Selectivity estimation using probabilistic models

By Lise Getoor, B. Taskar, Daphne Koller. ACM SIGMOD Conference, 2001

313 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/851e3f1780a89d38505fc0887a58a9fc3c7b493e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Ihab F. Ilyas
2. Joseph M Hellerstein
3. Samuel Madden
4. W. Hong
5. Naga K. Govindaraju
6. Ming C Lin
7. Amol Deshpande
8. Dinesh Manocha

In Computer Networks and Communications

1. Samuel Madden
2. W. Hong
3. Carlos Guestrin
4. Joseph M Hellerstein
5. Amol Deshpande
6. Ihab F. Ilyas
7. Naga K. Govindaraju
8. Dinesh Manocha

In Signal Processing

1. Samuel Madden
2. W. Hong
3. Carlos Guestrin
4. Amol Deshpande
5. Naga K. Govindaraju
6. Ihab F. Ilyas
7. Joseph M Hellerstein
8. Dinesh Manocha

Citations in the corpus

• Representing and Querying Correlated Tuples in Probabilistic Databases (2007, background): “[21] explore the application of PRMs to answering selectivity estimation queries but not queries expressed in standard database languages.”
• The History of Histograms (abridged) (2003, background): “…54] and their dynamic maintenance with a priori guarantees [29], approximate query answering for aggregate join queries [4], select-join queries [25], and within the general framework of on-line aggregation [33, 32, 51], computing frequencies of high-frequency items in a stream [52], and others.”
• Model-based approximate querying in sensor networks (2005, methodology): “[24] propose exploiting data correlations through use of graphical model techniques for approximate query processing, but neither provide any guarantees in the answers returned.”
• Fast computation of database operations using graphics processors (2004, methodology): “Recently, several algorithms have been designed to implement join operations efficiently using selectivity estimation [7, 10].”
• CORDS: automatic discovery of correlations and soft functional dependencies (2004, methodology): “[10], for example, use probabilistic relational models—which extend Bayesian network models to the relational setting—for selectivity estimation.”
• Model-Driven Data Acquisition in Sensor Networks (2004, methodology): “[7, 9] propose exploiting data correlations through use of graphical model techniques for approximate query processing, but neither provide any guarantees in the answers returned.”