The DNA microarray is an important technique that allows researchers to analyze many gene expression data in parallel. Although the data can be more significant if they come out of separate experiments, one of the most challenging phases in the microarray context is the integration of separate expression level datasets that have gathered through different techniques. In this paper, we prese...

Abstract Ensuring the quality of integrated data is undoubtedly one main problems systems. When focusing on multi-national and historical integration systems, where “space” “time” dimensions play a relevant role, it very much important to build layer in such way that final user accesses “by design” as complete possible. In this paper, we propose method for accessing multipurpose infrastructures...

In this paper, we propose solutions to some problems arising while data from different sources is to be integrated under a given target schema. We address the following problems: inferring missing data based on constraints imposed by the target schema, generating mappings from a source schema to a target schema based on key constraints and value dependencies, and merging data based on subsumpti...

M scientific and policy investigations require statistical analyses that “integrate” data stored in multiple, distributed databases. For example, a regression analysis on integrated state databases about factors influencing student performance would be more insightful than individual analyses, or complementary to them. Other contexts where the same need arises range from homeland security to en...

Data integration is a central problem in the design of Data Wareshouses and Decision Support Systems. When data passes from the sources of the application-oriented operational environment to the Data Warehouse, possible inconsistencies and redundancies should be resolved, so that the warehouse is able to provide an integrated and reconciled view of data of the organization. Generally speaking, ...

INTRODUCTION Integration of data sources refers to the task of developing a common schema as well as data transformation solutions for a number of data sources with related content. The large number and size of modern data sources make manual approaches at integration increasingly impractical. Data mining can help to partially or fully automate the data integration process.