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Approximate Distance Oracles for Geometric Spanner Networks

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Title: Approximate Distance Oracles for Geometric Spanner Networks


1
Approximate Distance Oracles for Geometric
Spanner Networks
  • Joachim Gudmundsson TUE, Netherlands
  • Christos Levcopoulos Lund U., Sweden
  • Giri Narasimhan Florida Intl U., Miami, USA
  • Michiel Smid Carleton U., Ottawa, Canada

2
Problem
  • Preprocess a geometric spanner network so that
    approximate shortest path lengths between two
    query vertices can be reported efficiently (using
    subquadratic space).

3
Main Results
  1. Let N be a geometric t-spanner for a set S of n
    points in ?d with m edges. N can be preprocessed
    so that (1?)-approximate shortest path lengths
    between two query points from S can be reported
    efficiently.

?
  • Preprocessing O(m nlogn)
  • Space O(m nlogn)
  • Query O(1)
  • Floor function not used. Only indirection.
  • No restrictions on interpoint distances.

4
Main Results
  1. Let N be a geometric t-spanner network of a set
    S of n points in ?d. A (1?)-spanner N of N can
    be computed in O(m nlogn) time such that N has
    only O(n) edges.
  • Floor function not used. Only indirection.
  • No restrictions on interpoint distances.

5
Main Results
  • Let V be a set of points in ?d with interpoint
    distances in the range D, D?k. We can
    preprocess V in O(n logn) time and O(n) space
    such that for any two points p,q ? V, we can
    compute in O(1) time,
  • BIndex(p,q) ?log?(pq/D)?
  • without the use of the floor function.

6
Previous Work
  • General Weighted Graphs
  • Cohen Zwick 97, Zwick98, Dor et al. 00,
    Thorup Zwick 01
  • Preprocess , Space
    , Approx
  • Klein 02 (Planar Networks) Query O(k)
  • Baswana Sen 04 (Unweighted Graphs)
  • Geometric Graphs Domains
  • Clarkson 87, Arikati et al. 96, Chen 95,
  • Chiang Mitchell 99, Chen et al. 00
  • Preprocess , Space
    , Approx 3,
  • Query O(log n)

7
Basic Idea
Preprocessing
  • Given a t-spanner network N, construct a
  • (1?)-spanner N of N with O(n) edges
  • Build a sequence of p O(logn) cluster graphs
  • H1 ? H2 ? ? Hi ? ? Hp
  • Each Hi has only edges of length in the range
  • (?D?i-1? tD?i and degree bounded by a
    constant.
  • For query (p,q), find i such that pq ? (D?i-1?
    D?i.
  • Report distance between p and q in Hi.

O(mnlogn)
O(mnlogn)
Search
O(1)
8
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9
Applications
10
Approximate Stretch Factors
  • PATH NETWORKS
  • O(nlogn)
  • CYCLE NETWORKS
  • O(nlogn)
  • TREE NETWORK
  • O(nlog2n) O(nlogn)
  • PLANAR NETWORKS
  • O(n3/2logn) O(nlogn)
  • ARBITRARY NETWORKS
  • O(mn1/?log2) 2? - approx O(m nlogn)
    (1e)-approx

11
Approximate Closest Pairs
  • Preprocess point set S such that for any query
    sets Red, Blue ? S, the approx closest pair in
    (Red,Blue) can be reported in time
  • O(m log m),
  • where m AB.

12
SP in Polygonal Domain with Polygonal Obstacles
  • Require that domain be t -rounded.
  • Preprocessing O(nlogn)
  • Space O(nlogn)
  • Query on vertices O(1)
  • Query on arbitrary points O(nlogn)

13
Open Problems
  • Output the SP in O(k) time.
  • Reduce the space complexity of O(nlogn).
  • Generalize to arbitrary geometric networks
  • HARD!
  • SP queries in dynamic spanner graphs.
  • Add edge(s) to best improve stretch factor of a
    graph.
  • Remove edge(s) to get minimum increase of stretch
    factor.

14
More Open Problems
  • Find the center of a given geometric graph.
  • Given a graph, how to compute a subgraph with
    minimum stretch factor, such that the subgraph is
    a
  • Spanning tree,
  • Path,
  • Planar graph
  • Replace input graph by a set of points.
  • Other applications?

15
Thanks!
16
What are Cluster Graphs?
  • Cluster graph Hi closely approximates distances
  • in N for vertices (p?q) at distance at least
    ?D?i-1.
  • Hi has degree bounded by a constant. (Size
    O(n))
  • Shortest path queries for vertices (p?q) such
    that
  • pq ? (D?i-1? D?i can be reported in constant
    time.
  • All O(log n) cluster graphs of N can be
    constructed
  • efficiently in O(nlogn) time.
  • (Time and space O(nlogn))

17
Constructing Cluster Graphs
18
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19
Basic Idea
Preprocessing
  • Given a t-spanner network N, construct a
  • (1?)-spanner N of N with O(n) edges
  • Build a sequence of p O(logn) cluster graphs
  • H1 ? H2 ? ? Hi ? ? Hp
  • Each Hi has only edges of length in the range
  • (?D?i-1? tD?i and degree bounded by a
    constant.
  • For query (p,q), find i such that pq ? (D?i-1?
    D?i.
  • Report distance between p and q in Hi.

O(mnlogn)
O(mnlogn)
Search
O(1)
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