Background:
A Window Aggregate function belongs to a class of functions, which have
emerged as a very important tool for Big Data Analytics. They lend support in analysis and decisionmaking
applications. A window aggregate function aggregates and returns the result by applying the
function over a limited number of tuples corresponding to current tuple and hence lending support for
big data analytics. We have gone through different patents related to window aggregate functions and its
optimization. The cost associated with Big data analytics, especially the processing of window functions
is one of the major limiting factors. However, now a number of optimizing techniques have evolved for
both single as well as multiple window aggregate functions.
Methods:
In this paper, the authors have discussed various optimization techniques and summarized the
latest techniques that have been developed over a period through intensive research in this area. The paper
tried to compare various techniques based on certain parameters like the degree of parallelism, multiple
window function support, execution time etc.
Results:
After analyzing all these techniques, segment tree data structure seems better technique as it
outperforms other techniques on different grounds like efficiency, memory overhead, execution speed
and degree of parallelism.
Conclusion:
In order to optimize the window aggregate function, segment tree data structure technique
is a better technique, which can certainly improve the processing of window aggregate function specifically
in big data analytics.
The Evidence Trace File: A Data Structure for Virtual Performance Assessments Informed by Data Analytics and Evidence‐Centered Design