4 research outputs found
Optimization of Analytic Window Functions
Analytic functions represent the state-of-the-art way of performing complex
data analysis within a single SQL statement. In particular, an important class
of analytic functions that has been frequently used in commercial systems to
support OLAP and decision support applications is the class of window
functions. A window function returns for each input tuple a value derived from
applying a function over a window of neighboring tuples. However, existing
window function evaluation approaches are based on a naive sorting scheme. In
this paper, we study the problem of optimizing the evaluation of window
functions. We propose several efficient techniques, and identify optimization
opportunities that allow us to optimize the evaluation of a set of window
functions. We have integrated our scheme into PostgreSQL. Our comprehensive
experimental study on the TPC-DS datasets as well as synthetic datasets and
queries demonstrate significant speedup over existing approaches.Comment: VLDB201
Aggregate Analytic Window Query over Spatial Data
Analytic window query is a commonly used query in the relational databases.
It answers the aggregations of data over a sliding window. For example, to get
the average prices of a stock for each day. However, it is not supported in the
spatial databases. Because the spatial data are not in a one-dimension space,
there is no straightforward way to extend the original analytic window query to
spatial databases. But these queries are useful and meaningful. For example, to
find the average number of visits for all the POIs in the circle with a fixed
radius for each POI as the centre. In this paper, we define the aggregate
analytic window query over spatial data and propose algorithms for grid index
and tree-index. We also analyze the complexity of the algorithms to prove they
are efficient and practical