Hierarchical and Dynamic k-Path Covers
A metric-independent data structure for spatial networks called k-all-path cover (k-APC) has
recently been proposed. It involves a set of vertices that covers all paths of size k, and is a
general indexing technique that can accelerate various path-related processes on spatial
networks, such as route planning and path subsampling to name a few. Although it is a
promising tool, it currently has drawbacks pertaining to its construction and maintenance.
First, k-APCs, especially for large values of k, are computationally too expensive. Second, an …
recently been proposed. It involves a set of vertices that covers all paths of size k, and is a
general indexing technique that can accelerate various path-related processes on spatial
networks, such as route planning and path subsampling to name a few. Although it is a
promising tool, it currently has drawbacks pertaining to its construction and maintenance.
First, k-APCs, especially for large values of k, are computationally too expensive. Second, an …
Hierarchical and Dynamic k -Path Covers
A metric-independent data structure for spatial networks called k-all-path cover (k-APC) has
recently been proposed. It involves a set of vertices that covers all paths of size k, and is a
general indexing technique that can accelerate various path-related processes on spatial
networks, such as route planning and path subsampling to name a few. Although it is a
promising tool, it currently has drawbacks pertaining to its construction and maintenance.
First, k-APCs, especially for large values of k, are computationally too expensive. Second, an …
recently been proposed. It involves a set of vertices that covers all paths of size k, and is a
general indexing technique that can accelerate various path-related processes on spatial
networks, such as route planning and path subsampling to name a few. Although it is a
promising tool, it currently has drawbacks pertaining to its construction and maintenance.
First, k-APCs, especially for large values of k, are computationally too expensive. Second, an …
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