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- Support distributed computation models to process large data sets. 支持分布式计算模型,处理大规模数据。
- A new method is proposed for sample selection in large data set. 提出了一种大规模数据集的训练样本选择方法。
- UDDI is not designed to support large data sets required by some research uses. UDDI并不是为那些通过搜索获取大量数据集的使用场合而设计的。
- If you are working with large data sets, consider using a dedicated buffer pool for the table space. 如果是使用大型数据集,可考虑用专用的缓冲池来代替表空间。
- In order to improve the efficiency we propose a distributed clustering algorithm based on large data sets. 为了提高聚类效率提出了一种基于分布式的大数据集聚类算法。
- However, for very large data sets with many dimensions, MOLAP solutions aren't always so effective. 然而,对于非常大的多维数据集, MOLAP方案并不总是有效的。
- Knowledge discovery in databases and data mining aim at semiautomatic tools for analysis of large data sets. 数据库中的知识发现即数据挖掘是致力于大型数据分析中的半自动工具的研究。
- When working with large data sets, this analysis can negatively impact the performance of the control when automatic resizing occurs. 当处理大数据集时,如果发生自动大小调整,这种分析可使控件的性能下降。
- We conclude that cache-oblivious algorithms do outperform traditional RAM-model algorithms when working on large data sets. 本文的主要结论是,当处理大数据量时,高速缓存参数无关算法显著优于传统的基于ram模型的算法。
- Meetings address topics including biogeography, systematics, visualization of large data sets, conservation, evolution, and biodiversity hotspots. 这些研讨会的主题包含生物地理学、系统分类学、视觉化资料库、生态保育、演化与生物多样性热点确立。
- Ramaswamy, R Rastogi, K Shim. Efficient algo rithms for mining outliers from large data sets [A]. In:ACM SIGMOD Conference Proceedings [C], New Or leans: 2000. 周海燕.;空间数据挖掘的研究[D]
- The main problem of IBR is the organization method for large data set, and the cooperation strategy with geometry model. 基于图像的绘制技术中的关键问题是采样数据的组织模式,及其与场景几何模型的结合策略。
- Finally,through some experiments on the data sets in UCI machine learning repository,the algorithm is proved more efficient and suitable for large data sets. 最后,通过UCI机器学习库中的一些数据集对算法进行测试,证明了算法对大型的数据集进行属性约简的高效性。
- The experimental evaluation showed that OSS consistently outperforms classical range query strategies (RQS) and OSS is more scalable than RQS for large data sets. 实验数据表明开放模型策略(OSS)在性能上超越从前的变换查询策略(RQS);比RQS更适合对大的数据集合进行处理.
- By contraries, the algorithm proposed could get rid of the constraints.Moreover, besides supplying some interesting rules to user, it also does well in mining for large data set. 而本文提出的方法不再受到上述限制的困扰,并且可以挖掘出用户感兴趣的规则,尤其对于大规模样本集的效果也是相当不错的。
- LPI is optimal in the sense of local manifold structure.However, LPI is not efficient in time and memory, which makes it difficult to be applied to very large data set. 摘要LPI对于局部流形结构是优化的,但在时空上运行效率较低,使其很难应用于大型数据集。
- Abstract LPI is optimal in the sense of local manifold structure.However, LPI is not efficient in time and memory, which makes it difficult to be applied to very large data set. 摘要LPI对于局部流形结构是优化的,但在时空上运行效率较低,使其很难应用于大型数据集。
- Knowledge discovery in databases is the nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in large data set. 数据库中的知识发现是指在大型数据集中识别有效、新奇、潜在有用、且最终可理解模式的非平凡的过程。
- Interactive exploration by users requires fast response times. This requirement presents challenges given the very large data sets over which such exploration is frequently conducted. 由于这种数据浏览频繁进行,而它所涉及的数据集又非常大,因此这个要求给人们提出了一道难题。
- A larger data set, one that could not be stored entirely in memory easily, would have required the entire application to be built around a database. 更大的数据集,比如不能完全存储到内存中的数据集,会要求整个应用程序都围绕着一个数据库构建。