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


Vol. 18,  No. 5, pp. 385-392, Oct.  2011
10.3745/KIPSTD.2011.18.5.385


PDF
  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
  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.

Forms

Search
(IN TITLE, AUTHOR, ABSTRACT,KEYWORDS)

Advanced Search ▼

POPULAR KEYWORDS
(TOP 10 KEYWORDS)

Recent Publications
(LAST 3 YEARS)

Old Journals

Indexing

All publications of TKIPS is indexed in DOI, EBSCO, Google Scholar, Crossref, and CrossCheck.

TKIPS is also selected as the Journal for Accreditation by NRF (National Research Foundation of Korea).

Related Journals