Name
Abstract | ||
|---|---|---|
Data-intensive software programs typically transfor m a set of source data values into target data values. While the group of developers who write, compile and test the software or the query have a clear understanding of the transformation lo gic (or transformation rules) at the time the program is cr eated, that understanding can quickly fade at an enterprise lev el for a variety of reasons, including poor documentation, loss of t he source (uncompiled) version of the software, loss of the d evelopers who wrote the software, or lack of available skills in the programming language (e.g., COBOL). This leaves the enterprise in a precarious position of not being able to maintain, upgrade or migrate the software programs at the heart of their operations unless they can recreate the transformations that r elate the source data to the target data. In this paper, we propose a technique to reverse en gineer transformation rules by analyzing the data using da ta mining algorithms and processing the results of these algo rithms. We demonstrate our technique using a prototype implementation and prove its correctness with a sample data set. |
Year | Venue | Keywords |
|---|---|---|
2008 | NTII | data mining,programming language,cobol |
Field | DocType | Citations |
Data mining,Computer science,K-optimal pattern discovery | Conference | 1 |
PageRank | References | Authors |
0.37 | 2 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Holger Kache | 1 | 34 | 2.11 |
| Yannick Saillet | 2 | 1 | 0.37 |
| Mary Roth | 3 | 43 | 5.66 |