2026-04-19T19:14:30Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/1692972025-07-22T22:33:36Zcom_2072_1033col_2072_452951

00925njm 22002777a 4500 dc Pardo, Paola author 2019-07-04 This project constructs a different cost-based pushdown police solution for querying in multidimensional environments. The integration of Qbeast, a novel index, in the Cassandra distributed database caused the need from frameworks, as Spark, to be aware and act in consecuence. We will see three approaches, the last one of them in a theorical frame: filter pushdown, sampling and a speculative physic data strategy. Each one of their implementations are detailed in the document, alongside with an explanation of the class modified. The solutions were tested with mixed data volumns, to see in which cases is efficient to follow that path. Results show that with low rows the new behaviour goes hand in hand with the default, but in intensive cases (starting with one gigabyte files) the speed-up begins to grow. Àrees temàtiques de la UPC::Informàtica Distributed database optimitzador consultes distribuït bases de dades indexació multidimensional optimizer queries distributed databases pushdown sampling multidimensional indexing Bases de dades distribuïdes Indexació automàtica Diseño e implementación de un optimizador de consultas basado en coste para índices multidimensionales en bases de datos distribuidas Desing and implementation of cost based query optimizer for distributed multidimensional indexing databases Estudio, diseño e implementación de distintas políticas de pushdown para bases de datos distribuidas con indexación multidimensional