Demo Data from VLDB 2026 / reviewers in the wild / expert
Sachin Smotra
dblp:415/9317
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
—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 |
Database system architecture and tuning · 38% Information retrieval · 38% Database theory · 12% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information retrieval › document processing › document analysis
document modeling |
0.9 | 1 | 2025 | Towards Principled, Practical Document Database Design · Proc. VLDB Endow. 2025 |
Database system architecture and tuning › database design
NoSQL schema design |
0.9 | 1 | 2025 | Towards Principled, Practical Document Database Design · Proc. VLDB Endow. 2025 |
Data models and query languages
conceptual modeling |
0.3 | 1 | 2025 | Towards Principled, Practical Document Database Design · Proc. VLDB Endow. 2025 |
Database theory
normal forms |
0.3 | 1 | 2025 | Towards Principled, Practical Document Database Design · Proc. VLDB Endow. 2025 |
Methods — techniques the papers use, named apart from their topics
conceptual modeling · 0.9anti-pattern analysis · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Towards Principled, Practical Document Database DesignabstractRelational database design is a well-understood process enabled by a combination of database theory (e.g., normal forms) as well as conceptual modeling (e.g., ER-based design). In contrast, database design for NoSQL databases, notably document databases, is often approached in a much more ad hoc manner. It is frequently driven by application details and physical considerations that muddy the design process in ways all too reminiscent of the pre-relational database era. In this paper, we argue for a return to sanity - for a logical, data-first, conceptually grounded approach to document database design. We explain how such an approach can work, yielding a clean, query-friendly document database design. We also highlight a collection of document (JSON) anti-patterns to avoid. The process and the anti-patterns both stem from the authors' experiences in current and past lives when dealing with a wide variety of JSON document data from commercial applications, government applications, and university research applications. Michael J. Carey 0001, Wail Y. Alkowaileet, Nick Digeronimo, Peeyush Gupta, Sachin Smotra, Till Westmann |
Proc. VLDB Endow. | 5 |