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Upen S. Chakravarthy

dblp:48/5703 · DBLP profile ↗
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5ranked-venue papers
4as first author
0since 2021 · last 1990
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 3 · 2 first-authorSystems, architecture and hardware · 1 · 1 first-authorSoftware engineering, systems software and programming languages · 1 · 1 first-authorTheory of computation · 1 · 1 first-author

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
3 papers
Query processing and optimization · 56% Database theory · 38% Database system architecture and tuning · 6%
Theoretical computer science
1 paper
Logic in computer science · 100%

Topics — the 5 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Database theory
deductive database
0.021990
Logic-Based Approach to Semantic Query Optimization · ACM Trans. Database Syst. 1990
Multiple Query Processing in Deductive Databases using Query Graphs · VLDB 1986
Query processing and optimization › semantic query processing
semantic query optimization
0.011990
Logic-Based Approach to Semantic Query Optimization · ACM Trans. Database Syst. 1990
Query processing and optimization
multi-query optimization
0.011988
Anatomy of a Mudular Multiple Query Optimizer · VLDB 1988
Query processing and optimization
multiple-query processing
0.011986
Multiple Query Processing in Deductive Databases using Query Graphs · VLDB 1986
Logic in computer science
first-order logic
0.011990
Logic-Based Approach to Semantic Query Optimization · ACM Trans. Database Syst. 1990

Methods — techniques the papers use, named apart from their topics

integrity constraints · 0.0first-order logic · 0.0query graph · 0.0
YearPublicationVenuePosition
1990 Logic-Based Approach to Semantic Query Optimization
abstract
The purpose of semantic query optimization is to use semantic knowledge (e.g., integrity constraints) for transforming a query into a form that may be answered more efficiently than the original version. In several previous papers we described and proved the correctness of a method for semantic query optimization in deductive databases couched in first-order logic. This paper consolidates the major results of these papers emphasizing the techniques and their applicability for optimizing relational queries. Additionally, we show how this method subsumes and generalizes earlier work on semantic query optimization. We also indicate how semantic query optimization techniques can be extended to databases that support recursion and integrity constraints that contain disjunction, negation, and recursion.
Upen S. Chakravarthy, John Grant, Jack Minker
ACM Trans. Database Syst.1
1988 Anatomy of a Mudular Multiple Query Optimizer
Arnon Rosenthal, Upen S. Chakravarthy
VLDB2
1986 Multiple Query Processing in Deductive Databases using Query Graphs
Upen S. Chakravarthy, Jack Minker
VLDB1
1982 Interfacing Predicate Logic Languages and Relational Databases
Upen S. Chakravarthy, Jack Minker
ICLP1
1982 Logic Programming on ZMOB: A Highly Parallel Machine
Upen S. Chakravarthy, Simon Kasif, Madhur Kohli, Jack Minker, D. Cao
ICPP1