Acquisition and Declarative Analytical Processing of Spatio-Temporal Observation Data

A generic framework for spatio-temporal observation data acquisition and declarative analytical processing has been designed and implemented in this Thesis. The main contributions of this Thesis may be summarized as follows: 1) generalization of a data acquisition and dissemination server, with grea...

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Detalles Bibliográficos
Autor: Villarroya Fernández, Sebastián
Tipo de recurso: tesis doctoral
Fecha de publicación:2018
País:España
Institución:Universidad de Santiago de Compostela (USC)
Repositorio:Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
Idioma:inglés
OAI Identifier:oai:minerva.usc.gal:10347/18045
Acceso en línea:http://hdl.handle.net/10347/18045
Access Level:acceso abierto
Palabra clave:Materias::Investigación::12 Matemáticas::1203 Ciencia de los ordenadores::120312 Bancos de datos
Materias::Investigación::12 Matemáticas::1203 Ciencia de los ordenadores::120318 Sistemas de información, diseño componentes
Descripción
Sumario:A generic framework for spatio-temporal observation data acquisition and declarative analytical processing has been designed and implemented in this Thesis. The main contributions of this Thesis may be summarized as follows: 1) generalization of a data acquisition and dissemination server, with great applicability in many scientific and industrial domains, providing flexibility in the incorporation of different technologies for data acquisition, data persistence and data dissemination, 2) definition of a new hybrid logical-functional paradigm to formalize a novel data model for the integrated management of entity and sampled data, 3) definition of a novel spatio-temporal declarative data analysis language for the previous data model, 4) definition of a data warehouse data model supporting observation data semantics, including application of the above language to the declarative definition of observation processes executed during observation data load, and 5) column-oriented parallel and distributed implementation of the spatial analysis declarative language. The huge amount of data to be processed forces the exploitation of current multi-core hardware architectures and multi-node cluster infrastructures.