A New Sensor-Based Spatial OLAP Architecture Centered on an Agricultural Farm Energy-Use Diagnosis Tool

A New Sensor-Based Spatial OLAP Architecture Centered on an Agricultural Farm Energy-Use Diagnosis Tool

Sandro Bimonte (Irstea, l'Unité Recherche Technologies et Systèmes d'information pour les Agrosystèmes (TSCF), Aubière, France), Marilys Pradel (Irstea, l'Unité Recherche Technologies et Systèmes d'information pour les Agrosystèmes (TSCF), Montoldre, France), Daniel Boffety (Irstea, l'Unité Recherche Technologies et Systèmes d'information pour les Agrosystèmes (TSCF), Montoldre, France), Aurelie Tailleur (ARVALIS - Institut du végétal, Service Agronomie Economie Environnement La Jaillière, La Chapelle St Sauveur, France), Géraldine André (Irstea, l'Unité Recherche Technologies et Systèmes d'information pour les Agrosystèmes (TSCF), Montoldre, France), Rabi Bzikha (Irstea, l'Unité Recherche Technologies et Systèmes d'information pour les Agrosystèmes (TSCF), Aubière, France) and Jean-Pierre Chanet (Irstea, l'Unité Recherche Technologies et Systèmes d'information pour les Agrosystèmes (TSCF), Aubière, France)
Copyright: © 2013 |Pages: 20
DOI: 10.4018/ijdsst.2013100101
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Abstract

Agricultural energy consumption is an important environmental and social issue. Several diagnosis tools have been proposed to define indicators for analyzing the large-scale energy consumption of agricultural farm activities (year, farm, production activity, etc.). In Bimonte, Boulil, Chanet and Pradel (2012), the authors define (i) new appropriate indicators to analyze agricultural farm energy-use performance on a detailed scale and (ii) show how Spatial Data Warehouse (SDW) and Spatial OnLine Analytical Processing (SOLAP) GeoBusiness Intelligence (GeoBI) technologies can be used to represent, store, and analyze these indicators by simultaneously producing graphical and cartographic reports. These GeoBI technologies allow for the analysis of huge volumes of georeferenced data by providing aggregated numerical values visualized by means of interactive tabular, graphical, and cartographic displays. However, existing data collection systems based on sensors are not well adapted for agricultural data. In this paper, the authors show the global architecture of our GeoBI solution and highlight the data collection process based on agricultural ad hoc sensor networks, the associated transformation and cleaning operations performed by means of Spatial Extract Transform Load (ETL) tools, and a new implementation of the system using a web-services-based loosely coupled SOLAP architecture to provide interoperability and reusability of the complex multi-tier GeoBI architecture. Moreover, the authors detail how the energy-use diagnosis tool proposed in Bimonte, Boulil, Chanet and Pradel (2012) theoretically fits with the sensor data and the SOLAP approach.
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1. Introduction

Agriculture energy consumption depends on the method of production used. Direct agricultural energy consumption was estimated to be 28 Mtoe of a total consumption of 1142 Mtoe, which was 2.5% of the energy directly consumed by the EU25 in 2004 (SOLAGRO, 2007); 55% of this energy was the result of fuel consumption. With the planned reduction in oil and rising oil prices, agriculture must reduce energy consumption to improve its economic development and decrease its environmental impact. Awareness of the importance of preserving nonrenewable energy resources is increasing, as evidenced by the energy development policies adopted in recent years by different governments (Energy Policy of 2005, the Grenelle Environment, etc.). Applied to the agricultural context, this reality requires a better assessment of the energy balance of farms in terms of energy performance. At present, many diagnosis tools exist that define a set of indicators to assess the energy performance of agricultural farms (Pradel & Boffety, 2012). These diagnoses, which are specifically adapted to conducting a comprehensive assessment at the farm scale, are not necessarily relevant to evaluating the energy performance at a detailed scale (plot, technical operation, etc.). Thus, in this paper, we present a theoretical diagnosis framework with new appropriate indicators to analyze the energy-use performance of agricultural farms at a more detailed scale (Sec 3), as proposed in Bimonte, Boulil, Chanet and Pradel (2012).

To implement our new diagnosis tool, two important issues have been addressed: (i) how data are collected (i.e., technological means and data frequency) and (ii) how data are analyzed (i.e., spatial decision-making systems).

Indeed, by defining detailed scale indicators, large quantities of georeferenced data have been collected to perform these energy diagnoses. Recent technological advances (embedded sensors, wireless sensor networks, GPS, etc.) make it possible to automatically collect large volumes of data (Chanet et al., 2004; Steinberger, Rothmund & Auernhammer, 2009). Many different types of data collection equipment exist on farms, including embedded systems in machines, smartphones, and smart buildings. Farmers are therefore able to access a large georeferenced database to trace their activities. The issue is to provide reliable integration of these data to facilitate control of the farm (Deurveilher et al., 2012). In this paper, we propose an embedded data-box that makes it possible to build a database of georeferenced historical activities for the farm equipment based on basic information (position actuators, fuel level, etc.). With this type of recorded data, we can offer a detailed view (temporally and spatially) of energy consumption for each type of activity (Sec. 4.1).

This huge volume of data represents another important limitation of systems that implement these diagnosis tools. They are usually based on classical data storage systems, such as spreadsheet tools, which do not allow interactive analysis of huge volumes of geographic data at different granularities/scales. Moreover, diagnosis systems do not provide any cartographic visualization of energy indicators because they limit the important analysis capabilities associated with geographic data (Maceachren et al., 2004).

By contrast, Geo-Business Intelligence technologies such as Spatial Data Warehouses (SDWs) and Spatial OLAP (SOLAP) systems are widely recognized as efficient tools for the on-line analysis of huge volumes of georeferenced datasets. SOLAP systems have been defined by Y. Bédard as “Visual platforms built especially to support rapid and easy spatiotemporal analysis and exploration of data following a multidimensional approach comprised of aggregation levels available in cartographic displays as well as in tabular and diagram displays” (Bédard & Han, 2001).

Thus, in this paper, we show how SDW and SOLAP technologies can be used to store data, collected using our sensor network, and analyze these indicators by simultaneously producing cartographic and tabular reports (Sec 4).

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