Size Estimation for Query Results Using Histograms
نویسندگان
چکیده
منابع مشابه
Query-Condition-Aware Histograms in Selectivity Estimation Method
The paper shows an adaptive approach to the query selectivity estimation problem for queries with a range selection condition based on continuous attributes. The selectivity factor estimates a size of data satisfying a query condition. This estimation is calculated at the initial stage of the query processing for choosing the optimal query execution plan. A non-parametric estimator of probabili...
متن کاملCost Estimation Techniques for Database Systems
This dissertation is about developing advanced selectivity and cost estimation techniques for query optimization in database systems. It addresses the following three issues related to current trends in database research: estimating the cost of spatial selections, building histograms without looking at data, and estimating the selectivity of XML path expressions. The first part of this disserta...
متن کاملA Histogram Utilizing the Cluster Information
Histograms are summary structures of large datasets, which are mainly used for selectivity estimation during query optimization. Selectivity estimation is the fast approximation of query result size. In this paper, we focus on multi-dimensional histograms, especially bidimensional histograms. At the time of selectivity estimation, buckets partially overlapping with a query return approximated r...
متن کاملCombining Histograms and Parametric Curve Fitting for Feedback-Driven Query Result-size Estimation
This paper aims to improve the accuracy of query result-size estimations in query optimizers by leveraging the dynamic feedback obtained from observations on the executed query workload. To this end, an approximate \synopsis" of data-value distributions is devised that combines histograms with parametric curve tting, leading to a speci c class of linear splines. The approach reconciles the bene...
متن کاملSASH: A Self-Adaptive Histogram Set for Dynamically Changing Workloads
Most RDBMSs maintain a set of histograms for estimating the selectivities of given queries. These selectivities are typically used for costbased query optimization. While the problem of building an accurate histogram for a given attribute or attribute set has been well-studied, little attention has been given to the problem of building and tuning a set of histograms collectively for multidimens...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2008