Fast algorithms for finding nearest common ancestors
SIAM Journal on Computing
Good worst-case algorithms for inserting and deleting records in dense sequential files
SIGMOD '86 Proceedings of the 1986 ACM SIGMOD international conference on Management of data
Two algorithms for maintaining order in a list
STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
Communications of the ACM
Finding level-ancestors in trees
Journal of Computer and System Sciences
Maintaining hierarchical graph views
SODA '00 Proceedings of the eleventh annual ACM-SIAM symposium on Discrete algorithms
Improved Algorithms for Finding Level Ancestors in Dynamic Trees
ICALP '00 Proceedings of the 27th International Colloquium on Automata, Languages and Programming
The Level Ancestor Problem Simplified
LATIN '02 Proceedings of the 5th Latin American Symposium on Theoretical Informatics
How to Draw a Planar Clustered Graph
COCOON '95 Proceedings of the First Annual International Conference on Computing and Combinatorics
HGV: A Library for Hierarchies, Graphs, and Views
GD '02 Revised Papers from the 10th International Symposium on Graph Drawing
Range Searching Over Tree Cross Products
ESA '00 Proceedings of the 8th Annual European Symposium on Algorithms
Two Simplified Algorithms for Maintaining Order in a List
ESA '02 Proceedings of the 10th Annual European Symposium on Algorithms
Visual navigation of compound graphs
GD'04 Proceedings of the 12th international conference on Graph Drawing
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Range searching over tree cross products – a variant of classic range searching – recently has been introduced by Buchsbaum et al (Proc 8th ESA, vol 1879 of LNCS, pp 120–131, 2000) A tree cross product consist of hyperedges connecting the nodes of trees T1,...,Td In this context, range searching means to determine all hyperedges connecting a given set of tree nodes Buchsbaum et al describe a data structure which supports, besides queries, adding and removing of edges; the tree nodes remain fixed In this paper we present a new data structure, which additionally provides insertion and deletion of leaves of T1,...,Td; it combines the former approach with a novel technique of using search trees superimposed over ordered list maintenance structures The extra cost for this dynamization is roughly a factor of ${\mathcal O}({\rm log} {\it n}/{\rm log log} {\it n})$ The trees being dynamic is especially important for maintaining hierarchical graph views, a problem that can be modeled as tree cross product Such views evolve from a large base graph by the contraction of subgraphs defined recursively by an associated hierarchy The graph view maintenance problem is to provide methods for refining and coarsening a view In previous solutions only the edges of the underlying graph were dynamic; with the application of our data structure, the node set becomes dynamic as well.