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Baldeo K. Taneja

dblp:49/3294 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 1989
—ORCID · none

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

Databases, data management, data science and information retrieval · 2

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
2 papers
Query processing and optimization · 100%

Topics — the 4 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Query processing and optimization
aggregate query processing		 0.011989
Processing Aggregate Relational Queries with Hard Time Constraints · SIGMOD Conference 1989
Query processing and optimization
approximate query processing		 0.011988
Statistical Estimators for Relational Algebra Expressions · PODS 1988
Query processing and optimization › approximate query processing
sampling-based approximate query processing		 0.011988
Statistical Estimators for Relational Algebra Expressions · PODS 1988
Query processing and optimization › query execution › expression evaluation
relational expression evaluation		 0.011988
Statistical Estimators for Relational Algebra Expressions · PODS 1988

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

statistical estimation · 0.0data sampling · 0.0
YearPublicationVenuePosition
1989 Processing Aggregate Relational Queries with Hard Time Constraints
Wen-Chi Hou, Gultekin Özsoyoglu, Baldeo K. Taneja
 SIGMOD Conference3
1988 Statistical Estimators for Relational Algebra Expressions
abstract
Present database systems process all the data related to a query before giving out responses. As a result, the size of the data to be processed becomes excessive for real-time/time-constrained environments. A new methodology is needed to cut down systematically the time to process the data involved in processing the query. To this end, we propose to use data samples and construct an approximate synthetic response to a given query.
Wen-Chi Hou, Gultekin Özsoyoglu, Baldeo K. Taneja
 PODS3