Fast and Adaptive Bulk Loading of Multidimensional Points
Fuente:
arXiv
Guardado en:
| Autores principales: | , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866910603932598272 |
|---|---|
| author | Moti, Moin Hussain Papadias, Dimitris |
| author_facet | Moti, Moin Hussain Papadias, Dimitris |
| contents | Existing methods for bulk loading disk-based multidimensional points involve multiple applications of external sorting. In this paper, we propose techniques that apply linear scan, and are therefore significantly faster. The resulting FMBI Index possesses several desirable properties, including almost full and square nodes with zero overlap, and has excellent query performance. As a second contribution, we develop an adaptive version AMBI, which utilizes the query workload to build a partial index only for parts of the data space that contain query results. Finally, we extend FMBI and AMBI to parallel bulk loading and query processing in distributed systems. An extensive experimental evaluation with real datasets confirms that FMBI and AMBI clearly outperform competitors in terms of combined index construction and query processing cost, sometimes by orders of magnitude. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_09447 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fast and Adaptive Bulk Loading of Multidimensional Points Moti, Moin Hussain Papadias, Dimitris Databases Existing methods for bulk loading disk-based multidimensional points involve multiple applications of external sorting. In this paper, we propose techniques that apply linear scan, and are therefore significantly faster. The resulting FMBI Index possesses several desirable properties, including almost full and square nodes with zero overlap, and has excellent query performance. As a second contribution, we develop an adaptive version AMBI, which utilizes the query workload to build a partial index only for parts of the data space that contain query results. Finally, we extend FMBI and AMBI to parallel bulk loading and query processing in distributed systems. An extensive experimental evaluation with real datasets confirms that FMBI and AMBI clearly outperform competitors in terms of combined index construction and query processing cost, sometimes by orders of magnitude. |
| title | Fast and Adaptive Bulk Loading of Multidimensional Points |
| topic | Databases |
| url | https://arxiv.org/abs/2409.09447 |