Conversely, libraries work predominantly with large unstructured text documents from diverse sources. While a number of text-mining tools do provide access to minimally structured text documents, the total amount of information they provide access to is small in comparison with that found in a large library. The third problem with data mining is that it faces the same difficulties as other searching mechanisms. The quality of data is critical for successful data mining, just as it is for successful searching by other methods. If information is not structured in a way that allows pattern discovery, the likelihood of extracting meaningful information from the data is greatly reduced.
Data mining looks for patterns in data.
It is very difficult for data mining tools to identify the relationships between different information objects when it is not possible to determine the meaning of the data. Despite advances in technology, it is not practical to use all processing techniques on all documents in a given search, except when small sets of data are concerned. Unless all data can be stored in memory and there is sufficient processing power, heuristics must be used to determine the optimal searching strategy. Users may reveal information about themselves and the purpose of their searches in the way they phrase their queries, but it is difficult to glean enough information to identify techniques that will optimally serve the user. Moreover, effective techniques for indexing and retrieving non-textual data are not yet available.