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Selectivity Estimation in Extensible Databases - A Neural Network Approach

Summary: Proposes a neural network-based selectivity estimator for extensible DBs with UDT/UDF predicates (text, spatial, image). Learns initial estimates and progressively refines them from operational data; demonstrates accuracy and integrates into Informix's DBA tool. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
8524
Venue
VLDB
Year
1998
Pagerank
8.0287389e-05
Overall Rank
2,841 | 80.24%
DOI
-

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 12 of 12 citing papers.

Rank Citing Paper Year Venue Pagerank
204 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00034784455
608 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00019235898
1,254 Selectivity Estimation for Range Predicates using Lightweight Models 2019 VLDB 0.00013027411
1,703 Are We Ready For Learned Cardinality Estimation? 2021 VLDB 0.00010836769
2,364 Deep Learning Models for Selectivity Estimation of Multi-Attribute Queries 2020 SIGMOD 8.9554751e-05
3,954 Efficiently Approximating Selectivity Functions using Low Overhead Regression Models 2020 VLDB 6.5926838e-05
3,990 FactorJoin: A New Cardinality Estimation Framework for Join Queries 2023 SIGMOD 6.5581983e-05
4,359 Astrid: Accurate Selectivity Estimation for String Predicates using Deep Learning 2021 VLDB 6.2569955e-05
4,417 Robust Query Driven Cardinality Estimation under Changing Workloads 2023 VLDB 6.2037371e-05
4,909 A Method for Optimizing Opaque Filter Queries 2020 SIGMOD 5.8338804e-05
7,610 Learning to be a Statistician: Learned Estimator for Number of Distinct Values 2022 VLDB 4.6965039e-05
9,728 SPACE: Cardinality Estimation for Path Queries Using Cardinality-Aware Sequence-based Learning 2025 SIGMOD 4.2942813e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 10 of 10 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
59 Sampling-Based Estimation of the Number of Distinct Values of an Attribute 1995 VLDB 0.00064501896
64 Improved Histograms for Selectivity Estimation of Range Predicates 1996 SIGMOD 0.00063612837
139 Predicate Migration: Optimizing Queries with Expensive Predicates 1993 SIGMOD 0.00042299329
252 Adaptive Selectivity Estimation Using Query Feedback 1994 SIGMOD 0.00030632263
327 Balancing Histogram Optimality and Practicality for Query Result Size Estimation 1995 SIGMOD 0.00027308479
367 Sequential Sampling Procedures For Query Size Estimation 1992 SIGMOD 0.00025509745
668 The Sequoia 2000 Storage Benchmark 1993 SIGMOD 0.00018430721
1,020 An Instant and Accurate Size Estimation Method for Joins and Selection in a Retrieval-Intensive Environment 1993 SIGMOD 0.00014624893
1,146 Estimating Alphanumeric Selectivity in the Presence of Wildcards 1996 SIGMOD 0.00013679782
1,528 A Model for the Prediction of R-tree Performance 1996 PODS 0.00011497734
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Semantically Similar Papers

Overall Rank Paper Year Venue Pagerank
1,981 Improved Selectivity Estimation by Combining Knowledge from Sampling and Synopses 2018 VLDB 9.8687545e-05
7,457 Selectivity Functions of Range Queries are Learnable* 2022 SIGMOD 4.7247191e-05
2,165 Self-Tuning, GPU-Accelerated Kernel Density Models for Multidimensional Selectivity Estimation 2015 SIGMOD 9.389622e-05
897 Selectivity Estimation and Query Optimization in Large Databases with Highly Skewed Distributions of Column Values 1988 VLDB 0.00015528028
372 Selectivity Estimation using Probabilistic Models 2001 SIGMOD 0.00025354779
2,356 Consistently Estimating the Selectivity of Conjuncts of Predicates 2005 VLDB 8.9620762e-05
3,954 Efficiently Approximating Selectivity Functions using Low Overhead Regression Models 2020 VLDB 6.5926838e-05
9,691 Selectivity Estimation for Queries Containing Predicates over Set-Valued Attributes 2023 SIGMOD 4.3035354e-05
2,364 Deep Learning Models for Selectivity Estimation of Multi-Attribute Queries 2020 SIGMOD 8.9554751e-05
1,254 Selectivity Estimation for Range Predicates using Lightweight Models 2019 VLDB 0.00013027411