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![FIGURE 2: ICN forwarding engine model and packets ( [4])](publication/337201610/figure/fig1/AS:824577177423874@1573605927908/CN-forwarding-engine-model-and-packets-4_Q320.jpg)
This paper explores the methodologies, challenges, and expected advantages related to the use of the information-centric network (ICN) technology for federating spatial databases. ICN services allow simplifying the design of federation procedures, improving their performance, and providing so-called data-centric security. In this work, we present a...
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... node uses the forwarding engine shown in Figure 2 and is connected to other nodes through channels called faces, which can be based on different transport technologies such as Ethernet, TCP/IP sockets, etc. ...
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... To download a named object, a consumer issues an Interest packet, which contains the object name and is forwarded to the producer. The forwarding process is referred to as routing by name since the upstream face is selected through a name-based prefix matching based on a forwarding information base (FIB) containing name prefixes, such as dbs#1 in Fig. 2. The FIB is usually configured by routing protocols, which announce name prefixes rather than IP subnetworks [6]. During the Interest forwarding process, the node temporarily keeps track of the forwarded Interest packets in a pending information table (PIT), which stores the name of the requested object and the identifier of the face ...
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... forwarding engine also supports multicast distribution for both Interest and Data packets. Interest multicasting takes place when there are more upstream faces for a given prefix (e.g., index/notify in Fig. 2), and the incoming Interest is forwarded to all of them. Data multicasting is implemented as follows: when a node receives multiple Interests for the same object, the engine forwards only the first packet and discards the subsequent ones, appending the identifier of the arrival downstream faces in the PIT; then when the requested Data ...
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... now discuss benefits and trade-offs related to the global indexing strategy discussed in Section III-D1. First of all, we would like to highlight the importance of having a global index, thus justifying the design effort made in this paper. To this end, Fig. 12 shows the maximum query rate with and without global indexing. In most cases, the resulting rate is dramatically higher with global indexing. Thus, a well-designed global indexing, enabling an effective query routing, can really make a difference in the performance for federated database applications. It is also worth observing that, ...
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... En la aplicación del método cuantitativo se obtuvo un análisis de las diferentes características comparativas como la facilidad de uso, configuraciones, calidad de servicio, entre otros que sobresalen en las bases de datos NoSQL clave/valor. Las características que se analizaron en [11], posterior a ello, se evaluó y se puso a prueba por los autores de este artículo, los resultados obtenidos se muestran en la Tabla 2. ...
The proportion of data generated is constantly growing as the number and variety of connected devices increase, advances in data storage and mining supporting this evolution bring along the development of new techniques for searching and accessing data and realizing applications. This article was developed to carry out a comparative analysis of three databases: Redis, CouchBase, and Amazon DynamoDB to provide key characteristics to choose the right keyvalue NoSQL system for the type of applications, data used, and data processing. This was done through qualitative and quantitative research by expressing the results through comparative tables with characteristics that were obtained through a literature review and defined by the authors after the use of each of the databases. It can be concluded from the study of quantitative data that DynamoBD is highly rated for its ease of use, easy configuration, automatic retrieval, and data replication. However, in the qualitative data, CouchBase is the best option for the user considering it is cross-platform and can be adapted to websites, apps, etc. Keywords: Redis, CouchBase, DynamonDB, Web Applications, NoSQL. Resumen La proporción de datos generados crece constantemente a medida que aumenta la cantidad y variedad de dispositivos conectados, los avances en el almacenamiento y la extracción de datos respaldan esta evolución, la cual trajo consigo el desarrollo de nuevas técnicas para buscar y acceder a los datos y realización de aplicaciones. Este artículo se desarrolló con el objetivo de realizar un análisis comparativo de tres bases de datos: Redis, CouchBase y Amazon DynamoDB. Con la finalidad de proporcionar características claves para elegir el sistema NoSQL clave-valor correcto para el tipo de aplicaciones, datos utilizados y procesamiento de datos. Se realizó mediante investigación de índole cualitativa y cuantitativa al expresar los resultados mediante cuadros comparativos con características que fueron obtenidas a través de una revisión bibliográfica y definidos por los autores posterior al uso de cada una de las bases de datos. Se puede concluir que con el estudio de los datos cuantitativos DynamoBD obtiene una alta calificación por su fácil uso, fácil configuración, recuperación automática y replicación de datos. No obstante, en los datos cualitativos CouchBase es la mejor opción para el usuario por ser de multiplataforma que se puede adaptar a sitios web, aplicaciones, etc. Palabras Clave: Redis, Couchbase, DynamoDB, Aplicaciones Web, NoSQL.
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... This document presented an architecture for federating spatial databases, such as the formation and grouping of common data; through a framework for a secure data center made up of layers [33]. ...
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Geospatial information has been indispensable for many application fields, including traffic planning, urban planning, and energy management. Geospatial data are mainly stored in relational databases that have been developed over several decades, and most geographic information applications are desktop applications. With the arrival of big data, geospatial information applications are also being modified into, e.g., mobile platforms and Geospatial Web Services, which require changeable data schemas, faster query response times, and more flexible scalability than traditional spatial relational databases currently have. To respond to these new requirements, NoSQL (Not only SQL) databases are now being adopted for geospatial data storage, management, and queries. This paper reviews state-of-the-art geospatial data processing in the 10 most popular NoSQL databases. We summarize the supported geometry objects, main geometry functions, spatial indexes, query languages, and data formats of these 10 NoSQL databases. Moreover, the pros and cons of these NoSQL databases are analyzed in terms of geospatial data processing. A literature review and analysis showed that current document databases may be more suitable for massive geospatial data processing than are other NoSQL databases due to their comprehensive support for geometry objects and data formats and their performance, geospatial functions, index methods, and academic development. However, depending on the application scenarios, graph databases, key-value, and wide column databases have their own advantages.
