By Juryon Paik, Dongho Won, Farshad Fotouhi, Ung Mo Kim (auth.), Marcus Gallagher, James P. Hogan, Frederic Maire (eds.)
This booklet constitutes the refereed lawsuits of the sixth overseas convention on clever information Engineering and automatic studying, excellent 2005, held in Brisbane, Australia, in July 2005.
The seventy six revised complete papers offered have been conscientiously reviewed and chosen from 167 submissions. The papers are geared up in topical sections on information mining and information engineering, studying algorithms and structures, bioinformatics, agent applied sciences, and monetary engineering.
Read Online or Download Intelligent Data Engineering and Automated Learning - IDEAL 2005: 6th International Conference, Brisbane, Australia, July 6-8, 2005. Proceedings PDF
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Extra info for Intelligent Data Engineering and Automated Learning - IDEAL 2005: 6th International Conference, Brisbane, Australia, July 6-8, 2005. Proceedings
And hence to specify the data of the interest over which a query should be answered, each query in a sensor network has a geographical region associated with it. It seems that any kind of index on distributed data requires a hierarchical structure that aggregates information from different region of the networks. Prior work in range query for sensor networks has addressed a number of important issues in constructing such hierarchies. More detailed information can be accessed by top down traversal of the hierarchy to visit the sensors holding the relevant information [2, 4, 7, 8].
Using Pre-aggregation for Efficient Spatial Query Processing in Sensor Environments 27 3 Efficient Spatial Query Processing Using Sensor Tree with Pre-aggregated Results In this section, distributed index structure in sensor networks for time-efficient aggregation query processing is proposed. The main idea is to cover the underlying sensors in networks distributed R-Tree with pre-aggregated results, named Sensor Tree. Sensor Tree stores for each Minimum Bounding Rectangle (MBR), the values of the aggregation function for sensing data of the same type of sensors within the MBR.
J. Franklin, and D. Culler, Supporting Aggregate Queries Over Ad-Hoc Wireless Sensor Networks, Proceedings of WMCSA 2002. 13. Y. Yao and J. Gehrke, Ouery Processing for Sensor Networks, Proceedings of CIDR 2003. Model Trees for Classification of Hybrid Data Types Hsing-Kuo Pao, Shou-Chih Chang, and Yuh-Jye Lee Dept. tw Abstract. In the task of classiﬁcation, most learning methods are suitable only for certain data types. For the hybrid dataset consists of nominal and numeric attributes, to apply the learning algorithms, some attributes must be transformed into the appropriate types.