Method of Constructing Point Generalization Constraints Based on the Cloud Platform
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F18%3A00103102" target="_blank" >RIV/00216224:14310/18:00103102 - isvavai.cz</a>
Result on the web
<a href="http://www.mdpi.com/2220-9964/7/7/235" target="_blank" >http://www.mdpi.com/2220-9964/7/7/235</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/ijgi7070235" target="_blank" >10.3390/ijgi7070235</a>
Alternative languages
Result language
angličtina
Original language name
Method of Constructing Point Generalization Constraints Based on the Cloud Platform
Original language description
As an important part of spatial data, the point feature has always been an essential element in web maps and navigation maps. With the development of location-based services and the rapid increase in volunteered geographic information and social media data, the amount of point data is increasing day by day, resulting in inevitable problems of overlay and congestion during visualization. Map generalization provides multiple algorithms that can be used to select, aggregate and make typification of points or point clusters. For the generalization of point data, however, the traditional stand-alone computing environment has difficulty with real-time realization. Currently, the rapid development of cloud computing technology provides a powerful support for improving the efficiency of map generalization. However, compared with the stand-alone environment, the data decomposition and the real-time display of point generalization in the cloud platform imposes higher requirements on the point generalization constraints, which play an important role in point-generalized process control. Based on the computational characteristics of the cloud platform, this paper analyzes the changes in point generalization constraints. In addition, our work proposes the constraints of point generalization based on the cloud platform and its construction method, builds a prototype system based on the Hadoop cloud platform. Our prototype system is tested using typical experimental data. Its efficiency and the quality of its results is examined. The results show that the efficiency and quality of point selection can be significantly improved by controlling the point generalization process with the generalization constraints in the cloud computing environment proposed in this paper. This paper provides a possible way for the realization of map generalization in the cloud computing environment. Its usability with real data and with many users accessing it will be the focus of further research.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10500 - Earth and related environmental sciences
Result continuities
Project
<a href="/en/project/LTACH17002" target="_blank" >LTACH17002: Dynamic mapping methods oriented to risk and disaster management in the era of big data</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
ISPRS International Journal of Geo-Information
ISSN
2220-9964
e-ISSN
—
Volume of the periodical
7
Issue of the periodical within the volume
7
Country of publishing house
CH - SWITZERLAND
Number of pages
18
Pages from-to
1-18
UT code for WoS article
000445150900002
EID of the result in the Scopus database
2-s2.0-85054029818