References in periodicals archive ?
CWIS supports hierarchical browsing interfaces, based on classifications (tree fields) assigned to the resources.
Since classification hierarchies may be either wide (a large number of entries at the top level) or deep (a large number of levels) or both, the CWIS browsing interface is dynamically generated, based on the structure of the classification tree.
To provide users with some idea of the distribution of entries through the classification tree, CWIS displays the number of resources present under any branch of the tree.
CWIS provides two separate search mechanisms, both based on Scout's OSMASE search engine.
CWIS supports most of the conventions offered by sites like Google, such as phrase searching (enclosing several words in quotation marks to indicate that the user is looking for the words in that specific sequence) and term exclusion (prepending a minus sign to a word to indicate that the user only wants results that do not include that term).
To better take advantage of the precision offered by the metadata assigned to resources, CWIS also supports fielded searching.
Because of the potential complexity of a fielded search, CWIS provides the ability for each user to save a set of search parameters and recall them at a later date to run the search again.
Combining this ability to save fielded searches with what has become known in Internet jargon as "push technology," CWIS also offers a feature sometimes referred to as "user agents.
For resource metadata administrators, CWIS also offers the option to run these searches on an hourly basis, which may facilitate editorial review or other workflow processes set up among a group of collection developers.
To leverage user community participation, CWIS offers three features: resource ratings, resource recommendations, and resource comments.
To take advantage of this information, CWIS includes a recommender system.
The facility provided by CWIS operates in a similar fashion, although it is a content-based recommender system rather than a collaborative recommender system such as Amazon's.