DATABASE

Data Redundancy and Normalization in Databases

Table of Contents

Introduction2

RDF Triple Stores2

Data Redundancy3

Normalization4

Relation of large data with normalization7

Relation between RDF triple store and normalization7

Relation normalization and data warehouse8

Database Issues9

Normalization of RDF Triple Stores9

Dynamic Table Schema10

Property Table Schema11

Class Table Schema11

Benefits of Normalization on Database12

Data Warehouse Requirements16

Conclusion17

Bibliography18

Data Redundancy and Normalization in Databases

Introduction

A database can be described as an organised collection of data's for adequate record keeping. The data's are usually organized to suit different model in reality and are arranged in such a way that it supports the processes involved in requiring the information's defined what a database is, this leads to the question to which this report is being written. The topic chosen is trying to research and data redundancy in databases.

The purpose of this report, which is a part of the assignment, is to identify the problem that are normally encountered in the process of creating or maintain a database system. The report also aims at introducing the popular type of database “triple stores,” while exploring the normalization techniques that can be used to fully utilise the potential of triple stores. The issue of data redundancy and benefits of normalization will also be discussed.

RDF Triple Stores

A triple store is a special type of graph database built on purpose to store 'triples', which may be understood as a data entity composed of subject, property/predicate and object that are related to each other in a relational format, for instance, Alice knows Bob. Information is stored and retrieved in triple stores using a query language just like a relational database, but it the former differs in a sense that it can store and retrieve data in the form of triples.

These triples can further be imported or exported by means of using Resource Description Framework (RDF) and other formats. Currently, the data stored by the web can have the capability to provide information in a format that could easily be understood and comprehended by the humans. But the meanings and relationship of information provided by conventional databases with information stored elsewhere on the web is not well defined.

This problem is solved by the Semantic Web, which employs triples store technique to store and retrieve data. This relatively modern technology is capable of retrieving and describing data from desperate and distinct sources. Resultantly, database systems capable of interpreting this data gathered from desperate sources could comprehend the data for ultimately providing in a human readable format (Khoussainova, 2012).

It should be noted that RDF stores from distinct data sources must be combined and cached on local resource such as the local search provider. In this regard, there are a number of schemas and storage implementations that have been proposed and that use databases to locally cache these triples. Moreover, there are a few implementations that make sure availability of RDF specific information by maintaining that information at the application layer, whereas some implementations store the RDF schema at the database level.

Data Redundancy

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