| dc.creator | Roumelis G., Vassilakopoulos M., Corral A., Manolopoulos Y. | en |
| dc.date.accessioned | 2023-01-31T09:52:03Z | |
| dc.date.available | 2023-01-31T09:52:03Z | |
| dc.date.issued | 2016 | |
| dc.identifier | 10.1007/978-3-319-45547-1_5 | |
| dc.identifier.isbn | 9783319455464 | |
| dc.identifier.issn | 03029743 | |
| dc.identifier.uri | http://hdl.handle.net/11615/78584 | |
| dc.description.abstract | Spatial indexes are important in spatial databases for efficient execution of queries involving spatial constraints. The xBR+-tree is a balanced disk-resident quadtree-based index structure for point data, which is very efficient for processing such queries. Bulk-loading refers to the process of creating an index from scratch as a whole, when the dataset to be indexed is available beforehand, instead of creating (loading) the index gradually, when the dataset items are available one-by-one. In this paper, we present an algorithm for bulk-loading xBR+-trees for big datasets residing on disk, using a limited amount of RAM. Moreover, using real and artificial datasets of various cardinalities, we present an experimental comparison of this algorithm vs. the algorithm loading items one-by-one, regarding performance (I/O and execution time) and the characteristics of the xBR+-trees created. We also present experimental results regarding the efficiency of bulk-loaded xBR+-trees vs. xBR+-trees where items are loaded one-by-one for query processing. © Springer International Publishing Switzerland 2016. | en |
| dc.language.iso | en | en |
| dc.source | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | en |
| dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988646215&doi=10.1007%2f978-3-319-45547-1_5&partnerID=40&md5=4f51b275232169251d771911fd926d66 | |
| dc.subject | Big data | en |
| dc.subject | Forestry | en |
| dc.subject | Query languages | en |
| dc.subject | Query processing | en |
| dc.subject | Trees (mathematics) | en |
| dc.subject | Artificial datasets | en |
| dc.subject | Bulk loading | en |
| dc.subject | Experimental comparison | en |
| dc.subject | Index structure | en |
| dc.subject | Spatial constraints | en |
| dc.subject | Spatial database | en |
| dc.subject | Spatial indexes | en |
| dc.subject | XBR+-trees | en |
| dc.subject | Loading | en |
| dc.subject | Springer Verlag | en |
| dc.title | Bulk-loading xBR+-trees | en |
| dc.type | conferenceItem | en |
