Efficient management of transitive relationships in large data and knowledge bases
ACM SIGMOD Record, 1989•dl.acm.org
We argue that accessing the transitive closure of relationships is an important component of
both databases and knowledge representation systems in Artificial Intelligence. The
demands for efficient access and management of large relationships motivate the need for
explicitly storing the transitive closure in a compressed and local way, while allowing
updates to the base relation to be propagated incrementally. We present a transitive closure
compression technique, based on labeling spanning trees with numeric intervals, and …
both databases and knowledge representation systems in Artificial Intelligence. The
demands for efficient access and management of large relationships motivate the need for
explicitly storing the transitive closure in a compressed and local way, while allowing
updates to the base relation to be propagated incrementally. We present a transitive closure
compression technique, based on labeling spanning trees with numeric intervals, and …
We argue that accessing the transitive closure of relationships is an important component of both databases and knowledge representation systems in Artificial Intelligence. The demands for efficient access and management of large relationships motivate the need for explicitly storing the transitive closure in a compressed and local way, while allowing updates to the base relation to be propagated incrementally. We present a transitive closure compression technique, based on labeling spanning trees with numeric intervals, and provide both analytical and empirical evidence of its efficacy, including a proof of optimality.
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