Download Data Structures on Event Graphs Bernard Chazelle Wolfgang

Wolfgang Mulzer
Institut für Informatik
Data Structures on Event
Graphs
Bernard Chazelle
Princeton University
Wolfgang Mulzer
FU Berlin
It‘s the data
Data can be
huge
corrupted
low-entropy
expensive
…
Rethink classical algorithms from
a data-oriented perspective.
Bernard Chazelle and Wolfgang Mulzer – Data Structures on Event Graphs
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It‘s the data
Data can be
huge
corrupted
low-entropy
expensive
…
We study a model that represents
temporal locality of the data.
Bernard Chazelle and Wolfgang Mulzer – Data Structures on Event Graphs
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A concrete problem – successor search
Given: An ordered universe U of n elements
x1
x2
x3
x4
x5
x6
x7
x8
x9
x10
Goal: maintain a subset S of U supporting successor queries
Operations: Insert(xi)
Delete(xi)
Successor(xi)
Also known as Union-Split-Find Problem.
Bernard Chazelle and Wolfgang Mulzer – Data Structures on Event Graphs
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A concrete problem – successor search
Given: An ordered universe U of n elements
x1
x2
x3
x4
x5
x6
x7
x8
x9
x10
Can be solved in O(log log n) time on a pointer machine.
[van Emde Boas, Kaas, Zijlstra 77]
This is optimal.
[Mehlhorn, Näher, Alt 88], [Pătraşcu, Thorup 06]
Bernard Chazelle and Wolfgang Mulzer – Data Structures on Event Graphs
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Event graphs
Given: An ordered universe U of n elements
and
a labeled, connected, undirected graph G
Ix0
Sx2
 G is labeled with operations Ixi, Dxi, Sxi
Dx2
Ix5
 G can be preprocessed
Sx7
Ix7
Dx9
Ix9
 G is known in advance
 Adversary walks on G to perform ops
Similar to Markov chains
Bernard Chazelle and Wolfgang Mulzer – Data Structures on Event Graphs
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Event graphs
x1
Ix0
x2
x3
x4
Sx2
x5
x6
x7
x8
x9
x10
 G is labeled with operations Ixi, Dxi, Sxi
Dx2
Ix5
 G can be preprocessed
Sx7
Ix7
 G is known in advance
Dx9
 Adversary walks on G to perform ops
Ix9
Bernard Chazelle and Wolfgang Mulzer – Data Structures on Event Graphs
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Decorated graphs
The walk of the adversary induces a walk on a much
bigger graph.
Decorated Graph dec(G): directed graph with vertex
set V(G)  Pow(U).
Represents current node of G + current set S.
Ix0
Sx2
(Sx2, )
Dx2
(Dx2, )
Ix5
Sx7
(Ix5, {x5, x9})
Ix7
Dx9
(Ix9, {x9})
Ix9
Bernard Chazelle and Wolfgang Mulzer – Data Structures on Event Graphs
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Decorated graphs
The walk of the adversary induces a walk on a much
bigger graph.
Decorated Graph dec(G): directed graph with vertex
set V(G)  Pow(U).
Represents current node of G + current set S.
If dec(G) is available, we can perform all operations in
constant time.
But: The size of dec(G) is exponential.
Bernard Chazelle and Wolfgang Mulzer – Data Structures on Event Graphs
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Decorated graphs
The walk of the adversary induces a walk on a much
bigger graph.
Decorated Graph dec(G): directed graph with vertex
set V(G)  Pow(U).
Represents current node of G + current set S.
Questions:
- What can we say about the structure of dec(G)?
-What can we deduce about dec(G), given G?
-In which cases can dec(G) be compressed efficiently?
Bernard Chazelle and Wolfgang Mulzer – Data Structures on Event Graphs
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The structure of decorated graphs
dec(G) contains a unique strongly connected component
that has no exit and is reachable from every other node.
C2
C1
C3
C4
This component is called the unique sink.
Bernard Chazelle and Wolfgang Mulzer – Data Structures on Event Graphs
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The structure of decorated graphs
Theorem: Given a node vV(G) and a set SU, we can
decide in time O(|V(G)|+|E(G)|) whether (v,S) lies in the
unique sink.
Proof idea: We show that for every node in the unique
sink there exists a unique certificate in G (a certifying
walk).
A modified graph search in G can be used to find a
certifying walk for (v,S), if it exists.
Bernard Chazelle and Wolfgang Mulzer – Data Structures on Event Graphs
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Can the decorated graph be compressed?
Consider the case that G is a path.
Ix7
Sx7
Ix0
Sx2
Dx2
Ix9
Dx9
Ix5
Theorem: If G is a path, the successor problem can be
solved in O(1) time per operation with O(n1+) space on a
word RAM, where n=|V|.
Bernard Chazelle and Wolfgang Mulzer – Data Structures on Event Graphs
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Can the decorated graph be compressed?
Ix7
Sx7
Ix0
Sx2
Dx2
Ix9
Dx9
Ix5
Theorem: If G is a path, the successor problem can be
solved in O(1) time per operation with O(n1+) space on a
word RAM, where n=|V|.
Bernard Chazelle and Wolfgang Mulzer – Data Structures on Event Graphs
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Can the decorated graph be compressed?
Ix7
Sx7
Ix0
Sx2
Dx2
Ix9
Dx9
Ix5
Theorem: If G is a path, the successor problem can be
solved in O(1) time per operation with O(n1+) space on a
word RAM, where n=|V|.
Proof: Maintain S in a doubly linked list.
Each node in G has a pointer to its predecessor or
successor in S.
Use this pointer to answer the queries.
Need only maintain those pointers that will be
relevant next.
Use lookup-table.
Bernard Chazelle and Wolfgang Mulzer – Data Structures on Event Graphs
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Example
x1
…
Dx1
x3
Sx5
x5
Dx3
Ix7
Bernard Chazelle and Wolfgang Mulzer – Data Structures on Event Graphs
x7
Dx2
Sx8
x10
Ix2
Dx9 …
16
Reducing the space requirement
A naïve implementation uses two lookup-tables per node to
update the pointers → O(n2) space usage.
Can be improved to O(n1+) space.
Approach: Use spatial decomposition and bootstrapping to
compress the lookup-tables (cf. [Crochemore et al, 2008])
Bernard Chazelle and Wolfgang Mulzer – Data Structures on Event Graphs
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What about randomization?
We assumed an adversary.
But: What if the walk on the path is random?
Theorem: If the requests are generated by a random walk
on a path, the successor problem can be solved in O(1)
expected time per operation with O(n) space on a word
RAM, where n=|V|.
Bernard Chazelle and Wolfgang Mulzer – Data Structures on Event Graphs
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What about randomization?
Theorem: If the requests are generated by a random walk
on a path, the successor problem can be solved in O(1)
expected time per operation with O(n) space on a word
RAM, where n=|V|.
Proof (sketch): Subdivide the path into segments of n
nodes.
The random walk requires (n) steps to leave a segment.
Build the quadratic data structure once the walk enters the
next segment.
Use overlapping segments and deamortization techniques
to make it work.
Bernard Chazelle and Wolfgang Mulzer – Data Structures on Event Graphs
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What about more complicated graphs?
What if G is a tree, a grid, or something more complicated?
Ix7
Sx7
Sx2
Dx2
Ix0
Ix9
Dx9
Ix7
Sx7
Ix0
Sx2
Dx2
Ix9
Dx9
Ix5
Ix7
The path approach does not work any more
We conjecture that in this case the O(log log n) bound from
van Emde Boas trees is optimal (but we do not know).
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Conclusion and open problems
A new way to model request sequences to a data structure.
Can be applied to any data structuring problem.
More algorithmic questions on decorated graphs, e.g., can
we estimate the size of the unique sink efficiently?
Can we prove lower bounds for the successor problem on
general event graphs?
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Thank you!
Bernard Chazelle and Wolfgang Mulzer – Data Structures on Event Graphs
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