Deductive Databases: Implementation, Parallelism and Applications

Soares, Tiago

doi:10.1007/11799573_52

Cited by 2 publications

(1 citation statement)

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“…reduction of excess information, -reduction or elimination of redundant information, -reduction of the volume of datasets stored in databases, -compensation for measurement errors, -reduction of the number of points describing a surface, -stepless regulation of grid resolution and adaptation of grid resolution to the morphology of the analyzed surface, -increasing node resolution on the examined surface for greater accuracy, -performance of analyses in consecutive measurement epochs at various locations with different density of measurement points, -performance of comparative analyses at the same node points which are determined mathematically, -acceleration of data modeling and visualization, -optimization of algorithms for faster processing of digital data, -simplification of directional (profile-related) data modeling and analysis, -greater clarity and transparency of the spatial organization of data, -precise arrangement and organization of the topological structure of stored data, -more convenient access to the required information in the data exploration process, -convenient and efficient data archiving, -multi-epoch archiving and modeling data for a specific time period, -higher efficiency of spatial data processing in real-time, -higher efficiency of database management systems (DBMS) [30], -acceleration of spatial data mining (SDM) [27,29], -acceleration of data transfer between spatial information systems (SIS) [31], -increasing the speed of access to data in internet spatial data servers (ISDS) [32], -simplification of the structure of recording and reading data in spatial database (SDB) models [33,34], -acceleration of spatial online application processing (SOLAP) [35], -increasing the data processing capacity of dynamic SIS (DSIS) [26].…”

Section: Introductionmentioning

confidence: 99%

A GRID-Based Spatial Interpolation Method as a Tool Supporting Real Estate Market Analyses

Szczepańska

Gościewski

Gerus-Gościewska

2020

IJGI

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The spatial distribution of prices is closely linked with the urban real estate market. Property prices are one of the key indicators of economic activity because they influence economic decisions. Decision-makers and consumers often need information about the spatial distribution of prices, but spatial-temporal analyses of the real estate market are based on the prices quoted in different locations across years (epochs). Due to this idiosyncrasy, the resulting datasets are dispersed (different across years) and difficult to compare. For this reason, the existing interpolation methods are not always effective in analyses of the real estate market. A different approach to interpolating real estate prices that supports the generation of continuous interpolated surfaces while maintaining the values of measurement points is thus needed. This paper proposes a method for replacing dispersed spatial data with a regular GRID structure. The GRID structure covers the measured object with a regular network of nodes, which supports uniform interpolation at every point of the analyzed space and a comparison of interpolation models in successive epochs (years). The proposed method was tested on a selected object. The results indicate that the GRID structure can be used in analyses of highly complex real estate markets where input data are incomplete, irregular and dispersed.

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Section: Introductionmentioning

confidence: 99%