it is mandatory to monitor and apply long-term management policies at different scales [2].Land use (LU) and land cover (LC) information at national and regional level has been historically recorded in many EU Member States because of their environmental and territorial management's needs and requirements. In addition to Corine Land Cover (CLC) 1990 databases, many EU countries have been producing LC databases to manage and satisfy their requirements on environmental, agricultural, forest and land planning issues. As a conse...

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Section: Use Casementioning

confidence: 80%

Should EU land use and land cover data be managed with a NOSQL document store?

Navarro-Carrión¹,

Zaragozí²,

Ramon-Morte³

et al. 2016

Int. J. DNE

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“…Comparing Algorithm two with the Oracle® Spatial results [14] which use raster loading of an image and translation of that image, the creation of the spatial indices were considerably faster. By adapting the software to allow the loading of KML files for processing and using the TIGER (Topologically Integrated Geographic Encoding and Referencing) 5 workloads as reference data sets, a comparison of the load times, as shown in Table V, with the results in [15] for some of the other currently popular Spatial Databases could be achieved. The algorithms were also repeated over several other maps including the US, Greenland and Russia, as illustrated in Table VI.…”

Section: Analysis and Resultsmentioning

confidence: 99%

“…However, for R-tree requires that the device has prior knowledge of the R-tree (for comparison on server side). Samet [14] and Zhang et al [15] proposed methods of speeding up the Quadtree processing, but this typically involves large specification machines and is not suitable for mobile devices.…”

Section: Introductionmentioning

confidence: 99%

Location-aware alert system for mobile devices

Sibley

Trestian

Muntean

2013

2013 IEEE International Conference on Communications (ICC)

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This version is available at: http://eprints.mdx.ac.uk/12133/ Copyright:Middlesex University Research Repository makes the University's research available electronically.Copyright and moral rights to this work are retained by the author and/or other copyright owners unless otherwise stated. The work is supplied on the understanding that any use for commercial gain is strictly forbidden. A copy may be downloaded for personal, non-commercial, research or study without prior permission and without charge.Works, including theses and research projects, may not be reproduced in any format or medium, or extensive quotations taken from them, or their content changed in any way, without first obtaining permission in writing from the copyright holder(s). They may not be sold or exploited commercially in any format or medium without the prior written permission of the copyright holder(s).Full bibliographic details must be given when referring to, or quoting from full items including the author's name, the title of the work, publication details where relevant (place, publisher, date), pagination, and for theses or dissertations the awarding institution, the degree type awarded, and the date of the award.If you believe that any material held in the repository infringes copyright law, please contact the Repository Team at Middlesex University via the following email address:eprints@mdx.ac.ukThe item will be removed from the repository while any claim is being investigated. Abstract-Being able to react fast to campaign events such as missing persons or disaster preventions, is of paramount importance. In these situations narrowing down the search area to a targeted and accurate location is imperative. Nowadays, modern mobile devices have the location awareness capabilities that can be used to determine the users Global Positioning System (GPS) coordinates. However in order to determine if a user is located within a specific area, complex floating point calculations are required. Moreover if the area is determined by a polygon, this calculation is further complicated. In this paper we propose a novel algorithm which makes use of spatial indices to determine if a mobile is located within a predefined polygon shape area. The algorithm determines the optimal length of the spatial index such as to ensure accuracy-processing time-memory trade-off. We build a prototype system, using free and open source software, to deliver alerts to mobile devices within a predetermined geographical area. The system is assessed in terms of accuracy, processing time and memory usage.

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“…The most common standard RDBMS benchmarks are TCP-(C, H, E) benchmarks [22] but there are also other database benchmarks like Bristlecone or Open Source Development Lab Data Base Test Suite (OSDL-DBTS) [23]. These benchmarks attempt to simulate real-world scenarios mostly in the business domain applications and none of these standard database benchmarks can be used for this research.…”

Section: Database Benchmarksmentioning

confidence: 99%

“…There are only a few spatial database benchmark projects, especially ones detailed as TCP benchmarks used in standard RDBMS. One of the most recent and versatile benchmarks is Jackpine [23]. Jackpine is a vector-based spatial database benchmark based on Bristlcone benchmark.…”

Section: Database Benchmarksmentioning

confidence: 99%