Abstract
Can we quickly explore large multidimensional data in main memory? Adaptive indexing responds to this need by building an index in crementally, in response to queries; in its default form, it indexes a single attribute or, in the presence of several attributes, one attributeper index level. Unfortunately, this approach falters when in dexing spatial data objects, encountered in data exploration tasks in volving multidimensional range queries. In this paper, we in traduce the Adaptive Incremental R-tree (AIR-tree): the first method for the adaptive indexing of non-point spatial objects; the AIR-tree in crementally and progressively constructs an in-memory spatialindex over a static array, in response to incoming queries, using a suite of heuristics for creating and splitting nodes. Our thoroughexperimental study on synthetic and real data and workloads shows that the AIR-tree consistently outperforms prior adaptive indexing methods focusing on multidimensional points and a pre-built stat icR-tree in cumulative time over at least the first thousand queries.
Original language | English |
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Journal | Proceedings of the VLDB Endowment |
Volume | 16 |
Issue | 9 |
Pages (from-to) | 2248-2260 |
Number of pages | 13 |
ISSN | 2150-8097 |
DOIs | |
Publication status | Published - 2023 |
Event | 49th International Conference on Very Large Data Bases, VLDB 2023 - Vancouver, Canada Duration: 28 Aug 2023 → 1 Sept 2023 |
Conference
Conference | 49th International Conference on Very Large Data Bases, VLDB 2023 |
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Country/Territory | Canada |
City | Vancouver |
Period | 28/08/2023 → 01/09/2023 |