<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0379-3982</journal-id>
<journal-title><![CDATA[Revista Tecnología en Marcha]]></journal-title>
<abbrev-journal-title><![CDATA[Tecnología en Marcha]]></abbrev-journal-title>
<issn>0379-3982</issn>
<publisher>
<publisher-name><![CDATA[Instituto Tecnológico de Costa Rica]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0379-39822021000100040</article-id>
<article-id pub-id-type="doi">10.18845/tm.v34i1.4822</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Hybrid storage engine for geospatial data using NoSQL and SQL paradigms]]></article-title>
<article-title xml:lang="es"><![CDATA[Un motor de almacenamiento híbrido para datos geoespaciales integrando los paradigmas NoSQL y SQL]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Herrera-Ramírez]]></surname>
<given-names><![CDATA[José A.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Treviño-Villalobos]]></surname>
<given-names><![CDATA[Marlen]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Víquez-Acuña]]></surname>
<given-names><![CDATA[Leonardo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto Tecnológico de Costa Rica Campus Local San Carlos ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Costa Rica</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Instituto Tecnológico de Costa Rica Campus Local San Carlos ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Costa Rica</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Instituto Tecnológico de Costa Rica Campus Local San Carlos ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Costa Rica</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>03</month>
<year>2021</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>03</month>
<year>2021</year>
</pub-date>
<volume>34</volume>
<numero>1</numero>
<fpage>40</fpage>
<lpage>54</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_arttext&amp;pid=S0379-39822021000100040&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_abstract&amp;pid=S0379-39822021000100040&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_pdf&amp;pid=S0379-39822021000100040&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract The design and implementation of services to handle geospatial data involves thinking about storage engine performance and optimization for the desired use. NoSQL and relational databases bring their own advantages; therefore, it is necessary to choose one of these options according to the requirements of the solution. These requirements can change, or some operations may be performed in a more efficient way on another database engine, so using just one engine means being tied to its features and work model. This paper presents a hybrid approach (NoSQL-SQL) to store geospatial data on MongoDB, which are replicated and mapped on a PostgreSQL database, using an open source tool called ToroDB Stampede; solutions then can take advantage from either NoSQL or SQL features, to satisfy most of the requirements associated to the storage engine performance. A descriptive analysis to explain the workflow of the replication and synchronization in both engines precedes the quantitative analysis by which it was possible to determine that a normal database in PostgreSQL has a shorter response time than to perform the query in PostgreSQL with the hybrid database. In addition, the type of geometry increases the update response time of a materialized view.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen El diseño e implementación de servicios para el manejo de datos geoespaciales implica pensar en el rendimiento del motor de almacenamiento y su optimización para cada uso deseado. Las bases de datos relacionales y no relacionales aportan sus propias funcionalidades, por lo tanto, es necesario elegir una de estas opciones de acuerdo con los requisitos de la solución. Estos requisitos pueden cambiar o tal vez algunas operaciones puedan realizarse de manera más eficiente en otro motor de base de datos, por lo que usar solo un motor significa estar vinculado a sus características y modelo de trabajo. Este artículo presenta un enfoque híbrido (NoSQL-SQL) para almacenar datos geoespaciales en MongoDB, estos datos son replicados y mapeados en una base de datos PostgreSQL, utilizando una herramienta de código abierto llamada ToroDB Stampede; las soluciones pueden aprovechar las funciones NoSQL o SQL para satisfacer la mayoría de los requisitos asociados con el rendimiento del motor de almacenamiento. Aquí se presenta un análisis descriptivo para explicar el flujo de trabajo de la replicación y sincronización en ambos motores; además, el análisis cuantitativo, mediante el cual se logró determinar que una base de datos normal en PostgreSQL tiene un tiempo de respuesta menor que realizar la consulta en PostgreSQL con la base de datos híbrida; asimismo, que el tipo de geometría incrementa el tiempo de respuesta de actualización de una vista materializada.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Base de datos]]></kwd>
<kwd lng="es"><![CDATA[SQL]]></kwd>
<kwd lng="es"><![CDATA[NoSQL]]></kwd>
<kwd lng="es"><![CDATA[ToroDB]]></kwd>
<kwd lng="es"><![CDATA[MongoDB]]></kwd>
<kwd lng="es"><![CDATA[PostgreSQL]]></kwd>
<kwd lng="es"><![CDATA[replicación]]></kwd>
<kwd lng="es"><![CDATA[base de datos espejo]]></kwd>
<kwd lng="en"><![CDATA[Database]]></kwd>
<kwd lng="en"><![CDATA[SQL]]></kwd>
<kwd lng="en"><![CDATA[NoSQL]]></kwd>
<kwd lng="en"><![CDATA[ToroDB]]></kwd>
<kwd lng="en"><![CDATA[MongoDB]]></kwd>
<kwd lng="en"><![CDATA[PostgreSQL]]></kwd>
<kwd lng="en"><![CDATA[replication]]></kwd>
<kwd lng="en"><![CDATA[mirroring]]></kwd>
</kwd-group>
</article-meta>
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