Managing Inconsistencies in Data Grid Environments

Abstract

Much work has been done in the area of data access and integration using various data mapping, matching, and loading techniques. One of the main concerns when integrating data from heterogeneous data sources is data redundancy. The concern is mainly due to the different business contexts and purposes from which the data systems were originally built. A common process for accessing data from integrated databases involves the use of each data source’s own catalogue or metadata schema. In this article, the authors take the view that there is a greater chance of data inconsistencies, such as data redundancies when integrating them within a grid environment as compared to traditional distributed paradigms. The importance of improving the data search and matching process is briefly discussed, and a partial service oriented generic strategy is adopted to consolidate distinct catalogue schemas of federated databases to access information seamlessly. To this end, a proposed matching strategy between structure objects and data values across federated databases in a grid environment is presented.

The Big Picture, from Grids and Clouds to Crowds: A Data Collective Computational Intelligence Case Proposal for Managing Disasters

“…The concern is mainly due to the different contexts and purposes for which the data systems were originally built. In other words, the main concern resides on the view that data sources have been originally produced for purposes other than their integration [1]. Another fundamental aspect which also needs to be considered is that nowadays data is available and accessible to and from a wider audience, and thus, data must support a many-to-many exploitation type of relationship with their owners and/or on-demand users who are also geographically distributed.…”