A General Service-Based Spatio-Temporal Data Analysis Framework
During the last decades, spatio-temporal data sets have drawn great attention in research areas and industrial applications. For the purpose of building better predictive models for spatio-temporal data analysis tasks, the data sets are mainly analyzed by traditional Data Mining (DM) tools and Geographical Information Systems (G1S) cooperatively. Usually the geospatial attributes of data need to be generated manually by analyst from GIS. When build/train a model and use the trained model to do prediction, the lack of automation of the geospatial attributes generation makes the task inefficient and hardly repeatable. Still, there are very few tools that can perform spatio-temporal data analysis tasks alone and automate the above procedure. In this paper, we propose a service-based spatio-temporal data analysis framework, which can leverage existing data mining took and geographical information systems, and perform spatio-temporal data analysis tasks in an integrative manner. In this framework, a data analysis task is divided into traditional data mining tasks and geospatial related tasks, each of which is executed on an existing tool/software. The entire procedure is automated by a bundle of web services. User can analyze data efficiently and repeatably by simply accessing the service. We also develop a discription model based on the Predictive Model Markup Language (PMML) and Keyhole Markup Language (KML) to describe the geospatial related data mining models in our framework. This enables information exchanging capability with other data mining tools and geospatial rendering services. A case study on spatial association rule mining demonstrates the usability of the framework.
spatio-temporal data mining pmml spatial
Jiajia Xu Wenjun Wang Weishan Dong Li Li Zhongbo Jiang
School of Computer Science and Technology Tianjin University Tianjin, PR China IBM Research - China Beijing, PR China
国际会议
成都
英文
684-688
2010-12-17（万方平台首次上网日期，不代表论文的发表时间）