1. Massive Data Streams in Graph Theory and Computational Geometry Ph.D. Dissertation Defense Jian Zhang Advisor: Joan Feigenbaum Committee: Ravi Kannan Avi Silberschatz Sampath Kannan (UPenn) Support: NSF grants 0105337 and 0331548

2. June 15, 2005J. Zhang - Ph.D. Dissertation Defense2 Talk Outline Streaming computational model Overview of results Approximate graph distances in the streaming model Future research directions

3. June 15, 2005J. Zhang - Ph.D. Dissertation Defense3 Data Streams A data stream is a sequence of data elements: a 1, a 2, …, a n. Stream of stock prices Stream of IP packets Data elements have different forms in different applications. Scalar value Tuple The semantics of the data elements are also different in different applications.

4. June 15, 2005J. Zhang - Ph.D. Dissertation Defense4 Streaming Computational Model Sequential access to the input stream Order of data elements in the stream is not controlled by the algorithm and may be adversarial. Algorithms may perform pre- or post-processing without access to the data stream. Working Space STREAM

5. June 15, 2005J. Zhang - Ph.D. Dissertation Defense5 Features of Streaming Algorithms Small working space compared to the stream length n Polylog n n  Small number of passes over the stream One pass Constant number of passes Fast per-data-element processing time

6. June 15, 2005J. Zhang - Ph.D. Dissertation Defense6 Sliding-Window Model A variation of streaming Data stream is a time series and may be infinite. Consider the n most recent data elements. As time progresses, new data elements arrive, and old data elements expire. The deletion of old data elements is implicit.

7. June 15, 2005J. Zhang - Ph.D. Dissertation Defense7 Why Streaming ? Data streams occur in real systems. IP-traffic flow Need to distinguish the working space from the data storage. Storage devices: large capacity but slow access Working space: small capacity but fast random access We want to restrict random access to the mass storage but still see every element of the input set at least once.

8. June 15, 2005J. Zhang - Ph.D. Dissertation Defense8 Earlier Work on Streaming Despite the restrictions of the model, a lot can be done, e.g.: L p norms [FKSV02, Indyk00] histograms [GKS01] clustering [GMMO00] Much of the work focuses on computing statistics. Often the working-space size is restricted to polylog space.

9. June 15, 2005J. Zhang - Ph.D. Dissertation Defense9 Talk Outline Streaming computational model Overview of results Approximate graph distances in the streaming model Future research directions

10. June 15, 2005J. Zhang - Ph.D. Dissertation Defense10 Dissertation Contributions Investigate important problem domains. Computational geometry problems Graph problems Show the importance of a more relaxed model. Sublinear space instead of polylog space Multiple passes There are problems that are provably hard in the restricted model but feasible in the more relaxed model.

11. June 15, 2005J. Zhang - Ph.D. Dissertation Defense11 Results on Geometric Problems (1) Exact computation is hard using sublinear space. Computing the exact Diameter, Closest Pair, or Convex Hull requires  (n) bits of space, where n is the number of points in the stream. Approximation is feasible. We give a one-pass, ε-approximation, streaming algorithm for diameter. The algorithm needs storage for O(1/ε) points and processes each point in O(log(1/ε)) time. [ Feigenbaum-S. Kannan-Zhang ]

12. June 15, 2005J. Zhang - Ph.D. Dissertation Defense12 Results on Geometric Problems (2) We give an ε-approximation algorithm that maintains the diameter in the sliding-window model. The algorithm uses O(1/ε log 3 n logR) bits of space, where R is the largest diameter attained in any window. The amortized processing time for each point is O(logn). We show that is  (1/ε logn logR) space is required for such an approximation.

13. June 15, 2005J. Zhang - Ph.D. Dissertation Defense13 Graph Stream Consider undirected graph: G =(V,E) V = {v 1, v 2, …, v n } E = {e 1, e 2, …, e m } A graph stream is a sequence of edges in E. Edges arrive in arbitrary order in the stream. More general than adjacency matrices or adjacency lists (4,5) (2,3) (1,3) (3,5) (1,2) (2,4) (1,5) (3,4) 1 2 3 4 5

14. June 15, 2005J. Zhang - Ph.D. Dissertation Defense14 Results on Graph Problems (1) Many problems require  (n) bits of space. Graph distances (even approximation), Connectivity testing, Planarity testing … Consider streaming algorithms that use O(n·polylogn) space and O(1) passes. In such a model, we can compute or approximate: Spanning trees Graph distances [ Feigenbaum-S. Kannan-McGregor-Suri-Zhang ]

15. June 15, 2005J. Zhang - Ph.D. Dissertation Defense15 Results on Graph Problems (2) (1+ ,  )-approximation: Our algorithm outputs {  (u,v)} s.t.  (u,v)  (1+  ) dist G (u,v) + , where dist G (u,v) is the true distance between vertices u and v. The algorithm uses O(n 1+1/k ) space. Processing time per edge is O(n 1/k ). Needs multiple passes. 1/k and  are arbitrarily small parameters.  and the number of passes are functions of k and 1/ . [ Elkin-Zhang ] We give a randomized streaming algorithm that approximates graph distances:

16. June 15, 2005J. Zhang - Ph.D. Dissertation Defense16 Results on Graph Problems (3) We give a one-pass, streaming algorithm for approximating graph distances. (2t+1)-approximation:  (u,v)  (2t+1)·dist G (u,v) O(t·n 1+1/t ·logn) space Processing time per edge: O(t 2 ·n 1/t ·logn) Needs one pass. Lower bound: The space complexity of one-pass, t-approximation is  (n 1+1/t ). [ Feigenbaum-S. Kannan-McGregor-Suri-Zhang ] For t = log n, this gives a one-pass, O(logn)-approximation algorithm using n·polylog space and polylog time per edge.

17. June 15, 2005J. Zhang - Ph.D. Dissertation Defense17 Publications J. Feigenbaum, S. Kannan, and J. Zhang, “Computing Diameter in the Streaming and Sliding-Window Models,” Algorithmica 41 (2005), pp. 25-41 J. Feigenbaum, S. Kannan, A. McGregor, S. Suri, and J. Zhang, “On Graph Problems in a Semi-Streaming Model,” ICALP 2004, pp. 531-543. Journal version to appear in Theoretical Computer Science. M. Elkin and J. Zhang, “Efficient Algorithms for Constructing (1+ε,β)-Spanners in the Distributed and Streaming Models,” PODC 2004, pp. 160-168 J. Feigenbaum, S. Kannan, A. McGregor, S. Suri, and J. Zhang, “Graph Distances in the Streaming Model: The Value of Space,” SODA 2005, pp 745-754

18. June 15, 2005J. Zhang - Ph.D. Dissertation Defense18 Other Results in Thesis Streaming-space requirement can be reduced by annotating the stream. J. Feigenbaum, S. Kannan, and J. Zhang, “Annotation and Computational Geometry in the Streaming Model,” Yale University Technical Report YALEU/DCS/TR-1249, 2003 Using streaming algorithms to detect BGP-update anomalies. J. Zhang, J. Rexford, and J. Feigenbaum, “Learning-Based Anomaly Detection in BGP Updates,” to appear in SIGCOMM Workshop on Mining Network Data 2005

19. June 15, 2005J. Zhang - Ph.D. Dissertation Defense19 Talk Outline Streaming computational model Overview of results Approximate graph distances in the streaming model Future research directions

20. June 15, 2005J. Zhang - Ph.D. Dissertation Defense20 Shortest-Path Distances Distance is the length of the shortest path. Fundamental problem in graph theory Many algorithms and approximations Most of them use BFS-like subroutines, which are hard to adapt to the streaming model.

21. June 15, 2005J. Zhang - Ph.D. Dissertation Defense21 The “Sketch” Approach A two-stage approach First stage: While going through the stream, construct a small sketch of the input graph. Second stage: Compute the distance using the sketch, without further access to the stream. Perform BFS-like computations in the second stage.

22. June 15, 2005J. Zhang - Ph.D. Dissertation Defense22 Graph Spanners as Sketches Edge subgraph H of a graph G, s.t., for any pair of vertices u and v, their distance in H, dist H (u,v), is not far from their distance in G, dist G (u,v). Multiplicative spanner [t-Spanner]: dist H (u,v)  t·dist G (u,v). Spanners are sparse. A t-Spanner has O(n 1+1/t ) edges. Reduce streaming graph distance to streaming spanner construction. BFS-like subroutines are used in most existing spanner constructions.

23. June 15, 2005J. Zhang - Ph.D. Dissertation Defense23 Streaming Spanner Construction For each incoming edge, decide whether it should be in the spanner. If the edge causes a cycle of length  t, do not put the edge in the spanner. This gives a t-spanner, because there is a path P of length < t connecting the two endpoints of any discarded edge. This spanner is sparse. Thm [Bollobás78] : A graph whose girth is larger than k can only have O(n 1+2/(k-1) ) edges. Need to know: For an incoming edge, does the path P exist?

24. June 15, 2005J. Zhang - Ph.D. Dissertation Defense24 Partial Solution: Clusters (1) A cluster is a subset of vertices and a small diameter spanning tree built on these vertices. Intra-cluster edge

25. June 15, 2005J. Zhang - Ph.D. Dissertation Defense25 Partial Solution: Clusters (2) Inter-cluster edges Bollobás’s result no longer applies. Need to control the number of clusters (i.e., make it ).

26. June 15, 2005J. Zhang - Ph.D. Dissertation Defense26 Summary of the One-Pass Algorithm Use a vertex-labeling scheme to construct the clusters. Structure of the algorithm: In the pre-processing phase, generate a multi-level set of labels. Go through the stream; for each edge: According to the current assignment of labels to vertices, decide whether to put this edge in the spanner. Depending on the type of edge, possibly assign more labels to one of its endpoints. Next, an example with t = log n

27. June 15, 2005J. Zhang - Ph.D. Dissertation Defense27 Labels logn/2 levels w.h.p., there are top-level labels. Semantics of labels: The set of vertices assigned the same top-level label forms a cluster. The set of vertices assigned the same lower-level label forms a “pre-cluster.” (0,1)(0,2)(0,3)(0,4)(0,5)(0,6)(0,7)(0,8)(0,9)(0,10) (0,11) (0,12) (1,2)(1,4)(1,7) (1,11) (2,2)(2,7) Level 0 Level 1 Level 2 (0,1)(0,2)(0,3)(0,4)(0,5)(0,6)(0,7)(0,8)(0,9)(0,10) (0,11) (0,12) (1,2)(1,4)(1,7) (1,11)

28. June 15, 2005J. Zhang - Ph.D. Dissertation Defense28 Initial Label Assignment v 1 v 2 v 3 v 4 v 5 v 6 v 7 v 8 v 9 v 10 v 11 v 12 (0,1)(0,2)(0,3)(0,4)(0,5)(0,6)(0,7)(0,8)(0,9)(0,10)(0,11)(0,12) (1,2)(1,4)(1,7)(1,11) (2,2)(2,7) Level 0 Level 1 Level 2

29. June 15, 2005J. Zhang - Ph.D. Dissertation Defense29 On arrival of an edge Already know what to do with: Intra-cluster/pre-cluster edges Inter-cluster edges Edges connecting pre-clusters: the sticky edges They are added to the spanner. They may lead to new label assignment and cluster growth.

30. June 15, 2005J. Zhang - Ph.D. Dissertation Defense30 “Good” Neighbor (1) (3,2) (2,2) (1,2) (0,2) (1,6) (0,6) (2,2) (3,2) vu Has marked labels

31. June 15, 2005J. Zhang - Ph.D. Dissertation Defense31 Good Neighbor (2) vu C(1,2) C(2,2) C(3,2) C(1,6)

32. June 15, 2005J. Zhang - Ph.D. Dissertation Defense32 “Bad” Neighbor (3,2) (1,6) vu No marked labels

33. June 15, 2005J. Zhang - Ph.D. Dissertation Defense33 Properties of the Clusters Small diameter Number of clusters bounded by. Do not need to cover the whole graph with clusters, but the uncovered subgraph is sparse. The uncovered subgraph consists of sticky edges, and there are not too many of them.

34. June 15, 2005J. Zhang - Ph.D. Dissertation Defense34 Sticky Edges are Rare u1u1 u2u2 u3u3 u4u4 v u 1, u 2, u 3, u 4 … A neighbor is good with probability at least ½. After seeing at most logn/2 good neighbors, v will be assigned a top- level label and be included in a cluster. No more sticky edges for v. The number of sticky edges can be bounded by the length of the shortest prefix in the above sequence that contains logn/2 good neighbors.

35. June 15, 2005J. Zhang - Ph.D. Dissertation Defense35 Talk Outline Streaming computational model Overview of results Approximate graph distances in the streaming model Future research directions

36. June 15, 2005J. Zhang - Ph.D. Dissertation Defense36 Summary We investigated two important problem domains. Exact computation is hard; approximation may be feasible. For some problems, particularly graph problems, considering a more general model is important, because polylog space is too restrictive. Constructing a sketch of non-numerical input is an important tool in streaming-algorithm design.

37. June 15, 2005J. Zhang - Ph.D. Dissertation Defense37 Future Research Directions Geometric problems: High-dimensional geometric problems Sliding-window with flexible size Graph problems: Dynamic graph problems