Model-driven reverse engineering for data warehouse design

Abstract

Abstract-Data warehouses integrate several operational sources to provide a multidimensional analysis of data, thus improving the decision making process. Therefore, an in-depth analysis of these data sources is crucial for data warehouse development. Traditionally, this analysis has been based on a set of informal guidelines or heuristics to support the manually discovery of multidimensional elements on a well-known documentation. Therefore, this task may become highly tedious and prone to fail. In this paper, MDA (Model Driven Architecture) is used to design a reverse engineering process in which the following tasks are performed (i) obtain a logical representation of data sources (ii) mark this logical representation with multidimensional concepts, and (iii) derive a conceptual multidimensional model from the marked model.

Generative component-based IP camera design

“…To tackle this problem and allow the development of highly dedicated and with very short time to market solutions through the use of component-based design, this paper presents model-driven generative component design. Several uses of model-driven generative design have been presented in the literature but almost exclusively focusing the software-only domain ( [8], [9], [10] and [11]). Only a few studies show the use of these techniques at the hardware level ( [12] and [13]).…”

Performance Improvement Techniques for Customized Data Warehouse

“…In the cases where the number of records in relational database becomes very high, the query processing time becomes very long [6]. Data warehouses [7][8][9] which store consolidated historic data can be constructed from relational databases. Data warehouse (DW) database store very small number of records for a large number of records in relational database [6].…”

“…In this paper, we have studied different techniques to improve performance of data retrieval from relational database as well as DW [8,9] database for different sizes of data. We have used bitmap indexing (BMI) [10] and data partitioning techniques to improve performance of data retrieval from data warehouse database [6].…”