<?xml version="1.0" encoding="UTF-8"?>
<feed xmlns="http://www.w3.org/2005/Atom" xmlns:dc="http://purl.org/dc/elements/1.1/">
<title>National Journal of Parallel and Soft Computing (2020)</title>
<link href="https://onlineresource.ucsy.edu.mm/handle/123456789/2583" rel="alternate"/>
<subtitle/>
<id>https://onlineresource.ucsy.edu.mm/handle/123456789/2583</id>
<updated>2026-06-21T18:43:15Z</updated>
<dc:date>2026-06-21T18:43:15Z</dc:date>
<entry>
<title>Information Retrieval System Using BM25, Pivoted Normalization and CombSUM Method</title>
<link href="https://onlineresource.ucsy.edu.mm/handle/123456789/2587" rel="alternate"/>
<author>
<name>Khaing, Nu Yin</name>
</author>
<author>
<name>Htwe, Ah Nge</name>
</author>
<id>https://onlineresource.ucsy.edu.mm/handle/123456789/2587</id>
<updated>2021-06-11T07:02:05Z</updated>
<published>2021-01-01T00:00:00Z</published>
<summary type="text">Information Retrieval System Using BM25, Pivoted Normalization and CombSUM Method
Khaing, Nu Yin; Htwe, Ah Nge
Retrieving information is difficult and time&#13;
consuming for searching a variety and large number&#13;
of documents on the digital library. This paper&#13;
intends to implement effective keyword search system&#13;
for digital library.BM25 and Pivoted Normalization&#13;
are best retrieval models for information retrieval&#13;
system. The CombSUM is combining these two&#13;
methods to get more relevant documents and to give&#13;
better output result. The proposed system will help&#13;
the user to get all relevant documents according to&#13;
the given query. When the user enters the query, the&#13;
most relevant documents are ranked by using BM25,&#13;
Pivoted Normalization Method and CombSUM.
</summary>
<dc:date>2021-01-01T00:00:00Z</dc:date>
</entry>
<entry>
<title>Duplicate Record Detection in Data Cleaning Using DCS++ Algorithm</title>
<link href="https://onlineresource.ucsy.edu.mm/handle/123456789/2584" rel="alternate"/>
<author>
<name>Phyo, Yin Yin</name>
</author>
<author>
<name>Win, Thidar</name>
</author>
<id>https://onlineresource.ucsy.edu.mm/handle/123456789/2584</id>
<updated>2021-06-11T07:02:02Z</updated>
<published>2021-01-01T00:00:00Z</published>
<summary type="text">Duplicate Record Detection in Data Cleaning Using DCS++ Algorithm
Phyo, Yin Yin; Win, Thidar
Duplicate Record Detection is a multiple&#13;
record search process that represents the same&#13;
physical entity in a dataset. It is also known as the&#13;
record linkage (or) entity matching [1]. The databases&#13;
contain very large datasets. Datasets contain&#13;
duplicate records that do not share a common key or&#13;
contain errors such as incomplete information,&#13;
transcription errors and missing or differing standard&#13;
formats (non-standardized abbreviations) in the&#13;
detailed schemas of records from multiple databases.&#13;
So, the duplicate detection needs to complete its&#13;
process in a very shorter time. Duplicate detection&#13;
requires an algorithm for determining whether&#13;
records are duplicate records or not.&#13;
In this paper, calculate a similarity metric that is&#13;
commonly used to find similar field items and use the&#13;
Duplicate Count Strategy Multi-Record Increase&#13;
(DCS++) Algorithm for approximately duplicate&#13;
records detection over publication xml dataset.
</summary>
<dc:date>2021-01-01T00:00:00Z</dc:date>
</entry>
</feed>
