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An adaptive algorithm for detection of duplicate records
 

An adaptive algorithm for detection of duplicate records

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    An adaptive algorithm for detection of duplicate records An adaptive algorithm for detection of duplicate records Presentation Transcript

    • An Adaptive Algorithm for Detection of Duplicate Records Presented By: Rama kanta Behera IT200127207 Under the guidance of : Miss Ipsita Mishra
    • 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
    • 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.
    • 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
    • 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
    • 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
    • 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)
    • 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.
    • Fig: Flowchart
    • IMPLEMENTATIONS There are three important implementation details that need to be discussed
        • Size of Records set
        • Use of Logarithms
        • Subsets of Records set
    • 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”.
    • THANK YOU !!!