Effective Data Mining Techniques for Unstructured Data in Big Data

Table of Contents

1. INTRODUCTION
1.1 IDEA AND MOTIVATION
1.2 LITERATURE SURVEY

2. PROBLEM DEFINITION AND SCOPE
2.1 SCOPE
2.2 SOFTWARE CONTEXT
2.3 SOFTWARE CONSTRAINTS
2.4 OUTCOMES
2.5 HARDWARE SPECIFICATION
2.6 S/W SPECIFICATION
2.7 AREA OF DISSERTATION

3. DISSERTATION PLAN
3.1 PROJECT PLAN
3.2 TIMELINE OF PROJECT
3.3 FEASIBILITY STUDY
3.3.1 Economical Feasibility
3.3.2 Technical Feasibility
3.3.3 Operational feasibility
3.3.4 Time Feasibility
3.4 RISK MANAGEMENT
3.4.1 Project Risk:
3.4.2 Risk Assessment
3.5 EFFORT AND COST ESTIMATION
3.5.1 Lines of code (LOC)
3.5.2 Effort
3.5.3 Development Time
3.5.4 Number of People

4. SOFTWARE REQUIREMENT SPECIFICATION
4.1 INTRODUCTION
4.1.1 Purpose
4.1.2 Scope of Document
4.1.3 Overview of responsibilities of developer
4.2 PRODUCT OVERVIEW
4.2.1 Block diagram
4.3 FUNCTINAL MODEL
4.3.1 Flow diagram
4.3.2 Data Flow Diagram
4.3.3 UML Diagrams
4.3.3.1 Sequence diagram
4.3.3.2 Class diagram
4.3.4 Non-Functional Requirements
4.4 BEHAVIORAL MODEL AND DESCRIPTION
4.4.1 Description of software behavior
4.4.2 Use case diagram

5. DETAILED DESIGN
5.1 ARCHITECTURE DESIGN
5.1.1 Algorithms:
5.2 INTERFACES
5.2.1 Human Interface
5.2.2. Database interface

6. TESTING
6.1 INTRODUCTION
6.1.1 Goals and Objective
6.2 TESTING STRATEGY
6.2.1 White Box Testing
6.2.2 Black Box Testing
6.2.3 System testing
6.2.4 Performance testing

7. DATA TABLE AND DISCUSSION
7.1 INPUT TO THE SYSTEM
7.2 OUTPUT:
7.3 PERFORMANCE OF PROPOSED SYSTEM
7.3.1 Performance of proposed system with respect to baseline algorithm:
7.3.2 Performance of proposed system with respect to blowfish encryption algorithm:
7.4 RESULT
7.4.1 Difference between proposed algorithm and base algorithm i.e provider aware algorithm:

8. SUMMARY AND CONCLUSION
8.1 FUTURE ENHANCEMENT

REFERENCES