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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. | ||
* Can only be implemented as a layer over IdAS | * Can only be implemented as a layer over IdAS | ||
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* [[Backlog]] | * [[Backlog]] | ||
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Revision as of 15:05, 18 March 2008
{{#eclipseproject:technology.higgins}}
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)