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Profile Documents Logout
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Document
Document

... Each identifier x is transformed into a number using some radix r.  The digits of each identifier are examined.  Digits having most skewed distributions are deleted.  Enough digits are deleted so that the number of remaining digits is ...
Advanced Data Structure
Advanced Data Structure

Advanced Data Structure
Advanced Data Structure

... • Use boolean array with 232 cells • 4GB memory already • Even O(1) checking... Too much memory required ...
Powerpoint
Powerpoint

... – Reading every element is typically easy – Other things depend on the representation • Hashing finds single elements quickly – But cannot preserve order ...
Lists, Hash Tables, Trees - NEMCC Math/Science Division
Lists, Hash Tables, Trees - NEMCC Math/Science Division

PPT
PPT

... • Matching parenthesis inside block – near parenthesis. • Pre-computed table stores position of matching parentheses for all near parentheses. – O(1) time if near parenthesis. – Table size is O( N (lg N 2 )) ...
DHTs
DHTs

CS163_Topic6
CS163_Topic6

Algorithms
Algorithms

K - CS1001.py
K - CS1001.py

... such as AVL trees, and Red and Black trees, that insure that the tree remains balanced, by performing balancing operations each time an element is inserted (or deleted). This will also be taught in the Data Structures course. ...
Multiple choice questions Answer on Scantron Form
Multiple choice questions Answer on Scantron Form

arrays
arrays

... The index of the first element is always 0. Think of scalar variables as houses and array elements as apartments. Each house has its own address (i.e., variable name). Each apartment in a building has the same address/name, but requires an apartment number to differentiate it from the others. That's ...
Optimal Encodings for Range Min-Max and Top-k
Optimal Encodings for Range Min-Max and Top-k

... Our Results In this paper, we present the first optimal encodings to range topk—and therefore range k-selection also—as well as a new problem which we call range min-max, in which the goal is to return the indices of both the minimum and maximum element in the array. We present our encoding to range ...
Linked list
Linked list

... Delete(S,x) – Given a pointer/reference, x, to an elkement, delete it from S Min(S) – Return the smallest element of S Max(S) – Return the largest element of S ...
Advanced Data Structures
Advanced Data Structures

... Mark a node as removed instead of actually removing it Less coding, performance hit not big if you are not doing this frequently (may even save time) Page 12 ...
ARRAY DATA STRUCTURE
ARRAY DATA STRUCTURE

... Notice that the first cell index starts at zero (0). myList[0] = 8; ...
Dictionaries and Hash Tables
Dictionaries and Hash Tables

... In the worst case, searches, insertions and removals on a hash table take O(n) time The worst case occurs when all the keys inserted into the dictionary collide The load factor a = n/N affects the performance of a hash table Assuming that the hash values are like random numbers, it can be shown that ...
Introduction to Java - CS-UCY
Introduction to Java - CS-UCY

... MyStrangeObject[] objs; objs = new MyStrangeObject[20]; for (int I=0;I<20;I++) objs[I] = new MyStrangeObject(); ...
Ch19v2.0
Ch19v2.0

... Carrano, Data Structures and Abstractions with Java, Second Edition, (c) 2007 Pearson Education, Inc. All rights reserved. 0-13-237045-X ...
Data Structures and Algorithms Arrays
Data Structures and Algorithms Arrays

... Traverse − print all the array elements one by one. Insertion − add an element at given index. Deletion − delete an element at given index. Search − search an element using given index or by value. Update − update an element at given index. In C, when an array is initialized with size, then it assig ...
Time and location: Materials covered concepts (so far) in a nutshell COS 226
Time and location: Materials covered concepts (so far) in a nutshell COS 226

... • findMedian in ~1 , insert ~ lg n, delete ~ lg n – Example: A leaky queue that can remove from any point, that can insert to end and delete from front, all in logarithmic time or better Typical cues to look for – log n time may indicate that you need a sorted array or balanced BST or some sort of a ...
In-memory hash tables for accumulating text vocabularies
In-memory hash tables for accumulating text vocabularies

... — while there are more than 900 000 words that occur once only. In this paper we experimentally evaluate the performance of several data structures for building vocabularies, using a range of data collections and machines. Given the well-known properties of text and some initial experimentation, we ...
Chapter9
Chapter9

Elementary Data Structures and Hash Tables
Elementary Data Structures and Hash Tables

... All direct-address table operations are O(1)! So why isn’t every set implemented with a direct-address table? The space complexity is Θ(`U`) ...
Worksheet 16
Worksheet 16

... access to values. Elements are accessed using an index, and the time it takes to access any one element is no different from the time it takes to access another. However, a fundamental problem of the simple array is that the size must be specified at the time the array is created. Often the size can ...
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Bloom filter



A Bloom filter is a space-efficient probabilistic data structure, conceived by Burton Howard Bloom in 1970, that is used to test whether an element is a member of a set. False positive matches are possible, but false negatives are not, thus a Bloom filter has a 100% recall rate. In other words, a query returns either ""possibly in set"" or ""definitely not in set"". Elements can be added to the set, but not removed (though this can be addressed with a ""counting"" filter). The more elements that are added to the set, the larger the probability of false positives.Bloom proposed the technique for applications where the amount of source data would require an impractically large amount of memory if ""conventional"" error-free hashing techniques were applied. He gave the example of a hyphenation algorithm for a dictionary of 500,000 words, out of which 90% follow simple hyphenation rules, but the remaining 10% require expensive disk accesses to retrieve specific hyphenation patterns. With sufficient core memory, an error-free hash could be used to eliminate all unnecessary disk accesses; on the other hand, with limited core memory, Bloom's technique uses a smaller hash area but still eliminates most unnecessary accesses. For example, a hash area only 15% of the size needed by an ideal error-free hash still eliminates 85% of the disk accesses, an 85–15 form of the Pareto principle (Bloom (1970)).More generally, fewer than 10 bits per element are required for a 1% false positive probability, independent of the size or number of elements in the set (Bonomi et al. (2006)).
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