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Beyond Equi-joins: Ranking, Enumeration and Factorization

We study theta-joins in general and join predicates with conjunctions and disjunctions of inequalities in particular, focusing on ranked enumeration where the answers are returned incrementally in an order dictated by a given ranking function. Our approach achieves strong time and space complexity p...

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Detalles Bibliográficos
Autores principales: Tziavelis, Nikolaos, Gatterbauer, Wolfgang, Riedewald, Mirek
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9106312/
https://www.ncbi.nlm.nih.gov/pubmed/35573749
http://dx.doi.org/10.14778/3476249.3476306
Descripción
Sumario:We study theta-joins in general and join predicates with conjunctions and disjunctions of inequalities in particular, focusing on ranked enumeration where the answers are returned incrementally in an order dictated by a given ranking function. Our approach achieves strong time and space complexity properties: with n denoting the number of tuples in the database, we guarantee for acyclic full join queries with inequality conditions that for every value of k, the k top-ranked answers are returned in [Formula: see text] time. This is within a polylogarithmic factor of [Formula: see text] , i.e., the best known complexity for equi-joins, and even of [Formula: see text] , i.e., the time it takes to look at the input and return k answers in any order. Our guarantees extend to join queries with selections and many types of projections (namely those called “free-connex” queries and those that use bag semantics). Remarkably, they hold even when the number of join results is n(ℓ) for a join of ℓ relations. The key ingredient is a novel [Formula: see text]-size factorized representation of the query output, which is constructed on-the-fly for a given query and database. In addition to providing the first non-trivial theoretical guarantees beyond equi-joins, we show in an experimental study that our ranked-enumeration approach is also memory-efficient and fast in practice, beating the running time of state-of-the-art database systems by orders of magnitude.