This special issue comprises of 12 selected best papers from the International Workshop on Computer Science for Environmental Engineering and EcoInformatics (CSEEE 2011). The conferences received 860 paper submissions from 15 countries and regions, of which 450 were selected for presentation after a rigorous review process. From these 450 research papers, through two rounds of reviewing, the guest editors selected 12 as the best papers on the “Data Mining and Data Management” track of the Conference. The candidates of the Special Issue are all the authors, whose papers have been accepted and presented at the CSEEE 2011, with the contents not been published elsewhere before.
  2011 International Workshop on Computer Science for Environmental Engineering and EcoInformatics will continue the excellent tradition of gathering world-class scientists, engineers and educators engaged in the fields of Computer Science and Environmental Biotechnology to meet and present their latest activities. CSEEE 2011 held on July 29-31, 2011, Kunming, China. This conference is sponsored by International Association for Scientific and High Technology, and is in cooperation with Yunnan University, and it is technical co-sponsored by Kunming University of Science and Technology.
  “WS-DAI-DM: An Interface Specification for Data Mining in Grid Environments”, by Yan Zhang, Luoming Meng, Honghui Li, Alexander Woehrer and Peter Brezany, proposes the WS-DAI-DM interface specification and related issues of providing the consistent web service interfaces to extract useful and hidden knowledge/patterns from distributed data resources in Grid environments. An application scenario about how end-users can access data mining services in Grid environments conveniently via the proposed mechanism is described in detail.
  “An Ensemble Data Mining and FLANN Combining Short-term Load Forecasting System for Abnormal Days”, by Ming Li and Junli Gao, proposes an ensemble decision tree and functional link neural network combining method for identifying the relationships between the power loads and the variables that influence the power loads especially in the abnormal days. The strategy improved the accuracy of the short time load forecasting of abnormal days while ensuring the overall prediction accuracy compared to the current using one in Anhui Province.
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