Year: 1991
Type: article
Abstract: This paper introduces a new algorithm for incremental attribute evaluation.The algorithm is lazy: Itevaluates only theattributes that are both affected byachange andthat are directly or indirectly observable by the user.In this way, the wasted work of computing values that are never actually used is avoided.Although the algorithm is not optimal, it performs better than the standard "optimal" algorithm in cases where expensive but optional computations need to be supported.Furthermore, the algorithm does not have some of the limitations of other algorithms.It works for general attributed graphs as well as for standard attributed trees.In addition, it does not presume any special editing model, and it supports multiple change points without loss of efficiency.
Author: Scott E. Hudson
Institution: University of Arizona
Language: English
Topic: Graph Theory and Algorithms
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W1990597096