2. Suffix Trees Construction and Applications João Carreira 2008

3. Outline Why Suffix Trees? Definition Ukkonen's Algorithm (construction)‏ Applications

4. Why Suffix Trees?

5. Asymptotically fast.

6. Why Suffix Trees? Asymptotically fast. The basis of state of the art data structures.

7. Why Suffix Trees? Asymptotically fast. The basis of state of the art data structures. You don't need a Phd to use them.

8. Why Suffix Trees? Asymptotically fast. The basis of state of the art data structures. You don't need a Phd to use them. Challenging.

9. Why Suffix Trees? Asymptotically fast. The basis of state of the art data structures. You don't need a Phd to use them. Challenging. Expose interesting algorithmic ideas.

10. Definition m leaves numbered 1 to m Suffix Tree for an m-character string:

11. Definition m leaves numbered 1 to m edge-label vs node-label Suffix Tree for an m-character string:

12. Definition m leaves numbered 1 to m edge-label vs node-label each internal node has at least two children Suffix Tree for an m-character string:

13. Definition m leaves numbered 1 to m edge-label vs node-label each internal node has at least two children the label of the leaf j is S[ j..m ] Suffix Tree for an m-character string:

14. Definition m leaves numbered 1 to m edge-label vs node-label each internal node has at least two children the label of the leaf j is S[ j..m ] no two edges out of the same node can have edge-labels beginning with the same character Suffix Tree for an m-character string:

15. Definition Example String: xabxac Length (m): 6 characters Number of Leaves: 6 Node 5 label: ac

16. Implicit vs Explicit What if we have “axabx” ?

17. Ukkonen's Algorithm suffix tree construction

18. Ukkonen's Algorithm Text: S[ 1..m ] m phases phase j is divided into j extensions: In extension j of phase i + 1: find the end of the path from the root labeled with substring S[ j..i ] extend the substring by adding the character S(i + 1) to its end suffix tree construction

19. Extension Rules Rule 1: Path β ends at a leaf. S(i + 1) is added to the end of the label on that leaf edge.

20. Extension Rules Rule 2: No path from the end of β starts with S(i + 1), but at least one labeled path continues from the end of β.

21. Extension Rules Rule 3: Some path from the end of β starts with S(i + 1), so we do nothing.

22. Ukkonen's Algorithm Complexity: suffix tree construction

23. Ukkonen's Algorithm Complexity: m phases suffix tree construction

24. Ukkonen's Algorithm Complexity: m phases phase j -> j extensions suffix tree construction

25. Ukkonen's Algorithm Complexity: m phases phase j -> j extensions find the end of the path of substring β: O(|β|) = O(m)‏ suffix tree construction

26. Ukkonen's Algorithm Complexity: m phases phase j -> j extensions find the end of the path of substring β: O(|β|) = O(m)‏ each extension: O(1)‏ suffix tree construction

27. Ukkonen's Algorithm Complexity: m phases phase j -> j extensions find the end of the path of substring β: O(|β|) = O(m)‏ each extension: O(1)‏ O(m 3 )‏ suffix tree construction

28. “First make it run, then make it run fast.” Brian Kernighan

29. Suffix Links Definition: For an internal node v with path-label xα, if there is another node s(v), with path-label α, then a pointer from v to s(v) is called a suffix link.

30. Suffix Links Lemma: If a new internal node v with path label xα is added to the current tree in extension j of some phase, then either the path labeled α already ends at an internal node or an internal at the end of the string α will be created in the next extension of the same phase. If Rule 2 applies:

31. Suffix Links Lemma: If a new internal node v with path label xα is added to the current tree in extension j of some phase, then either the path labeled α already ends at an internal node or an internal at the end of the string α will be created in the next extension of the same phase. If Rule 2 applies: S[ j..i ] continues with c ≠ S(i + 1)‏

32. Suffix Links Lemma: If a new internal node v with path label xα is added to the current tree in extension j of some phase, then either the path labeled α already ends at an internal node or an internal at the end of the string α will be created in the next extension of the same phase. If Rule 2 applies: S[ j..i ] continues with c ≠ S(i + 1)‏ S[ j + 1..i ] continues with c.

33. Single Extension Algorithm Extension j of phase i + 1: 1. Find the first node v at or above the end of S[ j - 1..i ] that either has a suffix link from it or is the root. Let λ denote the string between v and the end of S[ j – 1..i ].

34. Single Extension Algorithm Extension j of phase i + 1: 1. Find the first node v at or above the end of S[ j - 1..i ] that either has a suffix link from it or is the root. Let λ denote the string between v and the end of S[ j – 1..i ]. 2. If v is the root, follow the path for S[ j..i ] (as in the naive algorithm). Else traverse the suffix link and walk down from s(v) following the path for string λ.

35. Single Extension Algorithm Extension j of phase i + 1: 1. Find the first node v at or above the end of S[ j - 1..i ] that either has a suffix link from it or is the root. Let λ denote the string between v and the end of S[ j – 1..i ]. 2. If v is the root, follow the path for S[ j..i ] (as in the naive algorithm). Else traverse the suffix link and walk down from s(v) following the path for string λ. 3. Using the extension rules, ensure that the string S[ j..i ] S(i+1) is in the tree.

36. Single Extension Algorithm Extension j of phase i + 1: 1. Find the first node v at or above the end of S[ j - 1..i ] that either has a suffix link from it or is the root. Let λ denote the string between v and the end of S[ j – 1..i ]. 2. If v is the root, follow the path for S[ j..i ] (as in the naive algorithm). Else traverse the suffix link and walk down from s(v) following the path for string λ. 3. Using the extension rules, ensure that the string S[ j..i ] S(i+1) is in the tree. 4. If a new internal w was created in extension j – 1 (by rule 2), then string α must end at node s(w), the end node for the suffix link from w. Create the suffix link (w, s(w)) from w to s(w).

37. Node Depth The node-depth of v is at most one greater than the node depth of s(v). α ß xß xα xλ λ xß xα xλ ß α λ equal node-depth: 3 Node depth: 4Node depth: 3

38. γ number of characters in an edge “Directly implemented” edge traversal: O(|γ|)‏ Skip/count Trick

39. “Jump” from node to node. K = number of nodes in a path Time to traverse a path: O(|K|)‏ γ number of characters in an edge “Directly implemented” edge traversal: O(|γ|)‏

40. Ukkonen's Algorithm Using the skip/count trick: any phase of Ukkonen's algorithm takes O(m) time. Proof:

41. Ukkonen's Algorithm Using the skip/count trick: any phase of Ukkonen's algorithm takes O(m) time. Proof: There are i + 1 ≤ m extensions in phase i + 1

42. Ukkonen's Algorithm Using the skip/count trick: any phase of Ukkonen's algorithm takes O(m) time. Proof: There are i + 1 ≤ m extensions in phase i + 1 In a single extension, the algorithm walks up at most one edge, traverses one suffix link, walks down some number of nodes, applies the extension rules and may add a suffix link.

43. Ukkonen's Algorithm Using the skip/count trick: any phase of Ukkonen's algorithm takes O(m) time. Proof: There are i + 1 ≤ m extensions in phase i + 1 In a single extension, the algorithm walks up at most one edge, traverses one suffix link, walks down some number of nodes, applies the extension rules and may add a suffix link. The up-walk decreases the current node-depth by at most one.

44. Ukkonen's Algorithm Using the skip/count trick: any phase of Ukkonen's algorithm takes O(m) time. Proof: There are i + 1 ≤ m extensions in phase i + 1 In a single extension, the algorithm walks up at most one edge, traverses one suffix link, walks down some number of nodes, applies the extension rules and may add a suffix link. The up-walk decreases the current node-depth by at most one. Each suffix link traversal decreases the node-depth by at most another one.

45. Ukkonen's Algorithm Using the skip/count trick: any phase of Ukkonen's algorithm takes O(m) time. Proof: There are i + 1 ≤ m extensions in phase i + 1 In a single extension, the algorithm walks up at most one edge, traverses one suffix link, walks down some number of nodes, applies the extension rules and may add a suffix link. The up-walk decreases the current node-depth by at most one. Each suffix link traversal decreases the node-depth by at most another one. Each down-walk moves to a node of greater depth.

46. Ukkonen's Algorithm Using the skip/count trick: any phase of Ukkonen's algorithm takes O(m) time. Proof: There are i + 1 ≤ m extensions in phase i + 1 In a single extension, the algorithm walks up at most one edge, traverses one suffix link, walks down some number of nodes, applies the extension rules and may add a suffix link. The up-walk decreases the current node-depth by at most one. Each suffix link traversal decreases the node-depth by at most another one. Each down-walk moves to a node of greater depth. Over the entire phase the node-depth is decremented at most 2m times.

47. Ukkonen's Algorithm Using the skip/count trick: any phase of Ukkonen's algorithm takes O(m) time. Proof: There are i + 1 ≤ m extensions in phase i + 1 In a single extension, the algorithm walks up at most one edge, traverses one suffix link, walks down some number of nodes, applies the extension rules and may add a suffix link. The up-walk decreases the current node-depth by at most one. Each suffix link traversal decreases the node-depth by at most another one. Each down-walk moves to a node of greater depth. Over the entire phase the node-depth is decremented at most 2m times. No node can have depth greater than m, so the total increment to current node-depth (down walks) is bounded by 3m over the entire phase.

48. Ukkonen's Algorithm m phases 1 phase: O(m)‏

49. Ukkonen's Algorithm m phases 1 phase: O(m)‏ O(m 2 )‏

50. “First make it run fast, then make it run faster.” João Carreira

51. Edge-Label Compression A string with m characters has m suffixes. If edge labels are represented with characters, O(m 2 ) space is needed.

52. Edge-Label Compression A string with m characters has m suffixes. If edge labels are represented with characters, O(m 2 ) space is needed. To achieve O(m) space, each edge-label: (p, q)‏

53. Two more tricks...

54. Rule 3 is a show stopper If rule 3 applies in extension j, it will also apply in all further extensions until the end of the phase. Why?

55. Rule 3 is a show stopper If rule 3 applies in extension j, it will also apply in all further extensions until the end of the phase. Why? When rule 3 applies, the path labeled S[ j..i ] must continue with character S(i + 1), and so the path labeled S[ j + 1..i ] does also, and rule 3 again applies in extensions j+1...i+1.

56. Rule 3 is a show stopper End any phase i +1 the first time rule 3 applies. The remaining extensions are said to be done implicitly.

57. Once a leaf always a leaf Leaf created => always a leaf in all successive trees. No mechanism for extending a leaf edge beyond its current leaf. Once there is a leaf labeled j, extension rule 1 will always apply to extension j in any sucessive phase.

58. Once a leaf always a leaf Leaf created => always a leaf in all successive trees. No mechanism for extending a leaf edge beyond its current leaf. Once there is a leaf labeled j, extension rule 1 will always apply to extension j in any sucessive phase. Leaf Edge Label: (p, e)‏

59. Single Phase Algorithm In each phase i:

60. Single Phase Algorithm During construction:

61. Implicit to Explicit One last phase to add character $: O(m)‏

62. Suffix Trees are a Swiss Knife

63. Applications Exact String Matching:

64. Applications Exact String Matching: Three ocurrences of string aw. Preprocessing: O(m)‏ Search: O(n + k)‏

65. Applications And much more.. Longest common substring O(n)‏ Longest repeated substring O(n)‏ Longest palindrome O(n)‏ Most frequently occurring substrings of a minimum length O(n)‏ Shortest substrings occurring only once O(n)‏ Lempel-Ziv decomposition O(n)‏.....

66. “Biology easily has 500 years of exciting problems to work on.” Donald Knuth

67. web.ist.utl.pt/joao.carreira Questions?