1. Data Structures & Algorithms Radix Search Richard Newman based on slides by S. Sahni and book by R. Sedgewick

2. Radix-based Keys Key has multiple parts Each part is an element of some set Character Numeral Key parts can be accessed (e.g., string s[i]) Size of set is radix

3. Advantages of Radix-based Search Good worst-case performance Simpler than balanced trees, etc. Fast access to data Easy way to handle variable-length keys Save space (part of key in structure)

4. Disadvantages of Radix-based Search May be space-inefficient Performance depends on access to bytes of keys Must have distinct keys, or other way to handle duplicate keys

5. Digital Search Trees Similar to binary search trees Difference is that we use bits of the key to determine subtree to search Path in tree = prefix of key

6. Digital Search Trees Insert A-S-E-R-C-H-I-N-G Key Repr A00001 S10011 E00101 R10010 C00011 H01000 I01001 N01110 G00111 A S 1 E 0 10 10 R 10 C 1 0 H 10 I 10 N 10 G 10 Note that binary tree is not sorted in BST sense

7. Digital Search Trees Prop 15.1: A search or insertion into a DST takes about lg N comparisons on average, and about 2 lg N comparisons in the worst case, in a tree built from N keys. The number of comparisons is never more than the number of bits in the search key.

8. Tries Use bits of key to guide search like DST But keep keys in order like BST Allow recursive sort, etc. Pronounced “try-ee” or “try” Keys kept at leaves of a binary tree

9. Tries Defn. 15.1: A trie is a binary tree that has keys associated with each leaf, defined as follows: a trie for an empty set is a null link a trie for a single key is a leaf w/key a trie for > 1 key is an internal node with left link referring to trie for keys that start with 0, right for keys 1xxx

10. Tries Insert A-S-E-R-C-H-I-N-G Key Repr A00001 S10011 E00101 R10010 C00011 H01000 I01001 N01110 G00111 A S 1 A 0 Construct tree to point where prefixes match

11. Tries Insert A-S-E-R-C-H-I-N-G Key Repr A00001 S10011 E00101 R10010 C00011 H01000 I01001 N01110 G00111 A 10 AE 10 10 10 10 10 RS S 10 A Construct tree to point where prefixes match

12. Tries Insert A-S-E-R-C-H-I-N-G Key Repr A00001 S10011 E00101 R10010 C00011 H01000 I01001 N01110 G00111 10 A 10 10 10 10 RS A 10 C E 10 10 H

13. Tries Insert A-S-E-R-C-H-I-N- G Key Repr A00001 S10011 E00101 R10010 C00011 H01000 I01001 N01110 G00111 10 10 10 10 10 RS A 10 C E 10 10 H 10 1 0 10 HI

14. Tries Prop. 15.2: The structure of a trie is independent of key insertion order; there is one unique trie for any given set of distinct keys. Prop. 15.3: Insertion or search for a random key in a trie built from N random keys takes about lg N bit comparisons on average, in the worst case, bounded by bits in key

15. Tries Annoying feature of tries: One-way branching when keys have common prefix Prop. 15.4: A trie built from N random w-bit keys has about N/lg 2 nodes on the average (about 1.44 N)

16. Patricia Tries Annoying feature of tries: One-way branching when keys have common prefix Two different types of nodes in trie Patricia tries: fix both of these Practical Algorithm To Retrieve Information Coded In Alphanumeric

17. Patricia Tries Avoid one-way branching: Keep at each node the index of the next bit to test Skip over common prefix! Avoid two types of nodes: Store data in internal nodes Replace external links with back links

18. Patricia Tries S R 4 H 0 1 E 2 3 C 4 A Key Repr A00001 S10011 E00101 R10010 C00011 H01000 I01001 N01110 G00111

19. Patricia Tries S R 4 H 0 1 E 2 3 C 4 A Key Repr A00001 S10011 E00101 R10010 C00011 H01000 I01001 N01110 G00111

20. Patricia Tries Prop 15.5: Insertion or search in a patricia trie built from N random bitstrings takes about lg N bit comparisons on average, and about 2 lg N in the worst case, but never more than the length of the key.

21. Map Radix search Digital Search Trees Tries Patricia Tries Multiway tries and TSTs Text string algorithms

22. Multiway Tries Like radix sort, can get benefit from comparing more than one bit at a time Compare r bits, speed up search by a factor of r What could possibly be bad? Number of links is now R=2 r Can waste a lot of space!

23. Multiway Tries Structure is (almost) the same as binary tries Except there are R branches Search: start at root, leftmost digit Follow i th link if next R-ary digit is i If null link, then miss If reach leaf, it contains only key with prefix matching path to it - compare

24. Existence Tries Only keys, no records Insert/search Defn. 15.2: The existence trie for a set of keys is: Empty set: null link Non-empty set: internal node with links for each possible digit to tries built with the leading digit omitted

25. Existence Tries Convenient to return null on miss, dummy record on hit Convenient to have no duplicate keys and no key a prefix of another key Keys of fixed length, or Use termination character with value NULLdigit, only used as sentinel

26. Existence Tries No need to store any data All keys captured in trie structure If reach NULLdigit at the same time we run out of key digits, search hit Otherwise, search miss Insert: search until find null link, then add nodes for each of the remaining digits in the key

27. Existence Tries now is the time for a t n h e i i m e s o w f o r

28. Multi-way Tries R-ary branching Keys stored at leaves Path to leaf defines prefix of key stored at leaf Only build tree downward until prefixes become distinct

29. Multi-way Tries Defn. 15.3: The multiway trie for a set of keys associated with leaves is: Set empty: null link Singleton set: leaf with key Larger set: internal node with links for each possible digit to tries built with the leading digit omitted

30. Multi-way Tries Prop. 15.6: Search or insertion in a standard R-ary trie takes built from N random keys takes about log R N character comparisons, bounded by the length of the key; the number of links is about RN/ln R. Classic time-space tradeoff! Larger R = faster but more space

31. Ternary Search Trie (TST) Each node has a character (digit) and three links Left link refers to subtrie with current key digit less than that of the node Middle link refers to subtrie with current key digit the same Right link refers to subtrie with current key digit greater than node’s

32. Ternary Search Trie (TST) TST equivalent to BST that used characters for non-null links as keys Like 3-way radix sorting BSTs like QuickSort M-ary tries like RadixSort

33. Ternary Search Trie (TST) Search: start at root Recursively – Compare next character in key with character in node If less, take left link If greater, take right link If equal, take middle and go to next character in key Miss if encounter null link or reach end of key before NULLdigit

34. Ternary Search Trie (TST) Insert: start at root Search – Find location where prefix diverges Add new nodes for characters not consumed by search

35. Existence TST now is the time for n h e i i m e s o w f o r t

36. Ternary Search Trie (TST) Prop. 15.7: A search or insertion in a full TST requires time proportional to the key length. The number of links in a TST is at most three times the number of characters in all the keys.

37. Ternary Search Trie (TST) Can make more space efficient by putting keys in leaves at point where prefix is unique, and eliminating one-way branching as we did in Patricia Tries. Can compromise speed and space by having large branch at root (R or R 2 ) and rest of trie is regular TST. Works well if first char(s) well-distributed

38. Ternary Search Trie (TST) Nice for practical use Adapt to non-uniformity often seen Though character set may be large, often only a few are used, or are used after a particular prefix Don’t make many links we don’t need Structured format keys May have many symbols used But only a few at each part of key

39. Ternary Search Trie (TST) Nice for practical use Search misses are really fast! Can adapt for partial match searches “Don’t care” characters in search key Can adapt for “almost match” searches All but (any) one character match Access bytes or larger symbols rather than bits (like Patricia tries), which are often better supported/efficient, or more natural to the keys

40. Text-String-Index Recall String Index built with BST with string pointers into a large text Consider each position in text to be start of a string key that runs to the end of the text Build a symbol table with these keys Keys are all different (lengths alone suffice) Most are very long Suffix Tree = search tree for this

41. Text-String-Index BSTs are simple and work well for suffix trees Not likely to be a worst-case BST Patricia tries designed to do this! Need to have bit-level access Fast on misses TSTs Simple, take advantage of byte ops Can solve more complex problems Can change == to mean “prefix”

42. Text-String-Index If text is static, why not use Binary Search? Fast No need to support insert/delete Uses less memory (fewer links/pointers) But TSTs have some advantages Never retrace steps Support other operations Can also build FSM.. But better for linear search of new text

43. String Search If problem is to look for a particular string s in a large text t Naïve method: Search t linearly for s[0] When match found at t[i], Match s[j] with t[i+j] for j = 1 to |s|-1 If all |s| chars match, have a match! Else go back to searching t at t[i+1] Time? |s| times |t| - not good

44. FSM-based String Search Fast way to look for a particular string s in one or more (large) texts: Build FSM for search string States represent prefix matched Transition either extends match or Fails to longest suffix of what has been seen that is a prefix of s Can also build for multiple search strings

45. Finite State Machine a.k.a. Finite State Automaton (FSA) c a dany Set of States S – represented as nodes in graph Set of input symbols  – labels on directed edges Transition function  – for state and input, next state Initial state q 0 – where to start Final set of states F – subset of S for “accept” Start state F={q 1,q 2 } b  = {a,b,c,d} a,b,d c c q0q0 q3q3 q2q2 q1q1 q2q2 q1q1 Edge=transition  (q 1,c)=q 3

46. FSM-based String Search Search for abraca a a ab b abr r abra a abrac c abraca a Not aa else a b a b Build recognizer skeleton Add suffix-is-prefix links Add failure links a Start state Final state Is that all of them?

47. FSM-based String Search Linear time in |s| to build FSM for s Linear time (in |t|) to search large text t for all instances of s Can’t hope for better than that! What about searching for more than one string? Build FSM for all the strings! Linear time in sum of string lengths to build FSM Linear time in |t| to search all of t for all strings

48. Summary Radix search Digital Search Trees Tries Patricia Tries Multiway tries and TSTs Text string algorithms FSMs for fast string matching