Download presentation

Presentation is loading. Please wait.

Published byEthan Price Modified over 5 years ago

1
drawing trees in a streaming model Carla Binucci Ulrik Brandes Giuseppe Di Battista Walter Didimo Marco Gaertler Pietro Palladino Maurizio Patrignani Antonios Symvonis Katharina Zweig 3 Thanks to the Bertinoro Workshop on Graph Drawing, March 2008

2
drawing trees in a streaming model Carla Binucci Ulrik Brandes Giuseppe Di Battista Walter Didimo Marco Gaertler Pietro Palladino Maurizio Patrignani Antonios Symvonis Katharina Zweig 5 Thanks to the Bertinoro Workshop on Graph Drawing, March 2008

3
drawing trees in a streaming model Carla Binucci Ulrik Brandes Giuseppe Di Battista Walter Didimo Marco Gaertler Pietro Palladino Maurizio Patrignani Antonios Symvonis Katharina Zweig 2 Thanks to the Bertinoro Workshop on Graph Drawing, March 2008

4
drawing trees in a streaming model Carla Binucci Ulrik Brandes Giuseppe Di Battista Walter Didimo Marco Gaertler Pietro Palladino Maurizio Patrignani Antonios Symvonis Katharina Zweig 3 Thanks to the Bertinoro Workshop on Graph Drawing, March 2008

5
streams of data several applications produce (potentially infinite) streams of data that are too many to be stored and that should be analyzed in real time –networking applications IP packets, TCP connections, interface usage, … –enviromental monitoring atmospheric sensor measures, satellite scans, … –social networks applications emails, faxes, telephone calls, … in several cases such streams represent graphs

6
working with streams streaming model of computation [Muthukrishnan] –input: a stream of data –output: measure/compute some property –purpose: use limited resources streaming on graphs –triangle counting [Bar-Yossef et al][Buriol et al][Jowhari et al] –computing clustering coefficient [Buriol et al] –counting k 3,3 [Buriol et al] –testing matching, bipartiteness, connectivity, MST, t- spanners [Feigenbaum]

7
e1e1 e2e2 e3e3 e 11 e4e4 e5e5 e6e6 e7e7 e8e8 e9e9 e 10 e 12 e 13 e 14 e1e1 e2e2 e3e3 e4e4 e5e5 e6e6 e7e7 e8e8 e9e9 e 10 e 11 e 12 e 13 e 14

8
e1e1 e2e2 e3e3 e 11 e4e4 e5e5 e6e6 e7e7 e8e8 e9e9 e 10 e 12 e 13 e 14 e1e1 e2e2 e3e3 e4e4 e5e5 e6e6 e7e7 e8e8 e9e9 e 10 e 11 e 12 e 13 e 14 persistence = 6

9
drawing a stream of edges the input is a stream of edges: S = (e 1, e 2, e 3, e 4, e 5, …) the drawing has a persistence k –k may be infinite at any time i we have to produce a drawing i of G i = {e i-(k-1), e i-k,…,e i-1, e i } –remove e i-k from i-1 –add e i to i-1

10
quality assessment: competitive ratio consider an algorithm A for drawing a stream of edges S = (e 1, e 2, e 3, …) denote by A(S) a quality measure of algorithm A on stream S Opt(S) the quality measure of the optimal off-line algorithm on S competitive ratio of algorithm A the quality measure we consider is the area R A = max S A(S) Opt(S)

11
previous literature incremental graph drawing [de Fraysseix, Pach, Pollack][Biedl,Kant] … –precomputed vertex ordering dynamic graph drawing [Branke][Huang, Eades, Wang][Papakostas, Tollis]… –sequence of graphs where two consecutive drawings should be similar –arbitrary insertions/deletions allowed the no change scenario of [Papakostas, Tollis] corresponds to streamed graph drawing with infinite persistence

12
a specific streaming problem we restrict to straight-line planar grid drawings we assume that the current graph G i is always connected we focus on trees the edges of the stream correspond to an Eulerian tour of the tree –this guarantees that G i is connected

13
problem statement is it possible to draw the stream of edges produced by an Eulerian tour of a tree –with limited area –with persistence k –such that edges are straight-line segments and each drawing is planar ?

14
persistence = 6 intuition of the approach

15
algorithm Greedy Clockwise (GC) assume to have m points p 0, p 1, …, p m in convex position greedily use them in clockwise order –at time 1 draw the first edge on p 0 and p 1 and set next 2 = 2 –at time i if you need to insert a vertex, place it on and set next i+1 =(next i +1) mod m otherwise, set next i+1 =next i

16
persistence = 6

17
conditions for algorithm GC to work algorithm Greedy-Clockwise guarantees a non-intersecting drawing if Condition 1: point is not used in i by any vertex Condition 2: current edge e i does not introduce a crossing

18
Condition 1 is enough lemma let i-1 be the drawing of G i-1 built by algorithm GC and consider a vertex v that should be added to G i-1 at time i if Condition 1 is satisfied, then no crossing is introduced by drawing v on

19
proof that Condition 1 suffices i(x) < i(u) < i(y) < i(v) eiei u v x y

20
legs, feet, heels, and toes leg of u foot heel toe eiei ejej u v w

21
regular foot (r-foot) eiei ejej u j-i=5 persistence k = 5 j-i k

22
extra-large foot (xl-foot) eiei u v j-i=9 persistence k = 5 j-i > k

23
r-feet and xl-feet regular foot (or r-foot) –when j-i k –u is present in i-1, i, i+1,…, j+k –has maximum size k/2 extra-large foot (or xl-foot) –when j-i > k –u is not present from i+k on

24
when algorithm GC does not work? u

25
when algorithm GC works? theorem 1 algorithm GC draws without crossings the stream of edges produced by an Eulerian tour of a tree of maximum degree at most d on k/2 (d-1)+k+1 points in convex position also R GC =O(d 3 k 2 )

26
technical lemma consider Algorithm GC on m points in convex position suppose that for each vertex v it holds that during the time elapsing from when v is discovered and when it disappears from the drawing at most m-1 other vertices are discovered then Condition 1 holds at each time

27
proof of theorem 1 correctness we show that the time elapsing from when a vertex v is discovered and when it disappears from the drawing is at most k/2 (d-1)+k –hint: v may have at most d-1 r-feet of size k/2 quality m points in convex position require an O(m 3 ) area the area used for our k/2 (d-1)+k+1 points is (d 3 k 3 ) any off-line algorithm requires (k) area to place O(k) points therefore, the competitive ratio is O(d 3 k 2 )

28
algorithm SnowPlow (SP) alternates GC with its mirrored version, called Greedy Counter-Clockwise (GCC) call old( i ) the oldest vertex of i, i.e., the vertex that appears in i, i-1,…, i-j with highest j the decision of switching from GC to GCC (or vice versa) is taken each time a new foot of old( i ) is entered you switch to GCC only if you have used GC enough to ensure that GCC finds a free vertex on the left of old( i )

29
algorithm SnowPlow old( )

30
when algorithm SP works switching condition –if you have used at least k/2 points on one side (GC) you switch to the other side (GCC) theorem 2: algorithm SP draws without crossings and with persistence k a stream of edges produced by an Eulerian tour of a tree on 2k-1 points in convex position R SP =O(k 2 )

31
algorithm SnowPlow and xl-feet old( )

32
summary of the results competitive ratios of the proposed algorithms

33
open problems our competitive ratios are high: do better solutions exist? computing tighter lower bounds for streaming algorithms evaluation larger classes of planar (or general) graphs persistence: –what if persistence is different for different edges –what if k=O(log n), where n is the size of the stream? what if the degree of each vertex is known in advance?

34
thank you for your attention

Similar presentations

OK

Job Scheduling Lecture 19: March 19. Job Scheduling: Unrelated Multiple Machines There are n jobs, each job has: a processing time p(i,j) (the time to.

Job Scheduling Lecture 19: March 19. Job Scheduling: Unrelated Multiple Machines There are n jobs, each job has: a processing time p(i,j) (the time to.

© 2019 SlidePlayer.com Inc.

All rights reserved.

To make this website work, we log user data and share it with processors. To use this website, you must agree to our Privacy Policy, including cookie policy.

Ads by Google