Chandrasekhar Srinivasan

dblp:65/174 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2000
—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 · 56% Query processing and optimization · 44%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data models and query languages › query language
object-oriented query language
0.012000
lambda-DB: An ODMG-Based Object-Oriented DBMS · SIGMOD Conference 2000
Query processing and optimization
query optimization
0.012000
lambda-DB: An ODMG-Based Object-Oriented DBMS · SIGMOD Conference 2000
Data models and query languages
object-oriented database
0.012000
lambda-DB: An ODMG-Based Object-Oriented DBMS · SIGMOD Conference 2000

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

query preprocessor · 0.0monoid comprehension calculus · 0.0c++ binding · 0.0
YearPublicationVenuePosition
2000 lambda-DB: An ODMG-Based Object-Oriented DBMS
abstract
The λ-DB project at the University of Texas at Arlington aims at developing frameworks and prototype systems that address the new query optimization challenges for object-oriented and object-relational databases, such as query nesting, multiple collection types, methods, and arbitrary nesting of collections. We have already developed a theoretical framework for query optimization based on an effective calculus, called the monoid comprehension calculus [4]. The system reported here is a fully operational ODMG 2.0 [2] OODB management system, based on this framework. Our system can handle most ODL declarations and can process most OQL query forms. λ-DB is not ODMG compliant. Instead it supports its own C++ binding that provides a seamless integration between OQL and C++ with low impedance mismatch. It allows C++ variables to be used in queries and results of queries to be passed back to C++ programs. Programs expressed in our C++ binding are compiled by a preprocessor that performs query optimization at compile time, rather than run-time, as it is proposed by ODMG. In addition to compiled queries, λ-DB provides an interpreter that evaluates ad-hoc OQL queries at run-time.
Leonidas Fegaras, Chandrasekhar Srinivasan, Arvind Rajendran, David Maier 0001
SIGMOD Conference2