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Functional reactive programming

About: Functional reactive programming is a research topic. Over the lifetime, 3415 publications have been published within this topic receiving 89654 citations. The topic is also known as: FRP.


Papers
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Journal ArticleDOI
Gregor Kiczales1
TL;DR: This work proposes to use aspect-orientation to automate the calculation of statistics for database optimization and shows how nicely the update functionality can be modularized in an aspect and how easy it is to specify the exact places and the time when statistics updates should be performed to speed up complex queries.
Abstract: The performance of relational database applications often suffers. The reason is that query optimizers require accurate statistics about data in the database in order to provide optimal query execution plans. Unfortunately, the computation of these statistics must be initiated explicitly (e.g., within application code), and computing statistics takes some time. Moreover, it is not easy to decide when to update statistics of what tables in an application. A well-engineered solution requires adding source code usually in many places of an application. The issue of updating the statistics for database optimization is a crosscutting concern. Thus we propose to use aspect-orientation to automate the calculation. We show how nicely the update functionality can be modularized in an aspect and how easy it is to specify the exact places and the time when statistics updates should be performed to speed up complex queries. Due to the automatic nature, computation takes place on time for complex queries, only when necessary, and only for stale tables. The implementation language for the automated aspect-oriented statistics update concern is AspectJ, a well known and mature aspect-oriented programming language. The approach can however be implemented in any other aspect-oriented language. Unlike in traditional object-oriented pattern solutions, e.g. using the interceptor pattern, we do not have to modify existing code.

5,161 citations

01 Jan 2009
TL;DR: In this article, the authors present the first textbook that fully explains the neuro-dynamic programming/reinforcement learning methodology, which is a recent breakthrough in the practical application of neural networks and dynamic programming to complex problems of planning, optimal decision making, and intelligent control.
Abstract: From the Publisher: This is the first textbook that fully explains the neuro-dynamic programming/reinforcement learning methodology, which is a recent breakthrough in the practical application of neural networks and dynamic programming to complex problems of planning, optimal decision making, and intelligent control.

4,251 citations

Book
01 Jan 1996
TL;DR: This is the first textbook that fully explains the neuro-dynamic programming/reinforcement learning methodology, which is a recent breakthrough in the practical application of neural networks and dynamic programming to complex problems of planning, optimal decision making, and intelligent control.
Abstract: From the Publisher: This is the first textbook that fully explains the neuro-dynamic programming/reinforcement learning methodology, which is a recent breakthrough in the practical application of neural networks and dynamic programming to complex problems of planning, optimal decision making, and intelligent control

3,665 citations

Proceedings ArticleDOI
05 Jun 2000

2,162 citations

Book
01 Jan 1981
TL;DR: Describes basic programming principles and their step-by- step applications and shows how to apply them to real-world problems.
Abstract: Describes basic programming principles and their step-by- step applications. Numerous examples are included.hIt?4001? 0000000

1,457 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20234
202212
20215
202013
201911
201826