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Indexing Strategy

As we have already mentioned, the basic indexing strategy is to match probes (fragments of queries) with index terms (fragments of content labels). We now discuss the details of the distributed algorithm that accomplishes this matching. This algorithm can be characterized as a ``scatter-gather'' technique. Queries are sent to a front-end processor in the form of datagrams. The front-end processor assigns a query id, acknowledges receipt of the query and forwards the query to a randomly chosen node of the search engine. This is the first scattering step. The node that is assigned the query is called the home node of the query.

At the home node, the query is broken apart into probe fragments as discussed in section 3 above. The fragmentation algorithm is more subtle than one would expect, since loops and multiple edges are allowed in keynets. Each probe is then hashed using a standard algorithm given in [Section 6.4]Knuth73. The hash value is in two parts. One part is a node number and the other part is the local hash value used at that node. The local hash value and the query identifier are then sent to the node that was selected by the hash value. This forms the second scatter step of the algorithm. The result of hashing is to scatter the probes uniformly to all of the nodes of the search engine.

Upon receiving the local hash value of a probe, the node looks it up in its local hash table. The hash table algorithm we employ is called ``open addressing with double hashing,'' as described in [Section 6.4]Knuth73. We found that this technique is very space efficient and that collisions do not affect performance even when the hash table is 90%full. An index term in the hash table that matches a probe is called a ``hit.'' The hits are sent back to the home node of the query. This is the ``gather'' step of the algorithm. Special trailer messages are used for determining when all the hits of all the probes of a query have been collected. The home node then computes the similarity measure (currently the cosine measure) of each object in the collection, and the objects are ordered by the degree of similarity. The object identifiers of the most relevant objects are then sent back to the user.

The insertion of a new content label in the index is done in a manner very similar to the query algorithm. Since content labels and queries are both keynets conforming to the same keynet ontology, they use exactly the same data structure. The same fragmentation, hashing and scattering algorithms are used for content labels as for queries. The only difference is that instead of matching entries in the hash table, index terms are inserted into the table. Note that index terms are not explicitly stored in the index, just their hash values are stored. The number of bits in the hash value is chosen to be so large that it is very unlikely that two probes would have the same hash value. As a result it suffices to store only a hash value and not the index term itself. Since an index term is nearly always much larger than a local hash value, this results in a significant savings of space with only a slight reduction in retrieval effectiveness.

The query algorithm presented so far represents the basic level of service. Higher levels of service can be provided by using additional scatter-gather operations. For example, the second level of service uses two scatter-gather operations. After completing the collection phase of the basic level of service, the home node sends each object identifier to the node where its content label is stored, resulting in yet another scatter step. The content label is then retrieved and sent back to the user's computer where the content labels are gathered, arranged and displayed using the keynet ontology.

Next: Performance Up: A unified approach to Previous: The KEYNET Model
Fri Jan 20 21:43:28 EST 1995