Earticle

Mining association rules refers to extracting useful knowledge from large databases. Algo- rithms of this technique are both data and computation-intensive, which make grid platforms very attractive for them. However, to exploit these platforms, new data partitioning features are required where the specicities of both association rule mining technique and grids must be taken into consideration. In this paper, we propose a novel data partitioning approach for distributed association rule mining algorithms in the context of a grid computing environment. We conduct exper- iments using the French research grid "Grid'5000". Experimental results conrm that our data partitioning approach is very sucient for balancing the load when homogeneous clus- ters are used. For heterogeneous clusters, the proposed data partitioning approach constitute the preprocessing phase of the process of dynamic load balancing of distributed association rule mining.

LIDAR (LIght Detection And Ranging) is an active remote sensing technology which provides 3D coordinates of the Earth's surface by performing range measurements from the sensor. Early small footprint LIDAR systems recorded multiple discrete returns from the back-scattered energy. Recent advances in LIDAR hardware now make it possible to record full digital waveforms of the returned energy. LIDAR waveform decomposition involves separating the return waveform into a mixture of components which are then used to characterize the original data. The most common statistical mixture model used for this process is the Gaussian mixture. Waveform decomposition plays an important role in LIDAR waveform processing, since the resulting components are expected to represent reflection surfaces within waveform footprints. Hence the decomposition results ultimately affect the interpretation of LIDAR waveform data. Computational requirements in the waveform decomposition process result from two factors; (1) estimation of the number of components in a mixture and the resulting parameter estimates, which are inter-related and cannot be solved separately, and (2) parameter optimization does not have a closed form solution, and thus needs to be solved iteratively. The current state-of-the-art airborne LIDAR system acquires more than 50,000 waveforms per second, so decomposing the enormous number of waveforms is challenging using traditional single processor architecture. To tackle this issue, four parallel LIDAR waveform decomposition algorithms with different work load balancing schemes - (1) no weighting, (2) a decomposition results-based linear weighting, (3) a decomposition results-based squared weighting, and (4) a decomposition time-based linear weighting - were developed and tested with varying number of processors (8-256). The results were compared in terms of efficiency. Overall, the decomposition time-based linear weighting work load balancing approach yielded the best performance among four approaches.

SM 프로젝트는 SI 프로젝트와 달리 고객의 요청 사항(CSR)이 있을 때 마다 개발 계획을 세우고 관리를 하여야 하며 동시에 고객의 개발 요청 내용 외에도 운영업무에 대한 계획도 같이 세워 져야 한다. 하지만 대부분의 SM 프로젝트에서는 한 두 사람의 경험이나 판단으로 계획이 작성, 할당되며 운영업무에 대한 계획의 정확도도 떨어져 업무 load balancing 이 잘 되지 않는다. 본 논문에서는 이러한 문제 해결을 위해 효과적인 SM 프로젝트에서의 EFFROT ESTIMATION 프로세스를 제안하여 업무 load balancing 을 이루는 방안을 제시하고자 한다.