Application of clustering in managing unstructured textual data in relational database / Wael Mohamed Shaher Yafooz

Huge reliance on computer usage in everyday life, leads to a continuous increase of large data applications in textual forms. The data are reposited to a secondary storage for future usage. Therefore, a relational database (RDB) is most commonly used as a backbone in most application software for or...

Full description

Saved in:
Bibliographic Details
Main Author: Mohamed Shaher Yafooz, Wael
Format: Book Section
Language:English
Published: Institute of Graduate Studies, UiTM 2015
Subjects:
Online Access:http://ir.uitm.edu.my/id/eprint/19375/1/ABS_WAEL%20MOHAMED%20SHAHER%20YAFOOZ%20TDRA%20VOL%207%20IGS%2015.pdf
http://ir.uitm.edu.my/id/eprint/19375/
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Huge reliance on computer usage in everyday life, leads to a continuous increase of large data applications in textual forms. The data are reposited to a secondary storage for future usage. Therefore, a relational database (RDB) is most commonly used as a backbone in most application software for organising such data into structured form. The RDB has robust and powerful structures for managing, organising, and retrieving the data. However, the database structure can still contain large amounts of unstructured textual data. Dealing with unstructured textual data leads to three basic issues; users encounter difficulties to find useful information, inaccurate information retrieval and insufficient performance of query processing. Attempts have been made to resolve all of these issues by using several methods such as: full text searching, text indexing, a database schema management, database data model, and query-based techniques. However, the front-end approach, in the form of software applications, are still needed to organise the unstructured textual information in the RDB. This study proposes a Textual Virtual Schema Model (TVSM) as the back-end approach to reorganising textual data inside relational databases, while performing automatic semantic linking and clustering assignments…