Result-size estimation for information-retrieval subqueries
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Summary
This paper proposes and evaluates a technique for estimating the result of retrieval queries with non-Boolean relevance functions that estimates discretized document distributions over the range of the relevance function.
- Type
- article
- Published
- 2010-10-26
- Cited by
- 0
- References
- 30
- OpenAlex
- https://openalex.org/W1986834646
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:6453314
Keywords
Computer science, Query optimization, Relevance (law), Query expansion, Information retrieval
References
- Evaluating Top-k Selection Queries
- Simple Random Sampling from Relational Databases
- Selectivity Estimation and Query Optimization in Large Databases with Highly Skewed Distribution of Column Values
- Introduction to Modern Information Retrieval
- A critical analysis of vector space model for information retrieval
- Practical selectivity estimation through adaptive sampling
- Selectively estimation for Boolean queries
- Efficient implementation of lazy suffix trees
- Efficient Sampling Strategies for Relational Database Operations
- Wavelet-based histograms for selectivity estimation
- Accurate estimation of the number of tuples satisfying a condition
- Impact transformation: effective and efficient web retrieval
- Substring selectivity estimation
- Estimating alphanumeric selectivity in the presence of wildcards
- Fuzzy queries in multimedia database systems
- Modeling score distributions for combining the outputs of search engines
- Selectivity Estimation for Fuzzy String Predicates in Large Data Sets
- Random sampling for histogram construction: how much is enough?
- One-dimensional and multi-dimensional substring selectivity estimation
- Effective use of block-level sampling in statistics estimation
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