Demo Data from EDBT 2026 / reviewers in the wild / expert
Andres Calderon
dblp:200/6093
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · none
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 · 100% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Spatial and temporal data management
spatial data structures |
0.2 | 1 | 2024 | Pyneapple-G: Scalable Spatial Grouping Queries · Proc. VLDB Endow. 2024 |
Methods — techniques the papers use, named apart from their topics
parallelization · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Pyneapple-G: Scalable Spatial Grouping QueriesabstractThis paper demonstrates Pynapple-G , an open-source library for scalable spatial grouping queries based on Apache Sedona (formerly known as GeoSpark). We demonstrate two modules, namely, SGPAC and DDCEL , that support grouping points, grouping lines, and polygon overlays. The SGPAC module provides a large-scale grouping of spatial points by highly complex polygon boundaries. The grouping results aggregate the number of spatial points within the boundaries of each polygon. The DDCEL module provides the first parallelized algorithm to group spatial lines into a DCEL data structure and discovers planar polygons from scattered line segments. Exploiting the scalable DCEL, we support scalable overlay operations over multiple polygon layers to compute the layers' intersection, union, or difference. To showcase Pyneapple-G , we have developed a frontend web application that enables users to interact with these modules, select their data layers or data points, and view results on an interactive map. We also provide interactive notebooks demonstrating the superiority and simplicity of Pyneapple-G to help social scientists and developers explore its full potential. Laila Abdelhafeez, Andres Calderon, Amr Magdy 0001, Vassilis J. Tsotras |
Proc. VLDB Endow. | 2 |