DBScholar

Back to papers

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)

Paper ID
11496
Venue
VLDB
Year
2016
Pagerank
5.3274671e-05
Overall Rank
9,034 | 38.02%
DOI
10.14778/3007328.3007336

Incoming Non-self Citations Over Time

Authors

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
Previous Page 1 / 1 Next

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.

Previous Page 1 / 1 Next

Semantically Similar Papers