Adaptive Indexing of Objects with Spatial Extent

Fatemeh Zardbani, Nikos Mamoulis, Stratos Idreos, Panagiotis Karras

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperConference articleResearchpeer-review

8 Citations (Scopus)

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 languageEnglish
JournalProceedings of the VLDB Endowment
Volume16
Issue9
Pages (from-to)2248-2260
Number of pages13
ISSN2150-8097
DOIs
Publication statusPublished - 2023
Event49th International Conference on Very Large Data Bases, VLDB 2023 - Vancouver, Canada
Duration: 28 Aug 20231 Sept 2023

Conference

Conference49th International Conference on Very Large Data Bases, VLDB 2023
Country/TerritoryCanada
CityVancouver
Period28/08/202301/09/2023

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