Databases and Systems

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However, for the arbitrary N-ary fact, the objects are linked to each other
conceptually like the nodes of a semantic network. The Qualifiers act somewhat like
the edges in the network, but they are not fully satisfactory for this purpose given the
current structure. Therefore, it is often hard to reconstruct the semantics of an N-ary
fact given the data in the Associations table alone. (Even in cases where it is not hard,
it generally requires several computational steps.) To avoid this (generally unneeded)
computation, the "Narrative" field in the Facts table stores an explicit textual
description of the N-ary fact for the user's perusal.
By an analogy with Information-Retrieval methods, the Associations table may be
regarded as an index (17) to the narrative text, for the purpose of rapid retrieval. The
only difference between the Association table and the inverted files created by freetext
indexing engines (e.g., for Web-searchable document collections) is that the
index-term vocabulary is more controlled with the Associations table. The similarity,
however, is that, in both cases, complex Boolean retrieval (e.g., list all neurons where
Dopamine has an inhibitory role) requires set operations, such as Union. Intersection,
and Difference, on subsets (projections/ selections) of the Associations table with
each other. Relational set operations are computationally less efficient than the
equivalent AND, OR and NOT operations that would have been needed with, say, a
classical Binary-Relationship table, but the plus feature is flexibility and a simple
structure. (For example, multiple object instances on a single axis do not need to be
managed through separate many-to-one related tables.) Also, in practice a significant
proportion of queries tend to be based on a single axis rather than multiple ones.
Such queries can be answered by locating the fact IDS corresponding to a particular
class instance, and then simply returning the narrative for those IDS.
Managing Hierarchical Associations
While the Associations table can manage arbitrary N-ary data, that does not mean
that every association in the database must be stored this way. Use of the
Associations table should be restricted to represent highly heterogeneous facts
(where both the number and the nature of the axes vary greatly). Facts best managed
in the orthodox fashion include parent-child relationships, a special category of
binary relationship. These are seen quite commonly in the NS, for example, with
receptors (which have subtypes), and anatomical structures (which have substructures).
We have mentioned earlier the need to preprocess queries accessing hierarchical
data. This way, a query specified at a coarser level of granularity must also be able to
retrieve facts stored in the database at a finer level of granularity, without having to
store facts redundantly at multiple granularity levels. Standard transitive-closure
algorithms for this purpose have been well-researched for the "Bill of Materials
Problem" (18). Limited transitive-closure support will be provided in SQL-3 (19).