Android Shopping Location Identifier

 

Android Shopping Location Identifier

 

 PROJECT ID: ANDROID08

 

PROJECT NAME: Android Shopping Location Identifier

 

PROJECT CATEGORY: MCA / BCA / BCCA / MCM / POLY / ENGINEERING

 

PROJECT ABSTRACT:

Given a set of points P and a query set Q, a group enclosing query (GEQ) fetches the point p_ 2 P such that the maximum distance of p_ to all points in Q is minimized. This problem is equivalent to the Min-Max case (minimizing the maximum distance) of aggregate nearest neighbor queries for spatial databases. This work first designs a new exact solution by exploring new geometric insights, such as the minimum enclosing ball, the convex hull and the furthest voronoi diagram of the query group. To further reduce the query cost, especially when the dimensionality increases, we turn to approximation algorithms.

 Our main approximation algorithm has a worst case p2-approximation ratio if one can find the exact nearest neighbor of a point. In practice, its approximation ratio never exceeds 1.05 for a large number of data sets up to six dimensions. We also discuss how to extend it to higher dimensions (up to 74 in our experiment) and show that it still maintains a very good approximation quality (still close to 1) and low query cost. In fixed dimensions, we extend the p2-approximation algorithm to get a (1 + ǫ)-approximate solution for the GEQ problem. Both approximation algorithms have O (log N +M) query cost in any fixed dimension, where N and M are the sizes of the data set P and query group Q. Extensive experiments on both synthetic and real data sets, up to 10 million points and 74 dimensions, confirm the efficiency, effectiveness and scalability of the proposed algorithms, especially their significant improvement over the state-of-the-art method.

Existing System:

While being general enough to cover different aggregate operators, its generality also means that important opportunities could be overlooked to optimize query algorithms for specific operators. For instance, the state of the art is limited by heuristics that may yield very high query cost in certain cases, especially for data sets and queries in higher (more than two) dimensions. Motivated by this observation, this work focuses on one specific aggregate operator, namely the MAX, for the aggregate nearest neighbor queries in large databases and designs methods that significantly reduce the query cost compared to the MBM (Minimum Bounding Method) algorithm from.

Following the previous instance when studying a specific aggregate type for aggregate nearest neighbor queries (e.g., group nearest neighbor queries for the SUM operator, we designate a query name, the group enclosing query (GEQ), for an aggregate nearest neighbor query with the MAX operator. An example of the GEQ problem is illustrated.

More applications could be listed to demonstrate the usefulness of this query and more examples are available from the prior study. Intuitively, in contrast to many existing variants of the nearest neighbor queries that ask for the best answer of the average case the GEQ problem searches for the best answer of the worst case. This problem becomes even more difficult if the query group is large as well.

Proposed System:

This work presents new, efficient algorithms, including both exact and approximate versions, for the GEQ problem that significantly outperform the state of the art, the MBM algorithm. Specifically, • We present a new exact search method for the GEQ problem in Section 4 that instantiates several new geometric insights, such as the minimum enclosing ball, the convex hull and the furthest voronoi diagram of the query group, to achieve higher pruning power than the MBM approach.

• We design a √2-approximation (worst case approximation ratio in any dimensions) algorithm in Section 5.1, if one can find the exact nearest neighbor of a point and the minimum enclosing ball of Q. Its asymptotic query cost is O (log N + M) in any fixed dimensions. Our idea is to reduce the GEQ problem to the classical nearest neighbor search by utilizing the center of the minimum enclosing ball for Q.

• We extend the above idea to a (1+ǫ)-approximation algorithm in any fixed dimension in Section 5.2. This algorithm has a strong theoretical interest and it also achieves the optimal O (log N+M) query cost in any fixed dimension.

• We extend the same idea from the √2-approximate algorithm to much higher dimensions in Section 5.3, since it is impossible to find the exact nearest neighbor efficiently and the exact minimum enclosing ball in high dimensions in practice.

• We discuss the challenges when Q becomes large and disk-based in Section 6.1, and show how to adapt our algorithms to handle this case efficiently. We also present an interesting variation of the GEQ problem, the constrained GEQ.

• We demonstrate the efficiency, effectiveness and scalability of our algorithms with extensive experiments. These results show that both our exact and approximate methods have significantly outperformed the MBM method up to 6 dimensions. Beyond 6 dimensions and up to very high dimensions (d = 74), our approximate algorithm is still efficient and effective, with an average approximation ratio that is close to 1 and very low IO cost.

 

Implementation Modules:

1. Aggregate Nearest Neighbor

2. Approximate Nearest Neighbor

3. Min Max Nearest Neighbor

4. Nearest Neighbor

 

SOFTWARE REQUIREMENTS:

Operating system          :                               Windows XP/7.

Coding Language          :                               Java 1.7

Tool Kit                       :                               Android 2.3 ABOVE

IDE                              :                               Eclipse

HARDWARE REQUIREMENTS:

 System                        :                       Pentium IV 2.4 GHz.

Hard Disk                    :                     40 GB.

Floppy Drive                :                      1.44 Mb.

Monitor                        :                       15 VGA Colour.

Mouse                          :                        Logitech.

Ram                             :                        512 Mb.

MOBILE                     :                        ANDROID

 

 

TABLE OF CONTENTS

·        Title Page      

·        Declaration

·        Certification Page

·        Dedication

·        Acknowledgements

·        Table of Contents

·        List of Tables

·        Abstract

 

CHAPTER SCHEME

CHAPTER ONE: INTRODUCTION

CHAPTER TWO: OBJECTIVES

CHAPTER THREE: PRELIMINARY SYSTEM ANALYSIS

·         Preliminary Investigation

·         Present System in Use

·         Flaws In Present System

·         Need Of New System

·         Feasibility Study

·         Project  Category

CHAPTER FOUR: SOFTWARE ENGINEERING AND PARADIGM APPLIED   

·         Modules

·         System / Module Chart

CHAPTER FIVE: SOFTWARE AND HARDWARE REQUIREMENT

CHAPTER SIX: DETAIL SYSTEM ANALYSIS

·         Data Flow Diagram

·         Number of modules and Process Logic

·         Data Structures  and Tables

·         Entity- Relationship Diagram

·         System Design

·         Form Design 

·         Source Code

·         Input Screen and Output Screen

CHAPTER SEVEN: TESTING AND VALIDATION CHECK

CHAPTER EIGHT: SYSTEM SECURITY MEASURES

CHAPTER NINE: IMPLEMENTATION, EVALUATION & MAINTENANCE

CHAPTER TEN: FUTURE SCOPE OF THE PROJECT

CHAPTER ELEVEN: SUGGESTION AND CONCLUSION

CHAPTER TWELE: BIBLIOGRAPHY& REFERENCES          

Other Information

 

PROJECT SOFWARE	ZIP
PROJECT REPORT PAGE	60 -80 Pages
CAN BE USED IN	Marketing (MBA)
PROJECT COST	1500/- Only
PDF SYNOPSIS COST	250/- Only
PPT PROJECT COST	300/- Only
PROJECT WITH SPIRAL BINDING	1750/- Only
PROJECT WITH HARD BINDING	1850/- Only
TOTAL COST (SYNOPSIS, SOFTCOPY, HARDBOOK, and SOFTWARE, PPT)	2500/- Only
DELIVERY TIME	1 OR 2 Days (In case Urgent Call: 8830288685)
SUPPORT / QUERY
CALL	8830288685
[Note: We Provide Hard Binding and Spiral Binding only Nagpur Region]	Download Now