Name
Abstract | ||
|---|---|---|
Abstract data types (ADTs) represent the core for any software application, and a proper use of them is an essential requirement for developing a robust and efficient system. Moreover, a proper instantiation of a data structure that implements an abstract data type can greatly impact the performance of the system. In this paper we propose a learning approach for the dynamic configuration of data structures instances in a software system. In order to adapt a data structure to the system’s current execution context, a neural network will be used and an agent based system is proposed. We experimentally evaluate our system on a case study, emphasizing the advantages of the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/SYNASC.2009.25 | Symbolic and Numeric Algorithms for Scientific Computing |
Keywords | Field | DocType |
software system,proper instantiation,efficient system,proper use,software application,dynamic customization,data structures,data structures instance,abstract data type,case study,data structure,learning artificial intelligence,supervised learning,software systems,machine learning,abstract data types,neural nets,neural network,software agents | Abstract data type,Data structure,Data mining,Computer science,Software agent,Software system,Theoretical computer science,Supervised learning,Software,Artificial neural network,Personalization | Conference |
ISSN | ISBN | Citations |
2470-8801 | 978-1-4244-5911-7 | 1 |
PageRank | References | Authors |
0.36 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| István Gergely Czibula | 1 | 91 | 11.79 |
| Gabriela Czibula | 2 | 80 | 19.53 |
| Adriana Mihaela Guran | 3 | 2 | 4.51 |