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

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.

[1]  W. D. Basford,et al.  A comparison of energy use in conventional and integrated arable farming systems in the UK , 2003 .

[2]  André Fel.  R. Gras, M. Benoit, J.-P. Deffontaines, P.-L. Osty et alii, Le Fait technique en agronomie. Activité agricole, concepts et méthodes d'étude , 1990 .

[3]  Wolfgang Lehner,et al.  Data modeling for Precision Dairy Farming within the competitive field of operational and analytical tasks , 2007 .

[4]  Kathy J. Davis,et al.  Adding Value to Agricultural Data: A Golden Opportunity , 2003 .

[5]  Ahsan Abdullah,et al.  The Case for an Agri Data Warehouse: Enabling Analytical Exploration of Integrated Agricultural Data , 2004, Databases and Applications.

[6]  Kevin P. Scheibe,et al.  Dimensional issues in agricultural data warehouse designs , 2008 .

[7]  A C A Clements,et al.  A global livestock production and health atlas (GLiPHA) for interactive presentation, integration and analysis of livestock data. , 2002, Preventive veterinary medicine.

[8]  P. K. Malhotra,et al.  Original papers: Design and development of data mart for animal resources , 2008 .

[9]  Adrian McDonald,et al.  PICABUE: a methodological framework for the development of indicators of sustainable development , 1995 .

[10]  M. Chanet,et al.  ICT for traceability of sugarcane harvesting operations in small farms. , 2012 .

[11]  Sandro Bimonte,et al.  Un modèle UML et des contraintes OCL pour les entrepôts de données spatiales. De la représentation conceptuelle à l'implémentation , 2011, Ingénierie des Systèmes d Inf..

[12]  Sandro Bimonte,et al.  Definition and Analysis of New Agricultural Farm Energetic Indicators Using Spatial OLAP , 2012, ICCSA.

[13]  Constanta Zoie Radulescu,et al.  A multidimensional data model and OLAP analysis for agricultural production , 2009 .

[14]  Mickey Yost.  Data Warehousing and Decision Support at the National Agricultural Statistics Service , 2000 .

[15]  Kun Mean Hou,et al.  Communication et équipements agricoles. Du réseau embarqué au réseau ad hoc sans fil pour l'intégration des équipements agricoles dans des systèmes d'information étendus , 2003 .

[16]  Christian Bockstaller,et al.  Assessment of energy use in arable farming systems by means of an agro-ecological indicator: the energy indicator , 2002 .

[17]  Giner Alor-Hernández,et al.  Application of Probabilistic Techniques for the Development of a Prognosis Model of Stroke Using Epidemiological Studies , 2013, Int. J. Decis. Support Syst. Technol..

[18]  Amir Hussain,et al.  Data Mining a New Pilot Agriculture Extension Data Warehouse , 2006, J. Res. Pract. Inf. Technol..

[19]  Marilys Pradel,et al.  Quels indicateurs et solutions technologiques adaptés pour évaluer finement les performances énergétiques des exploitations agricoles , 2012 .

[20]  Mark Gahegan,et al.  Geovisualization for knowledge construction and decision support , 2004, IEEE Computer Graphics and Applications.

[21]  Frédéric Zahm,et al.  La méthode IDEA : indicateurs de durabilité des exploitations agricoles : guide d'utilisation , 2003 .

[22]  Ralph Kimball,et al.  The Data Warehouse Toolkit: Practical Techniques for Building Dimensional Data Warehouses , 1996 .

[23]  Jiawei Han,et al.  Fundamentals of spatial data warehousing for geographic knowledge discovery , 2001 .

[24]  Sandro Bimonte,et al.  The use of UML to design agricultural data warehouses , 2010 .

[25]  Vikrambhai S. Sorathia,et al.  Architecture of sensor based agricultural information system for effective planning of farm activities , 2004, IEEE International Conference onServices Computing, 2004. (SCC 2004). Proceedings. 2004.

[26]  Sandro Bimonte,et al.  When Spatial Analysis Meets OLAP: Multidimensional Model and Operators , 2010, Int. J. Data Warehous. Min..

[27]  Matthias Rothmund,et al.  Original papers: Mobile farm equipment as a data source in an agricultural service architecture , 2009 .