DATA STANDARDIZATION USING HIDDEN MARKOV MODEL

   Technology MsSql, Java,Advance Java,Spring
   Category Web Application

   Description

Record linkage refers to the process of joining records that
relate to the same entity or event in one or more data collections.
In the absence of a shared, unique key, record linkage involves the
comparison of ensembles of partially-identifying, non-unique data items
between pairs of records. Data items with variable
formats, such as names and addresses, need to be transformed and normalized
in order to validly carry out these comparisons. Traditionally, deterministic
rule-based data processing systems have been used to carry out this
pre-processing, which is commonly referred to as “standardization”.
This project describes an alternative approach to standardization, using
a combination of lexicon-based tokenization and probabilistic Hidden Markov
Models (HMMs). The project is developed using Visual Studio with C# .Net as
programming language. There is only one entity who will have the access to
the system which is admin. Admin first need to login using its login
credentials and then only he/she can access the system. After successful
login, admin can now add the training data by filling up all the registration
fields. While analyzing the data, admin will be asked to fill data in random
manner into information fields. The input data in the information tabs is a
collection of unstructured data which needs to be structured in a proper manner.
After filling the random data in random field, admin can now analyse the
inputted data, and based on input data the algorithm will process and provide
the data in a structured format and the data will be displayed into analysed
data in their respective fields.

  IEEE Paper
  Yes
  IEEE Paper
  Year 2017