Identification Of Diabetes Patients At Risk Of Other Chronic Diseases From Unstructured Data Using Big Data And Semantic Web Technologies

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Identification Of Diabetes Patients At Risk Of Other Chronic Diseases From Unstructured Data Using Big Data And Semantic Web Technologies

Title Page

Certification/Declaration

Approval Page

Dedication

Acknowledgement

Abstract

Table of content

 

Chapter 1

Introduction

1:1 Introduction

1:2 Background of the Study

1:3 Statements of Problems

1:4 Objectives of the Study

1:5 Research Question

1:6 Study of the Hypothesis

1:7 Significance of the Study

1:8 Justification of the Study

1:9 Scope of the Study

1:10 Definition of Terms

 

Chapter 2

Literature Review

2:0 Introduction

2:1 Conceptual Clarification

2:2 Theoretical Framework

2:3 Literatures on the Subject Matter

 

Chapter 3

Research Methodology

3:0 Area of Study

3:1 Source of Data

3:2 Sampling Techniques

3:3 Method Data Collection

3:4 Method of Data Analysis

3:5 Reliability of Instrument

3:6 Validity of Instrument

3:7 Limitations of the Study

 

Chapter 4

Data Analysis

4:0 Introduction

4:1 Finding of the Study

4:2 Discussion of the Study

4:3 Summary

 

Chapter 5

Summary, Conclusion and Recommendation

5:0 Summary of Findings

5:1 Conclusion

5:2 Recommendations

5:3 Proposal for Further Studies

 

Semantic technology encodes meanings separately from data and content files, and separately from application code.

This enables machines as well as people to understand, share and reason with them at execution time. With semantic technologies, adding, changing and implementing new relationships or interconnecting programs in a different way can be just as simple as changing the external model that these programs share.

With traditional information technology, on the other hand, meanings and relationships must be predefined and “hard wired” into data formats and the application program code at design time. This means that when something changes, previously unexchanged information needs to be exchanged, or two programs need to interoperate in a new way, the humans must get involved.

Off-line, the parties must define and communicate between them the knowledge needed to make the change, and then recode the data structures and program logic to accommodate it, and then apply these changes to the database and the application. Then, and only then, can they implement the changes.

Semantic technologies are “meaning-centered.” They include tools for:

autorecognition of topics and concepts,
information and meaning extraction, and
categorization.
Given a question, semantic technologies can directly search topics, concepts, associations that span a vast number of sources.

Semantic technologies provide an abstraction layer above existing IT technologies that enables bridging and interconnection of data, content, and processes. Second, from the portal perspective, semantic technologies can be thought of as a new level of depth that provides far more intelligent, capable, relevant, and responsive interaction than with information technologies alone.

 

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