Self Organizing List | Set 1 (Introduction)
Last Updated :
19 May, 2017
The worst case search time for a sorted linked list is O(n). With a Balanced Binary Search Tree, we can skip almost half of the nodes after one comparison with root. For a sorted array, we have random access and we can apply Binary Search on arrays.
One idea to make search faster for Linked Lists is
Skip List. Another idea (which is discussed in this post) is to
place more frequently accessed items closer to head.. There can be two possibilities. offline (we know the complete search sequence in advance) and online (we don't know the search sequence).
In case of offline, we can put the nodes according to decreasing frequencies of search (The element having maximum search count is put first). For many practical applications, it may be difficult to obtain search sequence in advance. A
Self Organizing list reorders its nodes based on searches which are done. The idea is to use locality of reference (In a typical database, 80% of the access are to 20% of the items). Following are different strategies used by Self Organizing Lists.
1) Move-to-Front Method: Any node searched is moved to the front. This strategy is easy to implement, but it may over-reward infrequently accessed items as it always move the item to front.
2) Count Method: Each node stores count of the number of times it was searched. Nodes are ordered by decreasing count. This strategy requires extra space for storing count.
3) Transpose Method: Any node searched is swapped with the preceding node. Unlike Move-to-front, this method does not adapt quickly to changing access patterns.
Competitive Analysis:
The worst case time complexity of all methods is O(n). In worst case, the searched element is always the last element in list. For
average case analysis, we need probability distribution of search sequences which is not available many times.
For online strategies and algorithms like above, we have a totally different way of analyzing them called
competitive analysis where performance of an online algorithm is compared to the performance of an optimal offline algorithm (that can view the sequence of requests in advance). Competitive analysis is used in many practical algorithms like caching, disk paging, high performance computers. The best thing about competitive analysis is, we don't need to assume anything about probability distribution of input. The Move-to-front method is 4-competitive, means it never does more than a factor of 4 operations than offline algorithm (See
the MIT video lecture for proof).
We will soon be discussing implementation and proof of the analysis given in the video lecture.
References:
http://en.wikipedia.org/wiki/Self-organizing_list
MIT Video Lecture
http://www.eecs.yorku.ca/course_archive/2003-04/F/2011/2011A/DatStr_071_SOLists.pdf
http://en.wikipedia.org/wiki/Competitive_analysis_(online_algorithm)
This article is compiled by
Abhay Rathi.
Similar Reads
Advanced Data Structures Advanced Data Structures refer to complex and specialized arrangements of data that enable efficient storage, retrieval, and manipulation of information in computer science and programming. These structures go beyond basic data types like arrays and lists, offering sophisticated ways to organize and
2 min read
Generic Linked List in C A generic linked list is a type of linked list that allows the storage of different types of data in a single linked list structure, providing more versatility and reusability in various applications. Unlike C++ and Java, C doesn't directly support generics. However, we can write generic code using
5 min read
Memory efficient doubly linked list We need to implement a doubly linked list with the use of a single pointer in each node. For that we are given a stream of data of size n for the linked list, your task is to make the function insert() and getList(). The insert() function pushes (or inserts at the beginning) the given data in the li
9 min read
XOR Linked List - A Memory Efficient Doubly Linked List | Set 1 In this post, we're going to talk about how XOR linked lists are used to reduce the memory requirements of doubly-linked lists.We know that each node in a doubly-linked list has two pointer fields which contain the addresses of the previous and next node. On the other hand, each node of the XOR link
15+ min read
XOR Linked List – A Memory Efficient Doubly Linked List | Set 2 In the previous post, we discussed how a Doubly Linked can be created using only one space for the address field with every node. In this post, we will discuss the implementation of a memory-efficient doubly linked list. We will mainly discuss the following two simple functions. A function to insert
10 min read
Skip List - Efficient Search, Insert and Delete in Linked List A skip list is a data structure that allows for efficient search, insertion and deletion of elements in a sorted list. It is a probabilistic data structure, meaning that its average time complexity is determined through a probabilistic analysis.In a skip list, elements are organized in layers, with
6 min read
Self Organizing List | Set 1 (Introduction) The worst case search time for a sorted linked list is O(n). With a Balanced Binary Search Tree, we can skip almost half of the nodes after one comparison with root. For a sorted array, we have random access and we can apply Binary Search on arrays. One idea to make search faster for Linked Lists is
3 min read
Unrolled Linked List | Set 1 (Introduction) Like array and linked list, the unrolled Linked List is also a linear data structure and is a variant of a linked list. Why do we need unrolled linked list? One of the biggest advantages of linked lists over arrays is that inserting an element at any location takes only O(1). However, the catch here
10 min read
Splay Tree
Trie