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Difference between revisions of "Deep Search and Access Backlog"

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Although the Higgins data model supports [[Node Relation]]s as first-class objects, IdAS in Higgins 1.0 treats them shallowly. For example, a search (e.g. using an IdAS Filter) treats [[Node Relation]]s no differently from other attributes. Many use-cases (esp. cross-contextual use cases) would expect that these links would be recursively traversed to N levels, with cycle detection, etc. and the attributes merged roughly analogous to inheritance.
 
Although the Higgins data model supports [[Node Relation]]s as first-class objects, IdAS in Higgins 1.0 treats them shallowly. For example, a search (e.g. using an IdAS Filter) treats [[Node Relation]]s no differently from other attributes. Many use-cases (esp. cross-contextual use cases) would expect that these links would be recursively traversed to N levels, with cycle detection, etc. and the attributes merged roughly analogous to inheritance.

Latest revision as of 09:35, 16 December 2008

{{#eclipseproject:technology.higgins|eclipse_custom_style.css}}

Higgins logo 76Wx100H.jpg

Although the Higgins data model supports Node Relations as first-class objects, IdAS in Higgins 1.0 treats them shallowly. For example, a search (e.g. using an IdAS Filter) treats Node Relations no differently from other attributes. Many use-cases (esp. cross-contextual use cases) would expect that these links would be recursively traversed to N levels, with cycle detection, etc. and the attributes merged roughly analogous to inheritance.

  • Can only be implemented as a layer over IdAS

Examples of new (deep) search functions

Given DS A in context C find all other DSes with relationships to A. Parameters:

  • [optional] min threshold of relationship level
  • [optional] within context C or across "all" Contexts (whatever all means!)
  • [optional] max of N degrees of separation (hops)
  • [optional] sort by relationship/context or context/relationship

Given DS A in context C find other contexts Cn where Cn != C such that C contains DSes with a relationship to A

Given a DS A in context C return the union of all all attributes (and their values) of A by following all correlation links from A Parameters:

  • [optional] max of N degrees of separation (hops)

See Also

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