2. Comparing Document Distributions: KDT can compare the distributions of keywords in two collections of documents containing similar keywords and display the results using tables and graphs.
3. Trend Analysis: KDT can compare the distributions of keywords in documents from different points in time and display the results using tables and graphs.
4. Association Discovery: KDT can search for several types of associations (e.g. Toivonen et al, 1995) between classes of documents.
5. Further, KDT includes a browsing facility in which the user can click on any discovered pattern and get the list of documents that contributed to the pattern.
These operations can assist users that have to analyze and assimilate information
spanning over a large number of documents, such as in business intelligence and economical analysis. For example, using the system an analyst can find out quickly the
most active economical areas for certain countries, or major products of companies.
Furthermore, the analyst can compare such a company “profile” to profiles of other
companies in the same business area, and discover distinguishing aspects in the activity
of the company. In business intelligence applications, the user may be interested in
comparing profiles of different companies to identify relatively weak and strong areas in
their activity, while in marketing applications an analyst may want to compare country
profiles when looking for appropriate international markets for a product. Other
types of KDT queries can answer questions like “find economical areas which are
dominant in the economies of some (unspecified) countries”, or “find economical
areas in which activity has increased or decreased in a specified period of time”.
Investigation of issues such as mentioned above is not supported directly in
conventional information retrieval systems, and typically requires a lot of manual effort
in retrieving and analyzing a large number of documents. It should be kept in mind that the answers to all KDT queries rely on document frequencies in some information source (such as a newswire or a professional magazine), which may introduce quantitative biases with respect to the real situation described in the texts. For example, an interesting story, from the media’s point of view, may be covered in a large number of articles, inflating the statistics of some items. To support verification of KDT’s finding, and for gaining further insights into them, the system provides a direct link from the results of each query to the documents which support that result.
Previous Next
3. Trend Analysis: KDT can compare the distributions of keywords in documents from different points in time and display the results using tables and graphs.
4. Association Discovery: KDT can search for several types of associations (e.g. Toivonen et al, 1995) between classes of documents.
5. Further, KDT includes a browsing facility in which the user can click on any discovered pattern and get the list of documents that contributed to the pattern.
These operations can assist users that have to analyze and assimilate information
spanning over a large number of documents, such as in business intelligence and economical analysis. For example, using the system an analyst can find out quickly the
most active economical areas for certain countries, or major products of companies.
Furthermore, the analyst can compare such a company “profile” to profiles of other
companies in the same business area, and discover distinguishing aspects in the activity
of the company. In business intelligence applications, the user may be interested in
comparing profiles of different companies to identify relatively weak and strong areas in
their activity, while in marketing applications an analyst may want to compare country
profiles when looking for appropriate international markets for a product. Other
types of KDT queries can answer questions like “find economical areas which are
dominant in the economies of some (unspecified) countries”, or “find economical
areas in which activity has increased or decreased in a specified period of time”.
Investigation of issues such as mentioned above is not supported directly in
conventional information retrieval systems, and typically requires a lot of manual effort
in retrieving and analyzing a large number of documents. It should be kept in mind that the answers to all KDT queries rely on document frequencies in some information source (such as a newswire or a professional magazine), which may introduce quantitative biases with respect to the real situation described in the texts. For example, an interesting story, from the media’s point of view, may be covered in a large number of articles, inflating the statistics of some items. To support verification of KDT’s finding, and for gaining further insights into them, the system provides a direct link from the results of each query to the documents which support that result.
Previous Next
No comments:
Post a Comment