SHF: Small: Developing a Highly Efficient and Accurate Approximation System for Warehouse-Scale Computers with the Sub-dataset Distribution Aware Approach

尽管今天的仓库规模的计算机提供了巨大的数据处理能力，但从大数据集中获得临时查询答案仍然具有挑战性。为了解决这个问题，近年来已经看到一种趋势，即利用近似计算，通过在合理的程度上牺牲结果准确性来实现对原始数据的小得多的样本的更快执行。基于离线的采样方法和在线聚类采样解决方案已经逐渐部署在真实的世界中以加速大数据查询。教育的好处来自于扩大学生的经验，从一个顶级的西班牙裔博士。学位授予机构和加强计算机科学/工程课程活动。关于仓库规模的计算机和大数据的在线跨机构本科选修课程将有助于提供重新想象的学习体验，最大限度地利用当今的技术，并辅之以来自三所不同大学的学生广泛的媒体丰富的学习材料。开发一个软硬件集成、可扩展的近似系统是一个主要的困难。 主要的挑战是在给定的错误范围内最小化访问数据的总大小及其关联I/O开销。现有的流行的等概率整群抽样解决方案不能很好地处理许多现实世界中的应用，以下的非均匀分布。本研究旨在通过研究新的子数据集分布感知方法来捕获子数据集分布，特别是对于非均匀类型，应用不等概率的聚类抽样来解决非均匀子数据集分布引起的低效抽样和大方差问题，并考虑到抽样过程的独特属性以匹配计算机硬件特征，例如固态硬盘阵列，以充分发挥其潜力。该研究将确保未来的大数据近似系统能够实现高速的大数据分析，从而彻底改变人们与世界互动的方式;并通过高效和有效的数据处理提高经济影响的生产力。

Despite the fact that today's warehouse-scale computers supply enormous data processing capacity, getting an ad-hoc query answer from a big dataset remains challenging. To attack the problem, recent years have seen one trend to exploit approximate computing to achieve faster execution on a much smaller sample of the original data by sacrificing result accuracy to a reasonable extent. Both offline based sampling approaches and online cluster sampling solutions have been gradually deployed in a real world to accelerate big data query. Educational benefits arise from broadening the experience of students from a top ranked Hispanic Ph.D. degree awarding institution and enhanced computer science/engineering curriculum activities. The online cross-institution undergraduate elective course about warehouse-scale computer and big data will be helpful in providing a re-imagined learning experience that makes optimum use of today's technologies supplemented by a broad range of media-rich study materials that students from three different universities. There are major difficulties in developing an integrated hardware and software, scalable approximation system.  The main challenge is to minimize the total size of accessed data and its associative I/O overhead subject to a given error bound. Existing popular cluster sampling with equal probability solutions do not deal well with many real-world applications following a non-uniform distribution. This research aims to tackle those challenges by investigating new sub-dataset distribution aware methods to capture sub-dataset distributions especially for non-uniform types, applying cluster sampling with unequal probability to address the inefficient sampling and large variance problem caused by non-uniform sub-dataset distribution, and taking into account the unique properties of sampling process to match with the computer hardware features, such as SSD arrays to unleash their full potential. The research will ensure future big data approximation system enables high velocity of big-data analytics to revolutionize the way that people interact with the world; and high productivity improvement of the economic impact through the efficient and effective data processing.