Actas de congresos
Data Quality In Agriculture Applications
Registro en:
Proceedings Of The Brazilian Symposium On Geoinformatics. , v. , n. , p. 128 - 139, 2012.
21794847
2-s2.0-84883001411
Autor
Malaverri J.E.G.
Medeiros C.B.
Institución
Resumen
Data quality is a common concern in a wide range of domains. Since agriculture plays an important role in the Brazilian economy, it is crucial that the data be useful and with a proper level of quality for the decision making process, planning activities, among others. Nevertheless, this requirement is not often taken into account when different systems and databases are modeled. This work presents a review about data quality issues covering some efforts in agriculture and geospatial science to tackle these issues. The goal is to help researchers and practitioners to design better applications. In particular, we focus on the different dimensions of quality and the approaches that are used to measure them.
128 139 Babu, S., Widom, J., Continuous queries over data streams (2001) SIGMOD Rec., 30 (3), pp. 109-120 Ballou, D., Wang, R., Pazer, H., Tayi, G.K., Modeling information manufacturing systems to determine information product quality (1998) Manage. Sci., 44, pp. 462-484 Barbosa, I., Casanova, M.A., Trust indicator for decisions based on geospatial data (2011) Proc. XII Brazilian Symposium on GeoInformatics, pp. 49-60 Blake, R., Mangiameli, P., The effects and interactions of data quality and problem complexity on classification (2011) J. Data and Information Quality, pp. 281-828 Bobrowski, M., Marré, M., Yankelevich, D., A homogeneous framework to measure data quality (1999) Proc. IQ, pp. 115-124. , MIT (2012) Center of Advanced Studies in Applied Economics, , http://cepea.esalq.usp.br/pib/, Accessed in June 2012 Chapman, A.D., (2005) Principles of Data Quality, , Global Biodiversity Information Facility, Copenhagen Chrisman, N.R., The role of quality information in the long-term functioning of a geographic information system (1984) Cartographica, 21 (2-3), pp. 79-87 Congalton, R.G., Green, K., (2009) Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, (13). , CRC Press, Boca Raton, FL, 2 edition (2012) Food and Agriculture Organization of the United Nations, , www.fao.org/countrystat, CountrySTAT Accessed on March 2012 Dai, C., Lin, D., Bertino, E., Kantarcioglu, M., An approach to evaluate data trustworthiness based on data provenance (2008) Proc. of the 5th VLDB Workshop on Secure Data Management, pp. 82-98. , Berlin, Heidelberg. Springer-Verlag (2008) EFarms, , http://proj.lis.ic.unicamp.br/efarms/, Accessed in June 2012 Land quality indicators and their use in sustainable agriculture and rural development (1997) FAO Land and Water Bulletin, , FAO Accessed in January 2012 (2012) Food and Agriculture Organization of the United Nations, , http://www.fao.org/, Accessed on March 2012 Content standard for digital geospatial metadata FGDC-STD-001-1998 (1998) Technical Report, US Geological Survey, , FGDC Goodchild, M.F., Li, L., Assuring the quality of volunteered geographic information (2012) Spatial Statistics, 1, pp. 110-120 Hartig, O., Zhao, J., Using web data provenance for quality assessment (2009) Proc. of the Workshop on Semantic Web and Provenance Management at ISWC (2003) Data Quality Assessment Framework, , http://dsbb.imf.org/, International Monetary Fund Accessed on January 2012 (2003) Geographic Information - Metadata, , http://www.iso.org/iso/, 19115 Accessed on January 2012 Kyeyago, F.O., Zake, E.M., Mayinza, S., The construction of an international agricultural data quality assessment framework (ADQAF) (2010) The 5th Int. Conf. on Agricultural Statistics (ICAS V)m Lee, Y.W., Strong, D.M., Kahn, B.K., Wang, R.Y., AIMQ: A methodology for information quality assessment (2002) Information & Management, 40 (2), pp. 133-146 Lunetta, R.S., Lyon, J.G., (2004) Remote Sensing and GIS Accuracy Assessment, , CRC Press Madnick, S., Zhu, H., Improving data quality through effective use of data semantics (2006) Data Knowl. Eng., 59, pp. 460-475 Madnick, S.E., Wang, R.Y., Lee, Y.W., Zhu, H., Overview and framework for data and information quality research (2009) J. Data and Information Quality, pp. 121-222 Medeiros, C.B., De Alencar, A.C., Data quality and interoperability in GIS (1999) Proc. of GeoInfo, , In portuguese Moraes, R.A., Rocha, J.V., Imagens de coeficiente de qualidade (Quality) e de confiabilidade (Reliability) para seleção de pixels em imagens de NDVI do sensor MODIS para monitoramento da cana-de-açúcar no estado de São Paulo (2011) Proc. of Brazilian Remote Sensing Symposium Naumann, F., From databases to information systems - Information quality makes the difference (2001) Proc. IQ Naumann, F., Rolker, C., Assessment methods for information quality criteria (2000) IQ, pp. 148-162. , MIT Parssian, A., Managerial decision support with knowledge of accuracy and completeness of the relational aggregate functions (2006) Decis. Support Syst., 42, pp. 1494-1502 Pierce, E.M., Assessing data quality with control matrices (2004) Commun. ACM, 47, pp. 82-86 Pipino, L.L., Lee, Y.W., Wang, R.Y., Data quality assessment (2002) Commun. ACM, 45, pp. 211-218 Prat, N., Madnick, S., Measuring data believability: A provenance approach (2008) Proc. of the 41st Hawaii Int. Conf. on System Sciences, p. 393 Redman, T.C., (2001) Data Quality: The Field Guide, , Digital Pr. [u.a.] Scholten, H., Ten Cate, A.J.U., Quality assessment of the simulation modeling process (1999) Comput. Electron. Agric., 22 (2-3), pp. 199-208 Shankaranarayanan, G., Cai, Y., Supporting data quality management in decision-making (2006) Decis. Support Syst., 42, pp. 302-317 (2009) TIPS 12: Data Quality Standards, , http://www.usaid.gov/policy/evalweb/documents/TIPS-DataQualityStandards. pdf, U.S. Agency for International Development Accessed in January 2012 Wang, R.Y., Strong, D.M., Beyond accuracy: What data quality means to data consumers (1996) Journal of Management Information Systems, 12 (4), pp. 5-34 Widom, J., Trio: A system for integrated management of data, accuracy, and lineage (2005) Proc. of the 2nd Biennial Conf. on Innovative Data Systems Research (CIDR) Xie, J., Burstein, F., Using machine learning to support resource quality assessment: An adaptive attribute-based approach for health information portals (2011) Proc. of the 16th Int. Conf. on Database Systems for Advanced Applications