2011
DOI: 10.1007/978-3-642-25591-5_31
|View full text |Cite
|
Sign up to set email alerts
|

Fully Retroactive Approximate Range and Nearest Neighbor Searching

Abstract: We describe fully retroactive dynamic data structures for approximate range reporting and approximate nearest neighbor reporting. We show how to maintain, for any positive constant d, a set of n points in R d indexed by time such that we can perform insertions or deletions at any point in the timeline in O(log n) amortized time. We support, for any small constant > 0, (1 + )-approximate range reporting queries at any point in the timeline in O(log n + k) time, where k is the output size. We also show how to an… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1

Citation Types

0
3
0

Year Published

2011
2011
2021
2021

Publication Types

Select...
2
2
1

Relationship

2
3

Authors

Journals

citations
Cited by 6 publications
(3 citation statements)
references
References 38 publications
0
3
0
Order By: Relevance
“…The notion of retroactive data structure helps to solve problems, such as dynamic shortest path problem (Sunita and Garg, 2018) and geometric problems, such as cloning Voronoi diagrams (Dickerson et al, 2010) and nearest neighbor search (Goodrich and Simons, 2011 time per operation. The data structure also supports the operation of determining the time at which an element was deleted from the data structure in O(lg 2 m).…”
Section: Related Workmentioning
confidence: 99%
“…The notion of retroactive data structure helps to solve problems, such as dynamic shortest path problem (Sunita and Garg, 2018) and geometric problems, such as cloning Voronoi diagrams (Dickerson et al, 2010) and nearest neighbor search (Goodrich and Simons, 2011 time per operation. The data structure also supports the operation of determining the time at which an element was deleted from the data structure in O(lg 2 m).…”
Section: Related Workmentioning
confidence: 99%
“…We will denote this linear ordering by the symbol ≤ Z . Considering points in their Zorder is a dimension reduction technique that is often used for proximity based data structures [7,19].…”
Section: Proximity Queriesmentioning
confidence: 99%
“…Then for each of the inner cells I ∈ I u ∪ I v we perform a 2-dimensional range reporting query with the rectangle [p i , p j ] × [z 0 , z 1 ], where z 0 and z 1 are the first and last point in I in Z-order, and record the union of the points reported in O(log w + k) time where k is output size. By a simple packing argument, the total number of inner cells is bounded by a function of the constants ε and d [19]. Thus, the total time for the query is O(log w + k).…”
Section: Omissions From Sectionmentioning
confidence: 99%