VLDB 2026 Research / reviewers in the wild / expert
B. L. Fritchman
dblp:32/6629
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1988
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 1
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 |
Data models and query languages · 44% Database system architecture and tuning · 44% Database theory · 13% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Database system architecture and tuning › database system implementation
database management system design |
0.0 | 1 | 1988 | SIM: A Database System Based on the Semantic Data Model · SIGMOD Conference 1988 |
Data models and query languages › conceptual modeling
semantic data model |
0.0 | 1 | 1988 | SIM: A Database System Based on the Semantic Data Model · SIGMOD Conference 1988 |
Database theory
integrity constraints |
0.0 | 1 | 1988 | SIM: A Database System Based on the Semantic Data Model · SIGMOD Conference 1988 |
Methods — techniques the papers use, named apart from their topics
generalization hierarchy · 0.0English-like DML · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1988 | SIM: A Database System Based on the Semantic Data ModelabstractSIM is a fully featured, commercially available database management system based on a semantic data model similar to Hammer and McLeod's SDM SIM has two primary modeling goals. The first is to narrow the gap between a user's real-world perception of data and the conceptual view imposed by the database system because of modeling presuppositions or limitations. The second goal is to allow, as much as possible, the semantics of data to be defined in the schema and make the database system responsible for enforcing its integrity SIM provides a rich set of constructs for schema definition, including those for specifying generalization hierarchies modeled by directed acyclic graphs, interobject relationships and integrity constraints. It also features a novel, easy-to-use, English-like DML. This paper describes the key modeling features of SIM, the architecture of the system and its implementation considerations. D. Jagannathan, R. L. Guck, B. L. Fritchman, J. P. Thompson, D. M. Tolbert |
SIGMOD Conference | 3 |