| dc.contributor.author | Ptošek, Vít | |
| dc.contributor.author | Slaninová, Kateřina | |
| dc.date.accessioned | 2018-12-17T12:38:03Z | |
| dc.date.available | 2018-12-17T12:38:03Z | |
| dc.date.issued | 2019 | |
| dc.identifier.citation | Advances in Intelligent Systems and Computing. 2019, vol. 897, p. 91-104. | cs |
| dc.identifier.isbn | 978-981-13-3249-4 | |
| dc.identifier.isbn | 978-981-13-3250-0 | |
| dc.identifier.issn | 2194-5357 | |
| dc.identifier.issn | 2194-5365 | |
| dc.identifier.uri | http://hdl.handle.net/10084/133435 | |
| dc.description.abstract | OpenStreetMap (OSM) is a popular collaborative open-source project that offers free editable map across the whole world. However, this data often needs a further on-purpose processing to become the utmost valuable information to work with. That is why the main motivation of this paper is to propose a design for big data processing along with data mining leading to the obtaining of statistics with a focus on the detail of a traffic data as a result in order to create graphs representing a road network. To ensure our High-Performance Computing (HPC) platform routing algorithms work correctly, it is absolutely essential to prepare OSM data to be useful and applicable for above-mentioned graph, and to store this persistent data in both spatial database and HDF5 format. | cs |
| dc.format.extent | 674220 bytes | |
| dc.format.mimetype | application/pdf | |
| dc.language.iso | en | cs |
| dc.publisher | Springer | cs |
| dc.relation.ispartofseries | Advances in Intelligent Systems and Computing | cs |
| dc.relation.uri | http://doi.org/10.1007/978-981-13-3250-0_7 | cs |
| dc.rights | © Springer Nature Singapore Pte Ltd. 2019 | cs |
| dc.subject | OpenStreetMap | cs |
| dc.subject | road network quality | cs |
| dc.subject | big data parsing | cs |
| dc.subject | multi-node processing | cs |
| dc.subject | ETL | cs |
| dc.subject | state machine | cs |
| dc.subject | pipeline | cs |
| dc.title | Multi-node approach for map data processing | cs |
| dc.type | conference paper | cs |
| dc.identifier.doi | 10.1007/978-981-13-3250-0_7 | |
| dc.rights.access | openAccess | cs |
| dc.type.version | publishedVersion | cs |
| dc.type.status | Peer-reviewed | cs |
| dc.description.source | Web of Science | cs |
| dc.description.volume | 897 | cs |
| dc.description.lastpage | 104 | cs |
| dc.description.firstpage | 91 | cs |