EDBT 2026 Demo / reviewers in the wild / expert

Gayatri Sathe

dblp:30/4571 · DBLP profile ↗
← Back
2ranked-venue papers
1as first author
0since 2021 · last 2001
—ORCID · none

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

Databases, data management, data science and information retrieval · 2 · 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
2 papers
Query processing and optimization · 61% Database system architecture and tuning · 21% Data mining · 18%
Computer graphics and multimedia
1 paper
Visualization and visual analytics · 100%

Topics — the 5 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Database system architecture and tuning › analytical database system
multidimensional OLAP		 0.012001
Intelligent Rollups in Multidimensional OLAP Data · VLDB 2001
Query processing and optimization › OLAP
data cube		 0.012000
i3: Intelligent, Interactive Investigaton of OLAP data cubes · SIGMOD Conference 2000
Query processing and optimization
interactive data exploration		 0.012000
i3: Intelligent, Interactive Investigaton of OLAP data cubes · SIGMOD Conference 2000
Query processing and optimization
OLAP		 0.012000
i3: Intelligent, Interactive Investigaton of OLAP data cubes · SIGMOD Conference 2000
Visualization and visual analytics
interactive data analysis		 0.012000
i3: Intelligent, Interactive Investigaton of OLAP data cubes · SIGMOD Conference 2000

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

advanced OLAP operators · 0.1
YearPublicationVenuePosition
2001 Intelligent Rollups in Multidimensional OLAP Data
Gayatri Sathe, Sunita Sarawagi
 VLDB1
2000 i3: Intelligent, Interactive Investigaton of OLAP data cubes
abstract
The goal of the i3(eye cube) project is to enhance multidimensional database products with a suite of advanced operators to automate data analysis tasks that are currently handled through manual exploration. Most OLAP products are rather simplistic and rely heavily on the user's intuition to manually drive the discovery process. Such ad hoc user-driven exploration gets tedious and error-prone as data dimensionality and size increases. We first investigated how and why analysts currently explore the data cube and then automated them using advanced operators that can be invoked interactively like existing simple operators.
Sunita Sarawagi, Gayatri Sathe
 SIGMOD Conference2