Fast and Adaptive Indexing of Multi-Dimensional Observational Data
Summary: An adaptive index for streaming multidimensional observations models successive points as hyperspace line segments rather than bounding boxes. It prioritizes high-throughput ingestion, then refines asynchronously to reduce over-coverage and improve query performance. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Sheng Wang (National University of Singapore)
- 2. David Maier (Portland State University)
- 3. Beng Chin Ooi (National University of Singapore)
BibTeX Citation
@article{wang_vldb16,
title = {{Fast and Adaptive Indexing of Multi-Dimensional Observational Data}},
author = {Wang, Sheng and Maier, David and Ooi, Beng Chin},
journal = {PVLDB},
series = {{VLDB} '16},
volume = {9},
number = {14},
pages = {1683--1694},
doi = {10.14778/3007328.3007336},
url = {https://doi.org/10.14778/3007328.3007336},
year = {2016}
}
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,459 | Efficient Data Ingestion and Query Processing for LSM-Based Storage Systems | 2019 | VLDB | 5.6119467e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 14 of 14 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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