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Sachin Smotra

dblp:415/9317 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Information retrieval › document processing › document analysis
document modeling
0.912025
Towards Principled, Practical Document Database Design · Proc. VLDB Endow. 2025
Database system architecture and tuning › database design
NoSQL schema design
0.912025
Towards Principled, Practical Document Database Design · Proc. VLDB Endow. 2025
Data models and query languages
conceptual modeling
0.312025
Towards Principled, Practical Document Database Design · Proc. VLDB Endow. 2025
Database theory
normal forms
0.312025
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
YearPublicationVenuePosition
2025 Towards Principled, Practical Document Database Design
abstract
Relational 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