Combining Fog and Cloud Computing to Support Spatial Analytics in Smart Cities

Spatial data generated by an Internet of Things (IoT) network is important to assist the spatial analytics process in issues related to smart cities. In these cities, IoT devices generate spatial data constantly. Thus, data can get increasingly voluminous very fast. In this paper, we investigate the challenge of managing these data through the use of a spatial data warehouse designed over a parallel and distributed data processing framework extended with a spatial analytics system. We propose an architecture aimed to assist a smart cities manager in the decision-making process. This architecture integrates a cloud layer where these technologies are located with a fog computing layer for extracting, transforming and loading the data into the spatial data warehouse. Furthermore, we introduce a set of guidelines to aid smart cities managers to implement the proposed architecture. These guidelines describe and discuss important issues that should be faced by the managers. We validate our architecture with a case study that uses real data collected by IoT devices in a smart city. This case study encompasses the execution of three different categories of spatial queries, demonstrating the architecture's efficacy and effectiveness to support spatial analytics in the context of smart cities.

Logical and Physical Design of Spatial Non-Strict Hierarchies in Relational Spatial Data Warehouse

The emergence of spatial or geographic data in DW Systems defines new models that support the storage and manipulation of the data. The need to build an SDW and to optimize SOLAP queries continues to attract the interest of researchers in recent years. Several spatial data models have been investigated to extend classical multidimensional data models with spatial concepts. However, most of existing models do not handle a non-strict spatial hierarchy. Moreover, the complexity of the spatial data makes the execution time of spatial queries very considerable. Often, spatial indexation methods are applied to optimizing access to large volumes of data and helps reduce the cost of spatial OLAP queries. Most of existing indexes support predefined spatial hierarchies. The authors show, in this article, that the logical models proposed in the literature and indexing techniques are not suitable to non-strict hierarchies. The authors propose a new logical schema supporting the non-strict hierarchies and a bitmap index to optimize queries defined by spatial dimensions with several non-strict hierarchies.

Peningkatan Kinerja Sistem Spatial Online Analytical Processing (SOLAP) Titik Panas Kebakaran Hutan

Data histori titik panas sebagai salah satu indikator kebakaran hutan dan lahan dapat dikelola dengan teknologi data warehouse dan sistem spatial online analytical processing (SOLAP). Pada penelitian sebelumnya telah dilakukan peningkatan kinerja terhadap sistem tersebut sehingga titik panas yang mampu dihasilkan meningkat menjadi 1500 titik. Penelitian ini bertujuan untuk meningkatkan kinerja sistem SOLAP data titik panas yang telah dibangun dalam penelitian sebelumnya. Peningkatan kinerja meliputi konfigurasi dari sisi perangkat lunak seperti peningkatan Java runtime environment (JRE), peningkatan server Apache Tomcat, dan peringkasan proses Javascript object notation (JSON) sedangkan spesifikasi perangkat keras menggunakan spesifikasi RAM dan processor yang sama dengan penelitian sebelumnya. Jumlah titik panas hasil query yang mampu dihasilkan dari konfigurasi tersebut meningkat menjadi 5344 titik.<br /><br />Kata kunci: kebakaran hutan, spatial data warehouse, spatial OLAP, titik panas

Aplicación de un Spatial Data Warehouse en la gestión de proyectos de vinculación: Caso de Estudio

La Dirección Técnica de Vinculación con la Sociedad de la Universidad Politécnica Salesiana, Sede Guayaquil, es la encargada de gestionar proyectos de vinculación que proporcionan el acercamiento entre Universidad y Sociedad, en conjunto con las Carreras. El desarrollo de estos proyectos no cuenta con la ayuda de un sistema de información geográfico para establecer los lugares donde se ha intervenido. Para brindar apoyo a la toma de decisiones en el proceso de la gestión de proyectos en sus etapas iniciales, se aplicó un Spatial Data Warehouse y se diseñó e implementó algunos cubos de datos espaciales, siguiendo la metodología HEFESTO, que permitieron la visualización escalar y geográfica de proyectos ejecutados; además, de la identificación de sectores vulnerables para el desarrollo de nuevos proyectos.