Federated searching was supposed to be the panacea to information overload; the Holy Grail of one-stop searching -- simple interfaces providing seamless searching across logically clustered databases of information. However, with experience it became clear that seamless simultaneous searching alone could not adequately address the scalabiity issues required to mine large and complicated databases. Artificial intelligence methods can be used to further enhance the automated clustering and dynamic user-generated contributions. Harvesting data and metadata is accomplished by having automated robots/agents capture data from specific host platforms. In addition to the advantages gained from harnessing computer power to classify and analyze material, there is another significant enhancement possible through the preprocessing function. Preprocessing allows for the incorporation of associations discovered via dynamic social tagging. This means including reader-contributed added value into the search domain. It is hard to contemplate the many ways that the preprocessing of data and metadata can enhance searching and navigating among large data sets.
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