Papers by Author: Ming Chen

Abstract: By means of pattern space division and based on Map/Reduce, the problem of processing the many-to-many corresponding relationship between the data set and the patterns set is converted to the problem of processing the many-to-many corresponding relationship between the data subsets and the pattern subspaces associated with the frequent 1-itemsets. Thus, the scale of the intermediate key/value pairs set is reduced so dramatically that the problem of single Map node bottleneck which results from combinatorial explosion of candidate patterns space is avoided. Over three rounds of Map/Reduce tasks, the pattern space is constructed and divided, the filtering rules is established and employed, father more, the mining of frequent patterns is realized in each pattern subspace independently. By making the best of both the universal trait of the entire pattern space and the individuality of each pattern subspace, the optimized non-recursive algorithm is designed and implemented to improve the efficiency of mining phase.

Abstract: Each of tasks within the application depends on multiple datasets that may be distributed anywhere within the Content-Based Networking. This paper defines the problem of scheduling distributed data-intensive application on to Gird resource and presents a formal resource and application model for the problem.

Abstract: The next generation of scientific experiments and studies are being carried out by large collaborations of researchers distributed around the world engaged in analysis of huge collections of data generated by scientific instruments. Grid computing has emerged as an enabler for such collaborations as it aids communities in sharing resource to achieve common objective. This paper defines the problem of scheduling distributed data-intensive application on to Gird resource and presents a formal resource and application model for the problem.

Abstract: Cloud Computing technology enables the sharing and collaborating of wide variety of resources. To fully utilize these resources, effective discovery techniques are necessities. Proposing and designing a resource discovery scheme based on Economic Agent. Base on the economic model and the technique in agent of grouping nodes sharing similar files to improve efficiency, this thesis suggests a resource discovery scheme based on economic agent, which is called EAGRD. Theoretical models on resource discovery are provided, under which EAGRD is compared with existing schemes theoretically. By controlling propagation of message into related communities, EAGRD improves time and network efficiency at the cost of topological maintenance overhead. Results from simulation demonstrate that this architecture is very effective in Cloud Computing resource discovery.

Abstract: First arrivals detecting on seismic record is important at all times. A novel support vector machine (SVM)-based method for seismic first-arrival pickup is proposed in this research. Firstly, the multi-resolution wavelet decomposition is used to de-noise the seismic record. And then, feature vectors are extracted from the denoise data. Finally, both SVM and artificial neural network (ANN) models are employed to train and predict the feature vectors. Experimental results demonstrate that the SVM model gives better accuracy than the ANN model. It is promising that the novel method is very prospective.