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Copyright © 2009 Curt Hill Self Organizing Lists Another form of searchable list
Normal Searched Lists Unlike an array, a list cannot be searched by a binary search A list is sorted only so that search can quit early It is thus it is largely an O(N) activity Since there are plenty of better searches that are O(log N) or better a list is seldom a good choice Copyright © 2009 Curt Hill
Self organizing lists Instead of sorting by key, sort by frequency Drive frequently used items to the front of the list and seldom accessed items to the back of the list A self organizing list modifies itself based on accesses Do this without prior information as to how frequently an item will be accessed –These lists may be kept in either vectors or lists Only uses a sequential search technique Copyright © 2009 Curt Hill
How? Several techniques Keep a count of accesses Move each item accessed to the front Move each item closer to the front Copyright © 2009 Curt Hill
Counts Keep a count of accesses in each record Keep the list sorted by this number of accesses At the beginning the list has no accesses so all counts are zero and hence any order is acceptable Each search then increments the count of just one item Copyright © 2009 Curt Hill
Counts Again Incrementing the count does not provoke a full sort of the list Rather you just move that item forward until its count is larger than the next and smaller than the prior The disadvantage of this approach –Extra storage in the list for the count Once a large number of accesses have occurred things do not move very far Copyright © 2009 Curt Hill
More on counts A number of accesses to an item in a short time normally moves the item –If that number of accesses is small compared to the total number it will not move item very far At the end of the run the list has the optimal static order Possible to save this or a variant of this for next time –Either save the exact counts or the counts divided by some constant Copyright © 2009 Curt Hill
Move to front Each accessed item becomes the first item in the list –At least until the next access Push all subsequent items down a slot Compare this to caching This works much better for lists than vectors since insertion in a list is painless while insertion in a list is painful Copyright © 2009 Curt Hill
Move to Front There is a pathlogically bad case where we always reference the last item –This case is extremely unlikely We typically use self-organizing lists where we have very few items that are very frequently used Copyright © 2009 Curt Hill
Transposition Every time a record is accessed swap it with the item before it Frequently used items will migrate toward the front and seldom used ones will move toward the back One item never changes the list much Modification: If this is an array you may want to swap the item with something closer to the front, such as halfway to the front rather than move it just one slot Copyright © 2009 Curt Hill
Example –1 is 30% –1 is 25% –1 is 20% –1 is 5% –1 is 4% –1 is 3% –2 are 2% –1 is 1.5% –1 is 1% –5 are.5% –2 are.3 % –4 are.2% –23 are.1% –6 arr.05% Copyright © 2009 Curt Hill A list of 50 items with widely differing frequencies.
Do the math Assume that the self-organizing list achieves optimal static order –The first three items account for 75% of the searches The average searches is the sum of the frequency times the position divided by total searches In this case the average search length is 3.9965 An log 2 N search would give a length of 9.9657 Copyright © 2009 Curt Hill
Frequency Distribution Copyright © 2009 Curt Hill
Summary Self-organizing lists need a radical frequency distribution to be effective against O(log 2 N) searches –Such as a binary or tree search A failure has to search entire list, so these should be infrequent as well Like perfect hashes these need to be looked for Very good search when conditions are right Copyright © 2009 Curt Hill
Copyright © Curt Hill Sorting Ordering an array.
Searching Given distinct keys k 1, k 2, …, k n and a collection of n records of the form »(k 1,I 1 ), (k 2,I 2 ), …, (k n, I n ) Search Problem - For key.
CHAPTER 09 Compiled by: Dr. Mohammad Omar Alhawarat Sorting & Searching.
Copyright © by Curt Hill Searching and Sorting A Summary on Searching.
Copyright © 2009 Curt Hill Look Ups A Recurring Theme.
FALL 2006CENG 351 Data Management and File Structures1 External Sorting.
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Amortized Analysis The problem domains vary widely, so this approach is not tied to any single data structure The goal is to guarantee the average performance.
©Silberschatz, Korth and Sudarshan12.1Database System Concepts Chapter 12: Indexing and Hashing Basic Concepts Ordered Indices B+-Tree Index Files B-Tree.
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Hashing General idea: Get a large array
HASHING PROJECT 1. SEARCHING DATA STRUCTURES Consider a set of data with N data items stored in some data structure We must be able to insert, delete.
“Enthusiasm releases the drive to carry you over obstacles and adds significance to all you do.” – Norman Vincent Peale Thought for the Day.
Simple Sorting Algorithms. 2 Bubble sort Compare each element (except the last one) with its neighbor to the right If they are out of order, swap them.
Copyright 2003Curt Hill Hash indexes Are they better or worse than a B+Tree?
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This material in not in your text (except as exercises) Sequence Comparisons –Problems in molecular biology involve finding the minimum number of edit.
1 Joe Meehean. Problem arrange comparable items in list into sorted order Most sorting algorithms involve comparing item values We assume items.
Copyright © Curt Hill Query Evaluation Translating a query into action.
Chapter 11 Heap. Overview ● The heap is a special type of binary tree. ● It may be used either as a priority queue or as a tool for sorting.
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Chapter 18: Searching and Sorting Algorithms. Objectives In this chapter, you will: Learn the various search algorithms Implement sequential and binary.
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Hash Tables CS 310 – Professor Roch Weiss Chapter 20 All figures marked with a chapter and section number are copyrighted © 2006 by Pearson Addison-Wesley.
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Copyright © 2011 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Chapter 17 Disk Storage, Basic File Structures, and Hashing.
Database System Concepts, 6 th Ed. ©Silberschatz, Korth and Sudarshan See for conditions on re-usewww.db-book.com Module D: Hashing.
Hashtables. An Abstract data type that supports the following operations: –Insert –Find –Remove Search trees can be used for the same operations but require.
Objectives Learn how to implement the sequential search algorithm Explore how to sort an array using the selection sort algorithm Learn how to implement.
Quick Sort, Shell Sort, Counting Sort, Radix Sort AND Bucket Sort
Lecture 12COMPSCI.220.FS.T Symbol Table and Hashing A ( symbol) table is a set of table entries, ( K,V) Each entry contains: –a unique key, K,
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Hashing COMP171 Fall Hashing 2 Hash table * Support the following operations n Find n Insert n Delete. (deletions may be unnecessary in some applications)
Marwan Al-Namari Hassan Al-Mathami. Indexing What is Indexing? Indexing is a mechanisms. Why we need to use Indexing? We used indexing to speed up access.
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