![]() ![]() This may result in overemphasizing only one of the three goals while neglect the others. Existing approaches either make idealistic assumptions on the statistical properties of the query, or completely disregard them. Ideally, the goal of solving the SSO problem is to achieve statistical accuracy, computational efficiency and broad applicability all at the same time. To build such an AQP system, finding the minimal sample size for a query regarding given error constraints in general, called Sample Size Optimization (SSO), is an essential yet unsolved problem. Nowadays, sampling-based Approximate Query Processing (AQP) is widely regarded as a promising way to achieve interactivity in big data analytics. Finally, we show theoretically and empirically that the $L^2$Miss algorithm and its extensions achieve satisfactory accuracy and efficiency for a considerably wide range of analytical queries. Moreover, we extend the $L^2$Miss algorithm to handle other error metrics. Afterwards, based on the MISS framework, we propose a concrete algorithm, called $L^2$Miss, to find optimal sample sizes under the $L^2$ norm error metric. Then, we propose a Model-guided Iterative Sample Selection (MISS) framework to solve the SSO problem generally. Then, based on the properties, we propose a linear model describing the relationship between sample sizes and the approximation errors of a query, which is called the error model. To overcome these limitations, we first examine carefully the statistical properties shared by common analytical queries. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |