Titlebook: Probabilistic Ranking Techniques in Relational Databases; Ihab F. Ilyas,Mohamed A. Soliman Book 2011 Springer Nature Switzerland AG 2011

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enchant

Query Semantics, each world . ∈ . is a valid ranked instance of the database. We denote with . (.) the rank/position of tuple . in .. We elaborate on the ranking requirement of worlds in ., using the example given by Figure 3.1.

Oratory

deviate

pulmonary-edema

Conclusion,This lecture compiles and summarizes state-of-the-art techniques supporting ranked retrieval in uncertain and probabilistic databases. The lecture discusses the interplay between ranking and un-certainty models, and it describes several proposed mechanisms to compute ranking queries under different types ofdata uncertainty.

Absenteeism

978-3-031-00718-7Springer Nature Switzerland AG 2011

embolus

Probabilistic Ranking Techniques in Relational Databases978-3-031-01846-6Series ISSN 2153-5418 Series E-ISSN 2153-5426

Budget

Introduction,s top-. queries) is to report the top ranked query results, based on scores computed by a given scoring function (e.g., a function defined on one or more database columns). A scoring function induces a unique total order on query results, where score ties are usually resolved using a deterministic tie-breaking criterion.

油氈

Methodologies,n with the methods that build on branch-and-bound search in Section 4.1. We then discuss Monte-Carlo simulation methods in Section 4.2.We give the details of a number of dynamic programming techniques in Section 4.3. We finally describe other methods that build on special properties of probabilistic ranking queries in Section 4.4.

假設(shè)