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

The University Of Central Florida Board Of Trustees, Orlando FL

Investigators

Abstract

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.