Tables and Dictionaries Start of lecture 38.

Tables: rows & columns of information A table has several fields (types of information) A telephone book may have fields name, address, phone number A user account table may have fields user id, password, home folder Name Address Phone Sohail Aslam 50 Zahoor Elahi Rd, Gulberg-4, Lahore 576-3205 Imran Ahmad 30-T Phase-IV, LCCHS, Lahore 572-4409 Salman Akhtar 131-D Model Town, Lahore 784-3753

Tables: rows & columns of information To find an entry in the table, you only need know the contents of one of the fields (not all of them). This field is the key In a telephone book, the key is usually “name” In a user account table, the key is usually “user id”

Tables: rows & columns of information Ideally, a key uniquely identifies an entry If the key is “name” and no two entries in the telephone book have the same name, the key uniquely identifies the entries Name Address Phone Sohail Aslam 50 Zahoor Elahi Rd, Gulberg-4, Lahore 576-3205 Imran Ahmad 30-T Phase-IV, LCCHS, Lahore 572-4409 Salman Akhtar 131-D Model Town, Lahore 784-3753

The Table ADT: operations insert: given a key and an entry, inserts the entry into the table find: given a key, finds the entry associated with the key remove: given a key, finds the entry associated with the key, and removes it

How should we implement a table? Our choice of representation for the Table ADT depends on the answers to the following How often are entries inserted and removed? How many of the possible key values are likely to be used? What is the likely pattern of searching for keys? E.g. Will most of the accesses be to just one or two key values? Is the table small enough to fit into memory? How long will the table exist?

TableNode: a key and its entry For searching purposes, it is best to store the key and the entry separately (even though the key’s value may be inside the entry) key entry “Saleem” “Saleem”, “124 Hawkers Lane”, “9675846” TableNode “Yunus” “Yunus”, “1 Apple Crescent”, “0044 1970 622455”

Implementation 1: unsorted sequential array An array in which TableNodes are stored consecutively in any order insert: add to back of array; (1) find: search through the keys one at a time, potentially all of the keys; (n) remove: find + replace removed node with last node; (n) key entry 1 2 3 … and so on

Implementation 2:sorted sequential array An array in which TableNodes are stored consecutively, sorted by key insert: add in sorted order; (n) find: binary search; (log n) remove: find, remove node and shuffle down; (n) key entry 1 2 3 … and so on We can use binary search because the array elements are sorted

Searching an Array: Binary Search Binary search is like looking up a phone number or a word in the dictionary Start in middle of book If name you're looking for comes before names on page, look in first half Otherwise, look in second half End of Lecture 38

Binary Search If ( value == middle element ) value is found else if ( value < middle element ) search left-half of list with the same method else search right-half of list with the same method Start lecture 39

Binary Search 10 1 5 7 9 10 13 17 19 27 Case 1: val == a[mid] val = 10 low = 0, high = 8 mid mid = (0 + 8) / 2 = 4 10 a: 1 5 7 9 10 13 17 19 27 1 2 3 4 5 6 7 8 low high

Binary Search -- Example 2 Case 2: val > a[mid] val = 19 low = 0, high = 8 mid = (0 + 8) / 2 = 4 new low new low = mid+1 = 5 13 17 19 27 a: 1 5 7 9 10 13 17 19 27 1 2 3 4 5 6 7 8 low high mid

Binary Search -- Example 3 Case 3: val < a[mid] val = 7 low = 0, high = 8 mid = (0 + 8) / 2 = 4 new high new high = mid-1 = 3 5 7 9 1 a: 5 7 9 1 10 13 17 19 27 1 2 3 4 5 6 7 8 low high mid

Binary Search -- Example 3 (cont) val = 7 5 7 9 10 13 17 19 1 27 2 3 4 6 8 a: 5 7 9 10 13 17 19 1 27 2 3 4 6 8 a: 5 7 9 10 13 17 19 1 27 2 3 4 6 8 a:

Binary Search – C++ Code int isPresent(int *arr, int val, int N) { int low = 0; int high = N - 1; int mid; while ( low <= high ){ mid = ( low + high )/2; if (arr[mid]== val) return 1; // found! else if (arr[mid] < val) low = mid + 1; else high = mid - 1; } return 0; // not found

Binary Search: binary tree An entire sorted list First half Second half First half Second half First half The search divides a list into two small sub-lists till a sub-list is no more divisible.

Binary Search Efficiency After 1 bisection N/2 items After 2 bisections N/4 = N/22 items . . . After i bisections N/2i =1 item i = log2 N

Implementation 3: linked list TableNodes are again stored consecutively (unsorted or sorted) insert: add to front; (1or n for a sorted list) find: search through potentially all the keys, one at a time; (n for unsorted or for a sorted list remove: find, remove using pointer alterations; (n) key entry and so on

Implementation 4: Skip List Overcome basic limitations of previous lists Search and update require linear time Fast Searching of Sorted Chain Provide alternative to BST (binary search trees) and related tree structures. Balancing can be expensive. Relatively recent data structure: Bill Pugh proposed it in 1990.

Skip List Representation Can do better than n comparisons to find element in chain of length n 20 30 40 50 60 head tail

Skip List Representation Example: n/2 + 1 if we keep pointer to middle element 20 30 40 50 60 head tail

Higher Level Chains For general n, level 0 chain includes all elements 40 50 60 head tail 20 30 26 57 level 1&2 chains For general n, level 0 chain includes all elements level 1 every other element, level 2 chain every fourth, etc. level i, every 2i th element

Higher Level Chains Skip list contains a hierarchy of chains 40 50 60 head tail 20 30 26 57 level 1&2 chains Skip list contains a hierarchy of chains In general level i contains a subset of elements in level i-1

Skip List: formally A skip list for a set S of distinct (key, element) items is a series of lists S0, S1 , … , Sh such that Each list Si contains the special keys + and - List S0 contains the keys of S in nondecreasing order Each list is a subsequence of the previous one, i.e., S0  S1  …  Sh List Sh contains only the two special keys End of lecture 39, Start of lecture 40.

Lecture No.38 Data Structure Dr. Sohail Aslam

Skip List: formally S3 S2 S1 S0 + - + - + - + - 31 64 31 34 23 56 64 78 + 31 34 44 - 12 23 26 S0

Skip List: Search We search for a key x as follows: We start at the first position of the top list At the current position p, we compare x with y  key(after(p)) x = y: we return element(after(p)) x > y: we “scan forward” x < y: we “drop down” If we try to drop down past the bottom list, we return NO_SUCH_KEY

Skip List: Search Example: search for 78 S3 S2 S1 S0 + - - + - + 31 + S1 - 23 31 34 64 + S0 - 12 23 26 31 34 44 56 64 78 +

Skip List: Insertion To insert an item (x, o) into a skip list, we use a randomized algorithm: We repeatedly toss a coin until we get tails, and we denote with i the number of times the coin came up heads If i  h, we add to the skip list new lists Sh+1, … , Si +1, each containing only the two special keys

Skip List: Insertion To insert an item (x, o) into a skip list, we use a randomized algorithm: (cont) We search for x in the skip list and find the positions p0, p1 , …, pi of the items with largest key less than x in each list S0, S1, … , Si For j  0, …, i, we insert item (x, o) into list Sj after position pj

Skip List: Insertion Example: insert key 15, with i = 2 + - S0 S1 S2 10 36 23 15 p2 S2 - + p1 S1 - 23 + p0 S0 - 10 23 36 +

Randomized Algorithms A randomized algorithm performs coin tosses (i.e., uses random bits) to control its execution It contains statements of the type b  random() if b <= 0.5 // head do A … else // tail do B … Its running time depends on the outcomes of the coin tosses, i.e, head or tail

Skip List: Deletion To remove an item with key x from a skip list, we proceed as follows: We search for x in the skip list and find the positions p0, p1 , …, pi of the items with key x, where position pj is in list Sj We remove positions p0, p1 , …, pi from the lists S0, S1, … , Si We remove all but one list containing only the two special keys

Skip List: Deletion Example: remove key 34 S3 - + p2 S2 - + S0 S1 45 12 23 S0 S1 S2 - 34 + p1 S1 - 23 34 + p0 End of lecture 40. S0 - 12 23 34 45 +

Skip List: Implementation - + S2 - 34 + S1 - 23 34 + Start of 41. S0 - 12 23 34 45 +

Implementation: TowerNode 40 50 60 head tail 20 30 26 57 Tower Node TowerNode will have array of next pointers. Actual number of next pointers will be decided by the random procedure. Define MAXLEVEL as an upper limit on number of levels in a node.

Implementation: QuadNode A quad-node stores: item link to the node before link to the node after link to the node below link to the node above This will require copying the key (jitem) at different levels quad-node x Start lecture 41

Skip Lists with Quad Nodes - + S2 - 31 + S1 - 23 31 34 64 + S0 - 12 23 26 31 34 44 56 64 78 +

Performance of Skip Lists In a skip list with n items The expected space used is proportional to n. The expected search, insertion and deletion time is proportional to log n. Skip lists are fast and simple to implement in practice

Implementation 5: AVL tree An AVL tree, ordered by key insert: a standard insert; (log n) find: a standard find (without removing, of course); (log n) remove: a standard remove; (log n) key entry key entry key entry key entry and so on

Anything better? So far we have find, remove and insert where time varies between constant logn. It would be nice to have all three as constant time operations!

Implementation 6: Hashing An array in which TableNodes are not stored consecutively Their place of storage is calculated using the key and a hash function Keys and entries are scattered throughout the array. key entry 4 10 hash function array index Key 123

Hashing insert: calculate place of storage, insert TableNode; (1) find: calculate place of storage, retrieve entry; (1) remove: calculate place of storage, set it to null; (1) key entry 4 10 123 All are constant time (1) !

Hashing We use an array of some fixed size T to hold the data. T is typically prime. Each key is mapped into some number in the range 0 to T-1 using a hash function, which ideally should be efficient to compute.

Example: fruits Suppose our hash function gave us the following values: hashCode("apple") = 5 hashCode("watermelon") = 3 hashCode("grapes") = 8 hashCode("cantaloupe") = 7 hashCode("kiwi") = 0 hashCode("strawberry") = 9 hashCode("mango") = 6 hashCode("banana") = 2 kiwi banana watermelon apple mango cantaloupe grapes strawberry 1 2 3 4 5 6 7 8 9

Example Store data in a table array: kiwi banana watermelon apple table[5] = "apple" table[3] = "watermelon" table[8] = "grapes" table[7] = "cantaloupe" table[0] = "kiwi" table[9] = "strawberry" table[6] = "mango" table[2] = "banana" kiwi banana watermelon apple mango cantaloupe grapes strawberry 1 2 3 4 5 6 7 8 9

Example Associative array: kiwi table["apple"] table["watermelon"] table["grapes"] table["cantaloupe"] table["kiwi"] table["strawberry"] table["mango"] table["banana"] kiwi banana watermelon apple mango cantaloupe grapes strawberry 1 2 3 4 5 6 7 8 9

Example Hash Functions If the keys are strings the hash function is some function of the characters in the strings. One possibility is to simply add the ASCII values of the characters: æ length - 1 ö å h ( str ) = ç str [ i ] ÷ % TableSize ç ÷ è ø i = Example : h ( ABC ) = ( 65 + 66 + 67 )% TableSize

Finding the hash function int hashCode( char* s ) { int i, sum; sum = 0; for(i=0; i < strlen(s); i++ ) sum = sum + s[i]; // ascii value return sum % TABLESIZE; }

Example Hash Functions Another possibility is to convert the string into some number in some arbitrary base b (b also might be a prime number): æ length - 1 ö å h ( str ) = ç str [ i ] ´ b i ÷ % T ç ÷ è ø i = Example : h ( ABC ) = ( 65 b + 66 b 1 + 67 b 2 )% T

Example Hash Functions If the keys are integers then key%T is generally a good hash function, unless the data has some undesirable features. For example, if T = 10 and all keys end in zeros, then key%T = 0 for all keys. In general, to avoid situations like this, T should be a prime number. End of lecture 41. Start of lecture 42.

Collision kiwi banana watermelon apple mango cantaloupe grapes Suppose our hash function gave us the following values: hash("apple") = 5 hash("watermelon") = 3 hash("grapes") = 8 hash("cantaloupe") = 7 hash("kiwi") = 0 hash("strawberry") = 9 hash("mango") = 6 hash("banana") = 2 kiwi banana watermelon apple mango cantaloupe grapes strawberry 1 2 3 4 5 6 7 8 9 hash("honeydew") = 6 • Now what?

Collision When two values hash to the same array location, this is called a collision Collisions are normally treated as “first come, first served”—the first value that hashes to the location gets it We have to find something to do with the second and subsequent values that hash to this same location.

Solution for Handling collisions Solution #1: Search from there for an empty location Can stop searching when we find the value or an empty location. Search must be wrap-around at the end.

Solution for Handling collisions Solution #2: Use a second hash function ...and a third, and a fourth, and a fifth, ...

Solution for Handling collisions Solution #3: Use the array location as the header of a linked list of values that hash to this location

Solution 1: Open Addressing This approach of handling collisions is called open addressing; it is also known as closed hashing. More formally, cells at h0(x), h1(x), h2(x), … are tried in succession where hi(x) = (hash(x) + f(i)) mod TableSize, with f(0) = 0. The function, f, is the collision resolution strategy.

Linear Probing We use f(i) = i, i.e., f is a linear function of i. Thus location(x) = (hash(x) + i) mod TableSize The collision resolution strategy is called linear probing because it scans the array sequentially (with wrap around) in search of an empty cell.

Linear Probing: insert Suppose we want to add seagull to this hash table Also suppose: hashCode(“seagull”) = 143 table[143] is not empty table[143] != seagull table[144] is not empty table[144] != seagull table[145] is empty Therefore, put seagull at location 145 robin sparrow hawk bluejay owl . . . 141 142 143 144 145 146 147 148 seagull

Linear Probing: insert Suppose you want to add hawk to this hash table Also suppose hashCode(“hawk”) = 143 table[143] is not empty table[143] != hawk table[144] is not empty table[144] == hawk hawk is already in the table, so do nothing. robin sparrow hawk seagull bluejay owl . . . 141 142 143 144 145 146 147 148

Linear Probing: insert Suppose: You want to add cardinal to this hash table hashCode(“cardinal”) = 147 The last location is 148 147 and 148 are occupied Solution: Treat the table as circular; after 148 comes 0 Hence, cardinal goes in location 0 (or 1, or 2, or ...) . . . 141 142 143 144 145 146 147 148 robin sparrow hawk seagull bluejay owl

Linear Probing: find Suppose we want to find hawk in this hash table We proceed as follows: hashCode(“hawk”) = 143 table[143] is not empty table[143] != hawk table[144] is not empty table[144] == hawk (found!) We use the same procedure for looking things up in the table as we do for inserting them robin sparrow hawk seagull bluejay owl . . . 141 142 143 144 145 146 147 148

Linear Probing and Deletion If an item is placed in array[hash(key)+4], then the item just before it is deleted How will probe determine that the “hole” does not indicate the item is not in the array? Have three states for each location Occupied Empty (never used) Deleted (previously used)

Clustering One problem with linear probing technique is the tendency to form “clusters”. A cluster is a group of items not containing any open slots The bigger a cluster gets, the more likely it is that new values will hash into the cluster, and make it ever bigger. Clusters cause efficiency to degrade.

Quadratic Probing Quadratic probing uses different formula: Use F(i) = i2 to resolve collisions If hash function resolves to H and a search in cell H is inconclusive, try H + 12, H + 22, H + 32, … Probe array[hash(key)+12], then array[hash(key)+22], then array[hash(key)+32], and so on Virtually eliminates primary clusters

Collision resolution: chaining Each table position is a linked list Add the keys and entries anywhere in the list (front easiest) No need to change position! key entry key entry 4 key entry key entry 10 key entry 123

Collision resolution: chaining Advantages over open addressing: Simpler insertion and removal Array size is not a limitation Disadvantage Memory overhead is large if entries are small. key entry key entry 4 key entry key entry 10 End of Lecture 42. key entry 123

Applications of Hashing Compilers use hash tables to keep track of declared variables (symbol table). A hash table can be used for on-line spelling checkers — if misspelling detection (rather than correction) is important, an entire dictionary can be hashed and words checked in constant time. Start of lecture 43 after animation.

Applications of Hashing Game playing programs use hash tables to store seen positions, thereby saving computation time if the position is encountered again. Hash functions can be used to quickly check for inequality — if two elements hash to different values they must be different.

When is hashing suitable? Hash tables are very good if there is a need for many searches in a reasonably stable table. Hash tables are not so good if there are many insertions and deletions, or if table traversals are needed — in this case, AVL trees are better. Also, hashing is very slow for any operations which require the entries to be sorted e.g. Find the minimum key