Improved histograms for selectivity estimation of range predicates
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Wavelet-based histograms for selectivity estimation
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Join synopses for approximate query answering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Independence is good: dependency-based histogram synopses for high-dimensional data
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Selectivity estimation using probabilistic models
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
One-dimensional and multi-dimensional substring selectivity estimation
The VLDB Journal — The International Journal on Very Large Data Bases
Learning with mixtures of trees
The Journal of Machine Learning Research
Selectivity Estimation for String Predicates: Overcoming the Underestimation Problem
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Selectivity estimation for fuzzy string predicates in large data sets
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Graph-based synopses for relational selectivity estimation
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Extending q-grams to estimate selectivity of string matching with low edit distance
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Example-driven design of efficient record matching queries
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Scalable semantic web data management using vertical partitioning
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Declarative information extraction using datalog with embedded extraction predicates
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Learning probabilistic relational models
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Probabilistic classification and clustering in relational data
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
A framework for evaluating database keyword search strategies
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Selectivity estimation for extraction operators over text data
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Estimating selectivity for joined RDF triple patterns
Proceedings of the 20th ACM international conference on Information and knowledge management
SPARK2: Top-k Keyword Query in Relational Databases
IEEE Transactions on Knowledge and Data Engineering
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Many databases today are text-rich, comprising not only structured, but also textual data. Querying such databases involves predicates matching structured data combined with string predicates featuring textual constraints. Based on selectivity estimates for these predicates, query processing as well as other tasks that can be solved through such queries can be optimized. Existing work on selectivity estimation focuses either on string or on structured query predicates alone. Further, probabilistic models proposed to incorporate dependencies between predicates are focused on the relational setting. In this work, we propose a template-based probabilistic model, which enables selectivity estimation for general graph-structured data. Our probabilistic model allows dependencies between structured data and its text-rich parts to be captured. With this general probabilistic solution, BN+, selectivity estimations can be obtained for queries over text-rich graph-structured data, which may contain structured and string predicates (hybrid queries). In our experiments on real-world data, we show that capturing dependencies between structured and textual data in this way greatly improves the accuracy of selectivity estimates without compromising the efficiency.