An adaptive algorithm for detection of duplicate records
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The document proposes an adaptive algorithm for detecting duplicate records in a database. The algorithm hashes each record to a unique prime number. It then divides the product of prior prime numbers by the new record's prime number. If it divides evenly, the record is duplicate. Otherwise, it is distinct and the product is updated with the new prime number, making the algorithm adaptive. The algorithm aims to reduce duplicate detection costs while maintaining scalability and caching prior records.
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08 Hash Tables
08 Hash TablesAndres Mendez-Vazquez At the beginning, the number of elements in a set of numbers to be stored in a computer system used to be not so large or having a wide range. Then, using a
simple table T [0, 1, ..., m − 1]called, direct-address table, could be used to store those numbers. As the situation became more and more complex, and a new idea came to be:
Definition
An associative array, map, symbol table, or dictionary is an abstract data type composed of a collection of tuples {(key, value)}
This can bee seen in the example of dictionaries in any spoken language. The problem became more complex when the range of the possible values for the
keys at the tuples became unbounded. Therefore a new type of data structure is needed to avoid the sparsity problem in the data, the hash table.
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4.4 hashing02
4.4 hashing02Krish_ver2 The document outlines various data structures and algorithms for implementing dictionaries and hash tables, including:
- Separate chaining, which handles collisions by storing elements that hash to the same value in a linked list. Find, insert, and delete take average time of O(1).
- Open addressing techniques like linear probing and quadratic probing, which handle collisions by probing to alternate locations until an empty slot is found. These have faster search but slower inserts and deletes.
- Double hashing, which uses a second hash function to determine probe distances when collisions occur, reducing clustering compared to linear probing.
Performance Analysis of Hashing Mathods on the Employment of App 
Performance Analysis of Hashing Mathods on the Employment of App IJECEIAES The administrative process carried out continuously produces large data. So the search process takes a long time. The search process by hashing methods can save time faster. Hashing is methods that directly access data in a table by making references to the key that hashing becomes the address in the table. The performance analysis of the hashing method is done by the number of 18 digit character values. The process of analysis is done on applications that have been implemented in the application. The algorithm of hashing method analyzed is progressive overflow (PO) and linear quotient (LQ). The main purpose of performance analysis of hashing method is to know how gig the performance of each method. The results analyzed showed the average value of collision with 15 keys in the analysis of 53.3% yield the same value, while 46.7% showed the linear quotient has better performance.
Presentation1
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Final exam in advance dbms
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Apriori algorithm
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13-hashing.ppt
13-hashing.pptsoniya555961 Hash tables provide an effective way to implement dictionaries by mapping keys to table slots using hash functions. Collisions occur when multiple keys hash to the same slot, but can be resolved through chaining or open addressing. With chaining, elements that hash to the same slot are stored in a linked list at that slot. All dictionary operations take expected O(1) time with hash tables using chaining due to uniform hashing and load factors remaining constant as the table grows. Good hash functions satisfy the uniform hashing assumption and distribute keys evenly among slots.
Design data Analysis hashing.ppt by piyush
Design data Analysis hashing.ppt by piyush22001003058 The document discusses hashing techniques for efficiently searching large datasets. Hashing maps data items to a smaller table using a hashing function to facilitate faster searching compared to linear or binary search. Collision resolution methods like open addressing and separate chaining are used to handle collisions when multiple items hash to the same location. Open addressing techniques like linear probing, quadratic probing and double hashing resolve collisions by searching adjacent locations, while separate chaining stores collided items in linked lists referenced by the hash table. Hashing allows for constant time search independent of the number of items.
Hashing algorithms and its uses
Hashing algorithms and its usesJawad Khan Hashing algorithms are used to access data in hash tables through a hash function that converts data into a hash value or key. This key is used to determine the position of data in the hash table, allowing for fast lookup. Collisions can occur if different data hashes to the same key, and are resolved through techniques like open addressing, chaining, or rehashing. Hashing provides efficient indexing and retrieval of data in many applications like databases, compilers, and blockchain.
Analysis Of Algorithms - Hashing
Analysis Of Algorithms - HashingSam Light The document discusses hashing techniques for implementing dictionaries. It begins by introducing the direct addressing method, which stores key-value pairs directly in an array indexed by keys. However, this wastes space when there are fewer unique keys than array slots. Hashing addresses this by using a hash function to map keys to array slots, reducing storage needs. However, collisions can occur when different keys hash to the same slot. The document then covers various techniques for handling collisions, including chaining, linear probing, quadratic probing, and double hashing. It also discusses properties of good hash functions such as minimizing collisions between related keys and producing uniformly random mappings.
computer notes - Data Structures - 36
computer notes - Data Structures - 36ecomputernotes The document discusses different methods for handling collisions in hash tables, which occur when two keys hash to the same slot. It describes linear probing, where the next empty slot is used to store the colliding key; quadratic probing which uses a quadratic function to determine subsequent slots; and chaining, where each slot contains a linked list of colliding keys. It notes the advantages and disadvantages of each approach.
Algorithms notes tutorials duniya
Algorithms notes tutorials duniyaTutorialsDuniya.com This document provides information about dictionaries and hash tables. It defines dictionaries as dynamic sets that support operations like insertion, deletion, and searching. Hash tables are described as an efficient implementation of dictionaries that map keys to array positions using a hash function. The document discusses hash functions, collisions, open and closed addressing techniques to handle collisions, and qualities of good hash functions.
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- 1. An Adaptive Algorithm for Detection of Duplicate Records Presented By: Rama kanta Behera IT200127207 Under the guidance of : Miss Ipsita Mishra
- 2. INTRODUCTION A “ records set ” is a list of prior distinct records. A new record is to be verified for a duplicate against the records set A database is a collection of related data. Various Algorithms like Matching learning algo, Learnable string similarity measures Adaptive Algo
- 3. OBJECTIVES Reduced cost of duplicate record detection. Perfect scalability of one such detection procedure. Cache prior information of distinct records and thus cause retaining of prior records redundant for furthering the search Keep the algorithm adaptive.
- 4. PREVALENT METHODS The Brute Force Method This method consumes complexity of the order number of records in the records set and requires all prior records to be stored. Method by Rail et. al The comparison of a new record against the records set is reduced from being full text match to comparing two integers
- 5. OUTLINE OF THE PROPOSED SOLUTION The central idea behind the present algorithm is based on the fundamental property of primality of numbers I f(x) Record set Integer number space Fig: hashing I P Record set Integer number Prime number f(x) g(x) Fig: Extended hashing into prime space
- 6. r1 r2 … rn I1 I2 … In P1 P2 … Pn PRODUCT( P prior) f(x) g(x) P1*p2 …*pn= P prior Fig: The complete algorithm
- 7. REALIZATION OF THE ALGORITHM Two functions f(x) and g(x) are to be realized for the implementation of the algorithm. Realizing f(x) Realizing g(x)
- 8. STEPS OF THE ALGORITHM Step 1 : For each new record, hash is performed and unique hash value (Hnew) for each distinct record is obtained. Step 2 : Hnew is mapped to its corresponding unique prime (Pnew). Step 3 : Pprior is divided with Pnew. If Pnew exactly divides Pprior, then the corresponding record to Pnew is a duplicate and already exists in Pprior. Else, Pnew is a distinct record. Step 4 : If Pnew is a distinct record, Pprior is multiplied with Pnew and the result is stored back in Pprior. Thus updating Pprior renders the algorithm adaptive.
- 10. IMPLEMENTATIONS There are three important implementation details that need to be discussed Size of Records set Use of Logarithms Subsets of Records set
- 11. CONCLUSION A new approach to handle duplicate records is presented This approach combines the concepts of number theory and algorithmic to solve the oftener felt problem of “duplicate record detection”.
- 12. THANK YOU !!!