
	Discovery Engine The discovery engine crawls through documents to infer meaning, value and relationships amongst people and content. On the basis of what the Raven engine discovers, it creates a browsable catalog of content and expertise. The catalog is a "table of contents" of all the written information and internal expertise that exists within an organization. The engine constantly refreshes the catalog by tracking user characteristics and usage activity. The result is a system that reflects much about an organization in terms of where things are, who knows what, what is important, and what subjects generate the most interest and interactivity. The discovery engine has two main components: an expertise locator and a content catalog.
	*Expertise Locator* - builds and maintains profiles in a repository that can be queried directly by users to locate experts by skill, experience, project, education, job type and many other attributes. The profiles are created through a variety of measures: drawing demographic data drawn from any LDAP directory; field mapping from specific applications such as teamrooms, discussions, and project tracking. The expertise locator also uses a metrics tool to determine affinities between subject matter and user activity to infer interests and expertise. Before adding this discovered content to an individual profile, Raven presents the end user with the update, which must be approved by him or her before it can be published and searched by the departmental or enterprise population. *Content Catalog* - crawls text sources (including text about people) to identify subject matter topics. It analyzes content by looking for frequency, proximity to other topics, relationship to people and a host of other measures. From this information the content catalog groups similar content into browsable categories, called a content map, which it constantly maintains and updates as it receives new content and usage data. Raven's content catalog then derives people's skills from content they have authored or read and maps them to the categories alongside documents. The catalog continually analyzes new content to calculate usage patterns and relationships to determine the value and relevance of people and content to one another. By subscribing to specific categories that interest them, users can direct their knowledge portals to deliver to them relevant information about news, projects, people and organizational structure. Raven uses IBM's DB2 Universal Database as the underlying technology to manage the catalog and the complex analyses required to sift through millions of documents.