SUPPORTING LINK ANALYSIS USING ADVANCED QUERYING METHODS ON SEMANTIC WEB DATABASES

There is an increasing demand for technologies that can help organizations unearth actionable knowledge from their data assets. This demand continues to drive the flurry of activities in data mining research where the emphasis is on technologies that can identify patterns in data. However, in addition to the “patterns ” view of data, other data and knowledge perspectives are required to support the broad range of complex analytical tasks found in contemporary applications. For example, in some applications in homeland security, bioinformatics, business and other investigative domains many tasks are focused on “connecting the dots”. For this genre of applications, support for identifying, revealing and analyzing links or relationships between groups of entities (link analysis) is crucial. Currently, mainstream database systems do not provide support for such analyses and current solutions rely on exporting their data from their databases into custom applications to be analyzed. This has the disadvantage of additional overhead and precludes the ability to exploit other mature technologies offered by today’s database systems. This thesis argues for database support for link analysis by providing an appropriat