A Design of SOA-based Data Integration Framework for Effective Spatial Data Mining


The KIPS Transactions:PartD, Vol. 18, No. 5, pp. 385-392, Oct. 2011
10.3745/KIPSTD.2011.18.5.385,   PDF Download:

Abstract

Recently, the concern of IT-in-Agriculture convergence technology that combines information technology and agriculture is increasing rapidly. Especially, the crop cultivation related prediction services by spatial data mining (SDM) can play an important role in reducing the damage of natural disaster and enhancing crop productivity. However, the data conversion and integration procedure to acquire the learning dataset of SDM for the prediction service need a lot of effort and time, because of their heterogeneity between distributed data. In addition, calculating spatial neighborhood relationships between spatial and non-spatial data necessitates requires the complicated calculation procedure for large dataset. In this paper, we suggest a SOA-based data integration framework that can effectively integrate distributed heterogeneous data by treating each data source as a service unit and support to find the optimal prediction service by improving productivity of learning dataset for SDM. In our experiment, we confirmed that our framework can be effectively applied to find the optimal prediction service for the frost damage area, by considering the case of peach crop cultivation in Icheon in Korea.


Statistics
Show / Hide Statistics

Statistics (Cumulative Counts from September 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article
[IEEE Style]
I. H. Moon, H. Hur, S. K. Kim, "A Design of SOA-based Data Integration Framework for Effective Spatial Data Mining," The KIPS Transactions:PartD, vol. 18, no. 5, pp. 385-392, 2011. DOI: 10.3745/KIPSTD.2011.18.5.385.

[ACM Style]
Il Hwan Moon, Hwan Hur, and Sam Keun Kim. 2011. A Design of SOA-based Data Integration Framework for Effective Spatial Data Mining. The KIPS Transactions:PartD, 18, 5, (2011), 385-392. DOI: 10.3745/KIPSTD.2011.18.5.385.