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Data Structure Algorithms Articles
Page 20 of 24
Level Linked (2,4)-Trees in Data Structure
In this section we explain how (2, 4)-trees can support efficient finger searches by the introduction of level links. The ideas explained in this section also implements to the more general class of height-balanced trees denoted (a, b)-trees, for b ≥ 2a.A (2, 4)-tree is defined as a height-balanced search tree where all leaves have the same depth and all internal nodes have degree two, three or four. Elements are stored at the leaves, and internal nodes only store search keys to guide searches. Since each internal node has degree at least two, it follows that a (2, 4)-tree has ...
Read MoreDynamic Finger Search Trees in Data Structure
A dynamic finger search data structure should in addition to finger searches also perform the insertion and deletion of elements at a position given by a finger.Finger search trees is defined as a variant of B-trees supporting finger searches in O(log d) time and updates in O(1) time, assuming that only O(1) moveable fingers are maintained.Traversing a finger d positions requires O(log d) time.The finger search trees (that means AVL-trees, red-black trees) constructions either consider a fixed constant number of fingers or only support updates in amortized constant time.Constructions supporting an arbitrary number of fingers and with worst case update ...
Read MoreFinger Searching in Data Structure
A finger search on a data structure is defined as an extension of any search operation that structure supports, where a reference (finger) to an element in the data structure is given along with the query. While the search time for an element is most frequently denoted as a function of the number of elements in a data structure, finger search times are treated as a function of the distance between the element and the finger.In a set of m elements, the distance d(a, b) between two elements a and b is their difference in rank. If elements a and ...
Read MoreMulti-Way Trees
A multiway tree is defined as a tree that can have more than two children. If a multiway tree can have maximum m children, then this tree is called as multiway tree of order m (or an m-way tree).As with the other trees that have been studied, the nodes in an m-way tree will be made up of m-1 key fields and pointers to children.multiway tree of order 5To make the processing of m-way trees easier some type of constraint or order will be imposed on the keys within each node, resulting in a multiway search tree of order m ...
Read MoreRebalancing Algorithms
The rebalancing Algorithms can be performed in following way −Day-Stout-Warren AlgorithmWe can implement actually rebalance method using the Day-Stout-Warren Algorithm.It's linear in the number of nodes.The following is a presentation of the basic DSW Algorithm in pseudo code.A node is allocated called as the "pseudo-root" and make the tree's actual root as the right child of the pseudo-root.Call tree-to-vine function for converting tree to sorted linked list with the pseudo-root as its argument.Call vine-to-tree function for converting sorted linked list to tree again with the pseudo-root and the size (number of elements) of the tree as its argument.The tree's actual ...
Read MoreBlocked Bloom Filter
We select a memory block first.Then we select local Bloom Filter within each block.It might cause imbalance between memory blocksThis filter is efficient, but poor false positive rate(FPR).At first instance, blocked Bloom filters should have the same FPR (False Positive Rate) as standard Bloom filters of the same size.Blocked Bloom Filter consists of a sequence of block b comparatively less than standard Bloom filters (Bloom filter blocks), each of which fits into one cache-line.Blocked Bloom filter scheme is differentiated from the partition schemes, where each bit is inserted into a different block.Blocked Bloom Filter is implemented in following ways −Bit ...
Read MoreCounter Size and Counter Overflow
Counter SizeWe must select counters large enough for avoiding overflow.Size is 4 bits/counter suggested by Poisson approximation.Average load implementing k = (ln 2)m/n counters is ln 2.Probability a counter has load minimum 16:≈e-ln2(ln 2)16/16!≈6.78E-17We consider 4 bits/counter for comparisons.Counter OverflowWhen a counter does overflow, it may be arrived at its maximum value.This situation can later cause a false negative only if eventually the counter goes down to 0 when it should have remained at nonzero.The expected time to this situation is very large but is something we need to keep in mind for any application that does not permit false ...
Read MoreCounting Bloom Filter
Basic ConceptA Counting Bloom filter is defined as a generalized data structure of Bloom filter that is implemented to test whether a count number of a given element is less than a given threshold when a sequence of elements is given. As a generalized form, of Bloom filter there is possibility of false positive matches, but no chance of false negatives – in other words, a query returns either "possibly higher or equal than the threshold" or "definitely less than the threshold".Algorithm descriptionMost of the parameters, used under counting bloom filter, are defined same with Bloom filter, such as n, ...
Read MorePerformance Metrics
There are three performance metrics for Bloom filters that can be traded off: computation or execution time (corresponds to the number k of hash functions), size of filter (corresponds to the number m of bits), and probability of error (corresponds to the false positive ratef = (1 − p)k )The Bloom filter (BF) introduces an error tolerance to enhance lookup performance and space efficiency. The Bloom filter either returns true or false. Thus, the result of Bloom filter is fallen under any one of the following classes: true positive, false positive, true negative, and false negative. Maximum number the Bloom ...
Read MoreMultiple-Choice Hashing
Multiple choice hashing is named because it employs the implementation of multiple hash functions.On a high level, when there are multiple hash functions each item is mapped to multiple buckets and therefore the Algorithmdesigner has freedom to select in which of those the item would reside.It turns out that this freedom permits for Algorithms which obtain allocations that are much more balanced then that availed by implementing a single hash function.We will present the main Algorithmic ideas and the main mathematical tools that are implemented for proving bounds on the allocations these Algorithms produce.We will see that the analysis is ...
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