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One of the main challenges in data matching and data cleaning, in highly integrated systems, is duplicates detection . While the literature abounds of approaches detecting duplicates corresponding to the same real-world entity, most of these approaches tend to eliminate duplicates (wrong information) from the sources, hence leading to what is called data repair. In this article, we propose a framework that automatically detects duplicates at query time and effectively identifies the consistent version of the data, while keeping inconsistent data in the sources. Our framework uses matching dependencies (MDs) to detect duplicates through the concept of data reconciliation rules (DRR) and conditional function dependencies (CFDs) to assess the quality of different attribute values. We also build a duplicate reconciliation index ( DRI ), based on clusters of duplicates detected by a set of DRRs to speed up the online data reconciliation process. Our experiments of a real-world data collection show the efficiency and effectiveness of our framework.

We present a scalable distributed database system SD-SQL Server. Its original feature is the scalable distributed partitioning of its relational tables. The system dynamically distributes the tables into segments created each at a different SD-SQL Server node. The partitioning is transparent to the applications. New segments result from splits following overflowing inserts. SD SQL Server avoids the periodic and cumbersome manual reorganizing of scaling tables, characteristic of the current DBMS technology. With the comfort of a single node SQL Server database user, the SD-SQL Server user may dispose of many times larger tables. We present the architecture of our system, and its user/application interface. Related work discusses our implementation and shows that the overhead of our scalable distributed table management should be typically negligible.

The most important benefit of Cloud Computing is that organizations no longer need to expend capital up-front for hardware and software purchases. Indeed, all services are provided on a pay-peruse basis. The cloud services market is forecast to grow, and numerous providers offer database as a service (DBaaS). Nevertheless, as the number of DBaaS' offerings increases, it becomes difficult to compare various offerings through checking of a documentation ads-oriented. In this paper, we propose and describe DBaaS-Expert -a framework which helps a user to choose the right DBaaS Cloud Provider among DBaaS' offerings. The core components of DBaaS-Expert is first an ontology which captures cloud data management systems services concepts, and second a ranking core which scores each DBaaS offer in terms of criteria.