Demo Data from EDBT 2026 / reviewers in the wild / expert
Rongjin Wang
dblp:421/2812
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
0009-0009-0626-0245ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Databases, data mining, and information retrieval
1 paper |
Spatial and temporal data management · 60% Indexing and storage engines · 40% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Indexing and storage engines
learned index |
1.0 | 1 | 2026 | Efficient Query Region Expansion and Decomposition based Spatial Range Query Algorithm · SIGIR 2026 |
Indexing and storage engines › learned index
learned spatial index |
1.0 | 1 | 2026 | Efficient Query Region Expansion and Decomposition based Spatial Range Query Algorithm · SIGIR 2026 |
Spatial and temporal data management
spatial indexing |
1.0 | 1 | 2026 | Efficient Query Region Expansion and Decomposition based Spatial Range Query Algorithm · SIGIR 2026 |
Spatial and temporal data management
spatial query processing |
1.0 | 1 | 2026 | Efficient Query Region Expansion and Decomposition based Spatial Range Query Algorithm · SIGIR 2026 |
Spatial and temporal data management › spatial query processing
spatial range query |
1.0 | 1 | 2026 | Efficient Query Region Expansion and Decomposition based Spatial Range Query Algorithm · SIGIR 2026 |
Methods — techniques the papers use, named apart from their topics
query region expansion · 1.0query region decomposition · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Efficient Query Region Expansion and Decomposition based Spatial Range Query AlgorithmabstractSpatial range queries play a crucial role in spatial information retrieval. Existing Z-order curve based algorithms suffer from accessing a large number of invalid points outside the query region. To address this challenge, we design a simple yet efficient Z-order curve based learned index, ZPI. Building upon ZPI, we propose a novel spatial range query algorithm, ZPI-RQ. ZPI-RQ leverages efficient decomposition mechanism to address the invalid points issue. To avoid decomposing the query region into a large number of overly small blocks, a query region expansion strategy is further introduced to align each query border with a m-order dividing lines. Experimental results show that ZPI-RQ significantly outperforms state-of-the-art algorithms in query efficiency, achieving a 3.6× improvement over traditional Z-order curve-based algorithms while accessing only 1% invalid points. Lianyin Jia, Rongjin Wang, Yingbin Su, Suprio Ray, Mengjuan Li, Jiaman Ding |
SIGIR | 2 |