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1.INTRODUCTIONSpatial objects (e.g., hotels) in reality are associated with multiple quality attributes (e.g.,

price, star), in addition to their spatial locations. Traditional spatial queries and joins (e.g.,

nearest neighbour, closest pair) focus on manipulating only spatial locations and distances,

but they ignore the importance of quality attributes.

The dominance comparison is suitable for comparing two objects with respect to multiple

quality attributes. For the sake of simplicity, we assume that the domain of each quality

attribute is fully ordered (e.g., integer domain). An object A is said to dominate another

object B, if A is no worse than B for all quality attributes and A is better than B for at least

one quality attribute.

The skyline query built upon the dominance comparison, retrieves the objects that are not

dominated by any other. However, the skyline query neglects the significance of spatial

locations.

In practice, spatial data analysts are interested in combining both distance and dominance

comparison to find results satisfying their specific applications. Consider the example that a

hotel chain is planning to open a new hotel in a metropolitan city. The city already has

several existing hotels (as competitors), each associated with its location and quality values.

The new hotel will be built such that its quality values reach the design competence. Among

a predefined set of candidate locations for the new hotel, a candidate location is desired if it is

far away from its nearest existing hotel that dominates its design competence. This way, the

new hotel will not be in a disadvantaged position in business competition with other hotels

within its proximity.

FEASIBILITY STUDY

The feasibility of the project is analyzed in this phase and business proposal is put

forth with a very general plan for the project and some cost estimates. During system analysis

the feasibility study of the proposed system is to be carried out. This is to ensure that the

proposed system is not a burden to the company. For feasibility analysis, some

understanding of the major requirements for the system is essential.


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Three key considerations involved in the feasibility analysis are

ECONOMICAL FEASIBILITY

TECHNICAL FEASIBILITY

SOCIAL FEASIBILITY

ECONOMICAL FEASIBILITY

This study is carried out to check the economic impact that the system will have on

the organization. The amount of fund that the company can pour into the research and

development of the system is limited. The expenditures must be justified. Thus the developed

system as well within the budget and this was achieved because most of the technologies

used are freely available. Only the customized products had to be purchased.

TECHNICAL FEASIBILITY

This study is carried out to check the technical feasibility, that is, the technical

requirements of the system. Any system developed must not have a high demand on the

available technical resources. This will lead to high demands on the available technical

resources. This will lead to high demands being placed on the client. The developed system

must have a modest requirement, as only minimal or null changes are required for

implementing this system.

SOCIAL FEASIBILITY

The aspect of study is to check the level of acceptance of the system by the user. This

includes the process of training the user to use the system efficiently. The user must not feel

threatened by the system, instead must accept it as a necessity. The level of acceptance by the

users solely depends on the methods that are employed to educate the user about the system

and to make him familiar with it. His level of confidence must be raised so that he is also able

to make some constructive criticism, which is welcomed, as he is the final user of the system.

1.1 Motivation

This project can suggests and rank the popular item based on the user selection. This

can be achieved by using the Baseline & Best-First Search Algorithm, ranking rule and

suggesting rules.


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1.2 Problem Definition

We proceed to present the definitions of the nearest dominator (ND) and nearest

dominator distance (ndd) of a location s, as follows.

Definition 1: (Nearest Dominator, Nearest Dominator Distance) given a location s, its quality

vector, and a set of spatial objects P, the nearest dominator of s in P is defined as i.e., the

nearest neighbour of s in P among those that dominate.

1.3 Objective of the Project

The FDL queries are dominated the spatial objects with their attributes to get the

spatial distances and also locations but this is not in an existing application. The skyline

queries problems are solved by the FDL queries and R-tree algorithm. The incremental NN

search algorithm to process the FDL query. We are using the traditional technique of data

mining such as Nearest Neighbours (NN) and Closest Pair for various spatial decisions from

the application.


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2.LITERATURE SURVEYM-tree: An Efficient Access Method for Similarity Search in Metric Spaces

A new access method, called M-tree, is proposed to organize and search large data

sets from a generic metric space, i.e. where object proximity is only defined by a distance

function satisfying the positivity, symmetry, and triangle inequality postulates. We detail

algorithms for insertion of objects and split management, which keep the M-tree always

balanced - several heuristic split alternatives are considered and experimentally evaluated.

Algorithms for similarity (range and k-nearest neighbours) queries are also described. Results

from extensive experimentation with a prototype system are reported, considering as the

performance criteria the number of page I/Os and the number of distance computations. The

results demonstrate that the M-tree indeed extends the domain of applicability beyond the

traditional vector spaces, performs reasonably well in high-dimensional data spaces, and

scales well in case of growing files.

The M-tree can be searched using nearest neighbors and range queries, even in

a complex environment where query predicates are expressed as conjunctions, disjunctions,

and negations ofsimple predicates. Moreover, a sorted access to the tree is also provided,where indexed objects are returned one by one sorted by increasing distance to the query

predicate.

The MT::Range Search method implements the range search algorithm: when called

for the tree with an arbitrarily complex query predicate, it recursively descends the

tree, returning a list containing all entries that are consistent with (i.e. satisfy)

the querypredicate. Returned entries contain the distance between

the entry object and the query predicate as the maximum radius of

the entry (accessible through the MT entry::max radius method).

The k-NN search algorithm is implemented in the MT::Top Search method. Given

a Top Query with an arbitrarily complex predicate, the algorithm returns an array

containing the kentries nearest to the query predicate, sorted by increasing distance.

The distance between each entry and the query predicate is returned as the maximum

radius of each entry.
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The sorted access to a tree with respect to an arbitrarily complex predicate is

performed by creating an instance of the MT cursorclass. Then, it is sufficient to

repeatedly send the Next() message to the so-created MT cursorobject to retrieve,

one-by-one, all the indexed objects sorted in increasing order of distance with respect

to the predicate. Once the user is satisfied with the result, he/she can interrupt the

sorted access by destroying the MT cursorobject.

Scalable Network Distance Browsing in Spatial Databases

An algorithm is presented for finding the knearest neighbours in a spatial network in a

best-first manner using network distance. The algorithm is based on pre-computing the

shortest paths between all possible vertices in the network and then making use of an

encoding that takes advantage of the fact that the shortest paths from vertex u to all of the

remaining vertices can be decomposed into subsets based on the first edges on the shortest

paths to them from u. Thus, in the worst case, the amount of work depends on the number of

objects that are examined and the number of links on the shortest paths to them from q, rather

than depending on the number of vertices in the network. The amount of storage required to

keep track of the subsets is red taking advantage of their spatial coherence which is captured

by the aid of a shortest path quad tree. In particular, experiments on a number of large road

networks as well as a theoretical analysis have shown that the storage has been reduced from

O(N3) to O(N1:5) (i.e., by an order of magnitude equal to the square root). The pre-

computation of the shortest paths along the network essentially decouples the process of

computing shortest paths along the network from that of finding the neighbours, and thereby

also decouples the domain S of the query objects and that of the objects from which the

neighbours are drawn from the domain Vof the vertices of the spatial network. This means

that as long as the spatial network is unchanged, the algorithm and underlying representation

of the shortest paths in the spatial network can be used with different sets of objects.

Distance Browsing in Spatial Databases

Two different techniques of browsing through a collection of spatial objects stored in

an R-tree spatial data structure on the basis of their distances from an arbitrary spatial query

object are compared. The conventional approach is one that makes use of a k-nearest

neighbour algorithm where k is known prior to the invocation of the algorithm. Thus if m#k
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neighbours are needed, the k-nearest neighbour algorithm needs to be re invoked for m

neighbours, thereby possibly performing some redundant computations. The second approach

is incremental in the sense that having obtained the k nearest neighbours, the k +1 nearest

neighbour can be obtained without having to calculate the k +1nearest neighbours from

scratch. The incremental approach finds use when processing complex queries where one of

the conditions involves spatial proximity (e.g., the nearest city to Chicago with population

greater than a million), in which case a query engine can make use of a pipelined strategy. A

general incremental nearest neighbour algorithm is presented that is applicable to a large

class of hierarchical spatial data structures. This algorithm is adapted to the R-tree and its

performance is compared to an existing k-nearest neighbour algorithm for R-trees.

Experiments show that the incremental nearest neighbour algorithm significantly outperforms

the k-nearest neighbour algorithm for distance browsing queries in a spatial database that

uses the R-tree as a spatial index. Moreover, the incremental nearest neighbour algorithm also

usually outperforms the k-nearest neighbour algorithm when applied to the k-nearest

neighbour problem for the R-tree, although the improvement is not nearly as large as for

distance browsing queries. In fact, we prove informally that, at any step in its execution, the

incremental.

On Dominating Your Neighbourhood Profitably

Recent research on skyline queries has attracted much interest in the database and data

mining community. Given a database, an object belongs to the skyline if it cannot be

dominated with respect to the given attributes by any other database object. Current methods

have only considered so-called min/max attributes like price and quality which a user wants

to minimize or maximize. However, objects can also have spatial attributes like x, y

coordinates which can be used to represent relevant constraints on the query results. In this

paper, we introduce novel skyline query types taking into account not only min/max

attributes but also spatial attributes and the relationships between these different attribute

types. Such queries support a micro-economic approach to decision making, considering not

only the quality but also the cost of solutions. We investigate two alternative approaches for

efficient query processing, a symmetrical one based on off-the-shelf index structures, and an

asymmetrical one based on index structures with special purpose extensions. Our

experimental evaluation using a real dataset and various synthetic datasets demonstrates that

the new query types are indeed meaningful and the proposed algorithms are efficient and

scalable.


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Efficient Progressive Skyline Computation

We focus on the retrieval of a set of interesting answers called the skyline from a

database. Given a set of points, the skyline comprises the points that are not dominated by

other points. A point dominates another point if it is as good or better in all dimensions and

better in at least one dimension. We present two novel algorithms, Bitmap and Index, to

compute the skyline of a set of points. Unlike most existing algorithms that require at least

one pass over the dataset to return the first interesting point, our algorithms progressively

return interesting points as they are identified. Our performance study further shows that the

proposed algorithms provide quick initial response time with Index being superior in most

cases.

Nearest Neighbour Queries

A frequently encountered type of query in Geographic Information Systems is to find

the k nearest neighbour objects to a given point in space. Processing such queries requires

substantially different search algorithms than those for location or range queries. In this paper

we present an efficient branch-and-bound R-tree traversal algorithm to find the nearest

neighbour object to a point, and then generalize it to finding the k nearest neighbours. We

also discuss metrics for an optimistic and a pessimistic search ordering strategy as well as for

pruning. Finally, we present the results of several experiments obtained using the

implementation of our algorithm and examine the behaviour of the metrics and the scalability

of the algorithm.

In-Route Skyline Querying for Location-Based Services

With the emergence of an infrastructure for location-aware mobile services, the

processing of advanced, location-based queries that are expected to underlie such services is

gaining in relevance. While much work has assumed that users move in Euclidean space, this

paper assumes that movement is constrained to a road network and that points of interest can

be reached via the network. More specifically, the paper assumes that the queries are issued

by users moving along routes towards destinations. The paper defines in-route nearest-

neighbour skyline queries in this setting and considers their efficient computation. The

queries take into account several spatial preferences, and they intuitively return a set of most

interesting results for each result returned by the corresponding non-skyline queries. The


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paper also covers a performance study of the proposed techniques based on real point-of-

interest and road network data.

Data Mining

Data mining, the extraction of hidden predictive information from large databases, it

is a powerful new technology with great potential to help companies focus on the most

important information in their data warehouses. Data mining tools predict future trends and

behaviours, allowing businesses to make proactive, knowledge-driven decisions. The

automated, prospective analyses offered by data mining move beyond the analyses of past

events provided by retrospective tools typical of decision support systems. Data mining tools

can answer business questions that traditionally were time consuming to resolve. They scour

databases for hidden patterns, finding predictive information that experts may miss because itlies outside their expectations. Data mining software is one of a number of analytical tools for

analyzing data. It allows users to analyze data from many different dimensions or angles,

categorize it, and summarize the relationships identified. Technically, data mining is the

process of finding correlations or patterns among dozens of fields in large relational

databases.

Data mining parameters include:

Association - looking for patterns where one event is connected to another event.

Sequence or path analysis - looking for patterns where one event leads to another later

event.

Classification - looking for new patterns (May result in a change in the way the data is

organized)

Clustering - finding and visually documenting groups of facts not previously known.

Forecasting - discovering patterns in data that can lead to reasonable predictions about

the future.

Professional users commonly require certain security provisions from their paid

content services. This is particularly so in the financial and legal industries. One security

provision is integrity assurance that the content and search results received are correct, and

have not been tampered with. For example, a patent examiner using Micro Patents Web

portal would expect from it the same search results as the up-to-date CD-ROM version.


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Nearest neighbour search

Nearest neighbour search (NNS)[4], also known as proximity search, similarity

search or closest point search, is an optimization problem for finding closest points in metric

spaces. The problem is: given a set Sof points in a metric spaceMand a query point qM,

find the closest point in Sto q. In many cases,Mis taken to be d-dimensional Euclidean

space and distance is measured by Euclidean distance orManhattan distance.

Various solutions to the NNS problem have been proposed. The quality and

usefulness of the algorithms are determined by the time complexity of queries as well as the

space complexity of any search data structures that must be maintained.

Linear search

The simplest solution to the NNS problem is to compute the distance from the query

point to every other point in the database, keeping track of the "best so far".

There are numerous variants of the NNS problem and the two most well-known are

thek-nearest neighbour search and the-approximate nearest neighbour search

Linear Search Problem

An immobile hider is located on the real line according to a known probability

distribution. A searcher, whose maximal velocity is one, starts from the origin and wishes to

discover the hider in minimal expected time. It is assumed that the searcher can change the

direction of his motion without any loss of time. It is also assumed that the searcher cannot

see the hider until he actually reaches the point at which the hider is located and the time

elapsed until this moment is the duration of the game." It is obvious that in order to find the

hider the searcher has to go a distance x1 in one direction, return to the origin and go distance

x2 in the other direction etc., (the length of the n-th step being denoted by xn), and to do it in

an optimal way. (However, an optimal solution need not have a first step and could start with

an infinite number of small 'oscillations'.) This problem is usually called the linear search

problem and a search plan is called a trajectory. It has attracted much research, some of it

quite recent. (Especially Beck)

The linear search problem for a general probability distribution is unsolved yet.

However, there exists a dynamic programming algorithm that produces a solution for any

discrete distribution[5]and also an approximate solution, for any probability distribution, with

any desired accuracy.[6]
http://en.wikipedia.org/wiki/Metric_spacehttp://en.wikipedia.org/wiki/Metric_spacehttp://en.wikipedia.org/wiki/Euclidean_spacehttp://en.wikipedia.org/wiki/Euclidean_spacehttp://en.wikipedia.org/wiki/Euclidean_distancehttp://en.wikipedia.org/wiki/Taxicab_geometryhttp://en.wikipedia.org/wiki/K-nearest_neighbor_algorithmhttp://en.wikipedia.org/wiki/K-nearest_neighbor_algorithmhttp://en.wikipedia.org/wiki/K-nearest_neighbor_algorithmhttp://en.wikipedia.org/wiki/%CE%95-approximate_nearest_neighbor_searchhttp://en.wikipedia.org/wiki/%CE%95-approximate_nearest_neighbor_searchhttp://en.wikipedia.org/wiki/%CE%95-approximate_nearest_neighbor_searchhttp://en.wikipedia.org/wiki/Dynamic_programminghttp://en.wikipedia.org/wiki/Linear_search_problem#cite_note-4http://en.wikipedia.org/wiki/Linear_search_problem#cite_note-4http://en.wikipedia.org/wiki/Linear_search_problem#cite_note-4http://en.wikipedia.org/wiki/Linear_search_problem#cite_note-5http://en.wikipedia.org/wiki/Linear_search_problem#cite_note-5http://en.wikipedia.org/wiki/Linear_search_problem#cite_note-5http://en.wikipedia.org/wiki/Linear_search_problem#cite_note-5http://en.wikipedia.org/wiki/Linear_search_problem#cite_note-4http://en.wikipedia.org/wiki/Dynamic_programminghttp://en.wikipedia.org/wiki/%CE%95-approximate_nearest_neighbor_searchhttp://en.wikipedia.org/wiki/K-nearest_neighbor_algorithmhttp://en.wikipedia.org/wiki/Taxicab_geometryhttp://en.wikipedia.org/wiki/Euclidean_distancehttp://en.wikipedia.org/wiki/Euclidean_spacehttp://en.wikipedia.org/wiki/Euclidean_spacehttp://en.wikipedia.org/wiki/Metric_spacehttp://en.wikipedia.org/wiki/Metric_space


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The linear search problem was solved by Anatole Beck and Donald J. Newman (1970) as a

two-person zero-sum game. Theirmini max trajectory is to double the distance on each step

and the optimal strategy is a mixture of trajectories that increase the distance by some fixed

constant[7]. This solution gives search strategies that are not sensitive to assumptions

concerning the distribution of the target. Thus, it also presents an upper bound for a worst

case scenario. This solution was obtained in the framework of an online algorithmby Shmuel

Gal.[8]. The best online competitive ratio is 9 but it can be reduced to 4.6 by using a

randomized strategy.

K-nearest neighbor

K-nearest neighbour search identifies the top k-nearest neighbours to the query. This

technique is commonly used in predictive analytics to estimate or classify a point based on

the consensus of its neighbours.K-nearestneighbour graphs are graphs in which every point

is connected to its - nearest neighbours.
http://en.wikipedia.org/wiki/Donald_J._Newmanhttp://en.wikipedia.org/wiki/Minimaxhttp://en.wikipedia.org/wiki/Linear_search_problem#cite_note-6http://en.wikipedia.org/wiki/Linear_search_problem#cite_note-6http://en.wikipedia.org/wiki/Linear_search_problem#cite_note-6http://en.wikipedia.org/wiki/Online_algorithmhttp://en.wikipedia.org/wiki/Shmuel_Galhttp://en.wikipedia.org/wiki/Shmuel_Galhttp://en.wikipedia.org/wiki/Linear_search_problem#cite_note-7http://en.wikipedia.org/wiki/Linear_search_problem#cite_note-7http://en.wikipedia.org/wiki/Linear_search_problem#cite_note-7http://en.wikipedia.org/wiki/Competitive_ratiohttp://en.wikipedia.org/wiki/K-nearest_neighbor_algorithmhttp://en.wikipedia.org/wiki/K-nearest_neighbor_algorithmhttp://en.wikipedia.org/wiki/Competitive_ratiohttp://en.wikipedia.org/wiki/Linear_search_problem#cite_note-7http://en.wikipedia.org/wiki/Shmuel_Galhttp://en.wikipedia.org/wiki/Shmuel_Galhttp://en.wikipedia.org/wiki/Online_algorithmhttp://en.wikipedia.org/wiki/Linear_search_problem#cite_note-6http://en.wikipedia.org/wiki/Minimaxhttp://en.wikipedia.org/wiki/Donald_J._Newman


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3.ANALYSIS3.1 Introduction

The dominance comparison is suitable for comparing two objects with respect to

multiple quality attributes. For the sake of simplicity, The domain of each quality attribute is

fully ordered (e.g., integer domain). An object A is said to dominate another object B, if A is

no worse than B for all quality attributes and A is better than B for at least one quality

attribute. The skyline query built upon the dominance comparison, retrieves the objects that

are not dominated by any other. However, the skyline query neglects the significance of

spatial locations. spatial data analysts are interested in combining both distance and

dominance comparison to find results satisfying their specific applications. Consider theexample that a hotel chain is planning to open a new hotel in a metropolitan city.

The city already has several existing hotels (as competitors), each associated with its

location and quality values. The new hotel will be built such that its quality values reach the

design competence. Among a predefined set of candidate locations for the new hotel, a

candidate location is desired if it is far away from its nearest existing hotel that dominates its

design competence. This way, the new hotel will not be in a disadvantaged position in

business competition with other hotels within its proximity.

3.2 Existing System

The Existing System has a skyline query are also not helpful here. Let dist(s, h)

denotes the Euclidean distance between a location(s) and a hotel (h). The queries are not

enough to complete the task of the application because the skyline queries are used to get the

nearest datasets only. We combine both spatial locations and quality attributes to define a

skyline query that not retrieves practically meaningful locations as expected.

Disadvantages

The system introduced the spatial queries.

The queries were not implanted the NN search algorithm for collecting the spatial

location of spatial objects.

This is not sufficient to know the ND (Nearest Dominators).


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3.3 Proposed System

This paper is the first to formulate the FDL query that captures practical needs

involving not only spatial locations but also quality attributes. Second, we adapt the

incremental NN search algorithm to process the FDL query. This makes it possible to find the

FDL on legacy implementations without much additional investment. Third, we design

specific and more efficient methods for the FDL query. Fourth, we conduct a thorough

theoretic analysis on the performance of proposed methods. Fifth, we generalize our

proposals to deal with the generic distance metric and other interesting query types. Finally,

we conduct an extensive experimental study for the proposed methods on both real and

synthetic datasets, and show that our best algorithm is indeed efficient and scalable.

Advantages

We are using the traditional technique of data mining such as Nearest Neighbours

(NN) and Closest Pair for various spatial decisions from the application.


spatial distances and also locations but this is not in an existing application.

The skyline queries problems are solved by the FDL queries and R-tree algorithm

3.4 SOFTWARE REQUIREMENT SPECIFICATION

3.4.1 Purpose


spatial distances and also locations but this is not in an existing application. The skyline

queries problems are solved by the FDL queries and R-tree algorithm.

3.4.1.2 Document convention:

Bond paper should be used for the preparation of the Thesis. Typing should be done

on the 12 point size letters for the running text, 14 point size for the sub-headings and 16

point size for main headings /titles/names/etc. The font should be preferably TIMES NEW

ROMAN.


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3.4.1.3 Intended Audience and Reading suggestion:

The purpose of this system is to incremental NN search algorithm to process the FDL

query. This makes it possible to find the FDL on legacy implementations without much

additional investment.

3.4.1.4 Scope:

The main goal of this work is to formulate the FDL query that captures practical

needs involving not only spatial locations but also quality attributes. Second, we adapt the

incremental NN search algorithm to process the FDL query. This makes it possible to find the

FDL on legacy implementations without much additional investment. Third, we design

specific and more efficient methods for the FDL query.

3.4.2. Overall Description:

3.4.2.1 Product perspective:

We conduct a thorough theoretic analysis on the performance of proposed methods.

The generalize proposals to deal with the generic distance metric and other interesting query

types.

3.4.2.2. Product Features

The system is to provide authentication for spatial queries and to retrieve information

from server with the help of Nearest Neighbours method using Authenticated Data Structures

(ADS). The design authenticates multi-step frame work which verifies query results

efficiently for expensive functions and high dimensionality data.

3.4.2.3 User class and Characteristics:

The purpose of this system is to an extensive experimental study for the proposed

methods on both real and synthetic datasets, and show that our best algorithm is indeed

efficient and scalable.

3.4.3. Operating Environment

The system is designed to be the cross platform supportable. The system is supported

on a wide range of hardware and any software platform. The system is implemented in web

environment using .Net framework. The System shall operate with the following Web


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browsers: Microsoft Internet Explorer versions 5.0 and 6.0, Netscape Communicator version

4.7, and Netscape versions 6 and 7 and Mozilla Firefox. The System shall operate on a server

running the current corporate approved versions of Red Hat Linux and Apache Web Server.

The System shall permit user access from the corporate Intranet and, if a user is

authorized for outside access through the corporate firewall, from an Internet connection at

the users home.

3.4.3.1. Design and Implementation Constrains

The system shall be built using a standard web page development tool that conforms to

Microsofts GUI standards.

There are no memory requirements.

The computers must be equipped with web browsers such as Internet explorer.

The product must be stored in such a way that allows the client easy access to it.

Response time for loading the product should take no longer than five minutes.

A general knowledge of basic computer skills is required to use the product.

3.4.3.2 Functional Requirements

User Registration

User Authentication and Login

Admin Login

Search for information using keyword

Get response from server

Get contact

Feedback

3.4.3.3. Non Functional Requirements

The major non-functional Requirements of the system are as follows

1. Usability

It is the ease of use and learns ability of a human-made object. The object of use can

be a software application, website, book, tool, machine, process, or anything a human

interacts with.
http://en.wikipedia.org/wiki/Software_applicationhttp://en.wikipedia.org/wiki/Toolhttp://en.wikipedia.org/wiki/Machinehttp://en.wikipedia.org/wiki/Machinehttp://en.wikipedia.org/wiki/Toolhttp://en.wikipedia.org/wiki/Software_application


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2. Reliability

The system is more reliable because of the qualities that are inherited from the chosen

Framework .Net. The code built by using this is more reliable.

3. Performance

This system is developing in the high level languages and using the advanced front-

end and back-end technologies it will give response to the end user on client system with in

very less time.

4. Supportability

The system is designed to be the cross platform supportable. The system is supported

on a wide range of hardware and any software platform, which is having Multi Language

support, built into the system.

5. Accessibility

It is the degree to which a product, device, service, or environment is available to as

many people as possible. Accessibility can be viewed as the "ability to access" and benefit

from some system or entity.

6. Maintainability

It is the ease with which a product can be maintained in order to:

Isolate defects or their cause

Correct defects or their cause

Meet new requirements

Make future maintenance easier

Cope with a changed environment

7. Testability

It is the degree to which a software artifact (i.e. a software system, software module,

requirements- or design document) supports testing in a given test context.

8. Scalability

Itis the ability of a system, network, or process, to handle a growing amount of work

in a capable manner or its ability to be enlarged to accommodate that growth.
http://en.wikipedia.org/wiki/Requirementshttp://en.wikipedia.org/wiki/Requirements


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3.4.4 Operating environment:

Hardware Requirements

SYSTEM : Pentium IV 2.4 GHz

HARD DISK : 40 GB

RAM : 256 MB

Software Requirements:

Operating system : Windows 7/ XP Professional

Front End : Microsoft Visual Studio .Net 2008

Coding Language : C#

Database : SQL SERVER 2005

3.4.5. External Interface Requirements:

3.4.5.1 User Interface

The user interface for the software shall be compatible to any browser such as Internet

Explorer, Mozilla or Netscape Navigator by which user can access to the system. The user

interface has implemented using software Microsoft .Net package.

3.4.5.2 Hardware Interfaces

Since the application must run over the internet, all the hardware shall require to

connect internet will be hardware interface for the system. As for e.g. Modem, WAN LAN,

Ethernet Cross-Cable.

3.4.5.3 Software Interfaces

1.

The system shall communicate with the User to provide the information to the given

query.

2. The System shall communicate with the Administrator to update the information.

3. The System shall communicate with the Administrator to update the false hits

information in the database.

4. The system shall be VeriSign like software which shall allow the users to complete

secured transaction. This usually shall be the third party software system which iswidely used for internet transaction.


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3.4.6. Other requirements

Appendix A: Analysis Models

Use Case Diagrams, Class diagram, Activity Diagrams, Sequence Diagrams,

Collaboration Diagrams will be provided which describes the flow of data between various

processes of the system.


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4.DESIGN4.1 Introduction

Unified Modelling Language (UML) is a standard language for software blue prints.

A modelling language is a language whose vocabulary and rules focus ion the conceptual and

physical representation of a system.

4.2 UML Diagrams

Fig: 4.2.1 Use case diagram for system


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Fig: 4.2.2 Class Diagram for system


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Fig: 4.2.3 Object diagram for system


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Fig: 4.2.4 State Chart Diagram for system


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Fig: 4.2.5 Activity diagram for system


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Fig: 4.2.6 Sequence diagram for system


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Fig: 4.2.7 Component diagram for system


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Fig: 4.2.8 Deployment diagram for system


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E-R Diagram

Login

Admin

User name Password

User

New user

registration Login

Search ItemCategory

List the popular

item

Buy the popular

item

Select item

Item

details


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System Architecture

Admin User

Login

Select the Item

Update the item

Select the

Category

Enter the Query

View the popular

item

Feedback for the

product

Buy the popular

item

Logout


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5. IMPLEMENTATION

5.1 MODULES

Admin

Login

New User Registration

Search Result

Contact

Feedback

Modules Description

Admin

In this module,Admin have rights to update product details in the application. Admin

can delete the product if it is not required.

Login

User has to give their username and password. If the username and password is valid

then that user can be permit to access this web application. This module will allow only

registered users to access.

Registration

New user cant enter the application directly. They want to register here to use this

application. User wants to provide all the required details in registration form. Registered

user name and password is considered as valid.

Search Result

In this module User choose their preferred category to search immediately. Searched

product can be ranked based on the count of the product. The count of the product can be

increased by the number user can searched that item and how many of them bought that item.

Contact


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In this moduleused for get the contact information about the admin.

Feedback

User can enter the feedback about the auction website. User details, time, feedback

has been monitoring by admin and visible to all.

Module Diagram:

Login

Admin User

Update the item

details

Select the

cate or

Enter the query

View the

product

Feed back for

the roduct

Logout


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5.2 Introduction to the Technology Used

FEATURES OF. NET

Microsoft .NET is a set of Microsoft software technologies for rapidly building and

integrating XML Web services, Microsoft Windows-based applications, and Web solutions.

The .NET Framework is a language-neutral platform for writing programs that can easily and

securely interoperate. Theres no language barrier with .NET: there are numerous languages

available to the developer including Managed C++, C#, Visual Basic and Java Script. The

.NET framework provides the foundation for components to interact seamlessly, whether

locally or remotely on different platforms. It standardizes common data types and

communications protocols so that components created in different languages can easily

interoperate.

.NET is also the collective name given to various software components built upon

the .NET platform. These will be both products (Visual Studio.NET and Windows.NET

Server, for instance) and services (like Passport, .NET My Services, and so on).

5.3.1. Features of .NET

Garbage collection relieves the programmer of the burden of manual memory

management.

Variables in C# are automatically initialized by the environment.

Managed execution environment

Variables are type-safe.

Built in versioning

Native support for the Component Object Model (COM) and Windows-based

APIs.

Restricted use of native pointers

With C#, every object is automatically a COM object

Platform and language independent

Inside a specially marked code block, developers are allowed to use pointers and

traditional C/C++ features such as manually managed memory and pointer

arithmetic.

Compiler allows use of initialised Variables only


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Strong exception handling

Full XML support

Suited well for building Web Services

Array bounds checking The language is intended for use in developing software components suitable for

deployment in distributed environments.

C# is a modern, object-oriented language that enables programmers builds solutions

for the Microsoft .NET platform. The framework provided allows C# components to

become XML Web services that are available across the Internet, from any

application running on any platform.

SQL-SERVER

1. T-SQL (Transaction SQL) enhancementsT-SQL is the native set-based RDBMS programming language offering high

performance data access.

2. CLR (Common Language Runtime)The next major enhancement in SQL Server 2005 is the integration of a .NET

compliant language such as C#, ASP.NET or VB.NET to build objects (stored

procedures, triggers, functions, etc.). This enables you to execute .NET code.

3. Service BrokerThe Service Broker handles messaging between a sender and receiver in a loosely

coupled manner. A message is sent, processed and responded to, completing the

transaction.

4. Data encryptionSQL Server 2005 has native capabilities to support encryption of data stored in user-

defined databases.5. SMTP mail

With SQL Server 2005, Microsoft incorporates SMTP mail to improve the native mail

capabilities. Say "see-ya" to Outlook on SQL Server!

6. HTTP endpointsYou can easily create HTTP endpoints via a simple T-SQL statement exposing an

object that can be accessed over the Internet. This allows a simple object to be called

across the Internet for the needed data.


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7. Multiple Active Result Sets (MARS)MARS allow a persistent database connection from a single client to have more than

one active request per connection.

8. Dedicated administrator connectionIf all else fails, stop the SQL Server service or push the power button. That mentality

is finished with the dedicated administrator connection. This functionality will allow a

DBA to make a single diagnostic connection to SQL Server even if the server is

having an issue.

9. SQL Server Integration Services (SSIS)SSIS has replaced DTS (Data Transformation Services) as the primary ETL

(Extraction, Transformation and Loading) tool and ships with SQL Server free of

charge.

10.Database mirroringDatabase mirroring is an extension of the native high-availability capabilities.


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5.3 Sample Code

using System;using System.Collections;using System.Configuration;using System.Data;

using System.Linq;using System.Web;using System.Web.Security;using System.Web.UI;using System.Web.UI.HtmlControls;using System.Web.UI.WebControls;using System.Web.UI.WebControls.WebParts;using System.Xml.Linq;using System.Data.SqlClient;

publicpartialclassBuying : System.Web.UI.Page{

SqlConnection con = newSqlConnection("Data Source=SPIRO20;InitialCatalog=raja;Integrated Security=True");

string name = "";string p_type = string.Empty;string city;int budgetmin = 0;

protectedvoid Page_Load(object sender, EventArgs e){

name= Session["UserName"].ToString();con.Open();SqlCommand cmd = newSqlCommand("select Propertytype,City,Budgetmin

from seller where User_Name='" + name + "'", con);

SqlDataReader reader = cmd.ExecuteReader();if (reader.HasRows){

if (reader.Read() == true){

p_type = reader[0].ToString();city = reader[1].ToString();budgetmin = Convert.ToInt32(reader[2].ToString());

}

}reader.Dispose();reader.Close();

SqlDataAdapter ad = newSqlDataAdapter("select DISTINCTb.Propertytype,b.Address,(b.Areafrom)as Needed_Area,b.budgetmin asMin_Budget,b.budgetmax as Max_Budget,b.Phone,b.Email from buyer b, seller swhere b.budgetmin


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// if (GridView1.Rows.Count > 1)// GridView1.Rows.Remove(dr);//}

// Response.Redirect("Buyer.aspx");}

protectedvoid Button1_Click(object sender, EventArgs e)

{

}

protectedvoid GridView1_SelectedIndexChanged1( object sender, EventArgse)

{

}

protectedvoid Button2_Click(object sender, EventArgs e){

Response.Redirect("Mail.aspx");}protectedvoid Button3_Click(object sender, EventArgs e){

}}


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6. TESTING

6.1 Introduction

Testing is an effective method for detecting errors. Once code has been generated,program testing begins. The testing process focuses on the logical internals of the software,

ensuring that all statements have been tested, and on the functional externals i.e., conducting

tests to uncover errors and ensures that defined input will produce actual results that agree

with required results.

6.2 TEST CASES

Test Case id Test case

description

Expected

Output

Actual Output Status

1 If

name!=admin

Should show

error

Error: verify username

is displayed

Success

2 If

name=admin

Successful

login

Should display the

admin Home page

Success

3 Entered valid

login id and

password

Login should

be successful

and client

enters intomain home

page

Login successful and

client enters into main

home page

Success

4 Entered

invalid id or

password

Login should

be failed with

an error

message

Login failed. Error

message not appeared

Fail

5 User id and

Password

checked

A message

Invalid user

id/password

will be

displayed.

Enter into the

application

A message Valid

userid/password will

be displayed Enter into

the application.

Fail


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6.3 Screen Shots:

6.3.1Loginpage

Shows the login page where users enter their details like username and password. After they

prefer whether they entering to member login or admin login. If user enters correct details it

will authenticate.


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6.3.2 Signup:

The signup page list out all the details like user name, user login, password, address, phone

number for the user to sign-in.


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6.3.3 Buyer:

The buyer page list out about all the location details what the buyer needs like property type,

country, state, address, email, contact, covered area, budget range.


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6.3.4 Buying:

The buying page provides the user requirements of what user needs where the user mentioned

in buyer list.


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6.3.5 Mail:

The page mainly consists of from address i.e.; buyer address to the address sent i.e.; user

address as if the buyer interested to buy the property.


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6.3.6 Mail Result:

The page displays the mail sent to the user.


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6.3.7 Seller:

This page list out the details of what type of property the user wants to sell.


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6.3.8 Selling:

It mainly list out the selling properties.


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6.3.9 Contact:

It provides the contact information of admin.


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6.3.10 Feedback:

User enters the feedback about auction website.


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6.3.11 Admin:

The admin page list out the name of password for login..


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6.3.12 Admin Updating:

Admin updates the details in administration.


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6.3.13 Buyer list:

This page displays the entire buyer list.


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6.3.14 Seller List:

This page displays entire seller list.


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7. Conclusion

A novel complex type of query: farthest dominated location (FDL) query. Given a set

of (competitors) spatial objectsPwith both spatial locations and non-spatial attributes, a set

of (candidate) locationsL, and a design competence vector (forL), a FDL query retrieves

the locations L such that the distance to its nearest dominating object in Pis maximized.

Although FDL queries are suitable for various spatial decision making applications, they are

not solved by any of the existing techniques. We develop several efficient R-tree based

algorithms for processing FDL queries, which offer users a range of selections in terms of

different indexes available on the data. We also generalize our proposals to support the

generic distance metric and other interesting query types. We conduct an extensive

experimental study with various settings on both real and synthetic datasets. The results

disclose the performance of our proposals, and identify our spatial joint based algorithm

(SJB) as the most efficient and scalable query processing algorithm.


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8. BIBILOGRAPHY

[1]. T. Brinkhoff, H.-P. Krieger and B. Seeger. Efficient Processing of Spatial Joins Using

R-Trees. In Proc. SIGMOD, pages 237246, 1993.

[2]. P. Ciaccia, M. Patella, and P. Zezula. M-tree: An efficient access method for

similarity search in metric spaces. In Proc. VLDB, pages 426435, 1997.

[3]. G. Hjaltason and H. Samet. Distance browsing in spatial database. ACM TODS,

24(2):265318, 1999.

[4]. X. Huang and C. S. Jensen. In-route skyline querying for location based services. In

Proc. W2GIS, pages 120135, 2004.

[5]. K. L. Tan, P. K. Eng, and B. C. Ooi. Efficient progressive skyline computation. In

Proc. VLDB, pages 301310, 2001.

[6]. B. Zheng, K. C. K. Lee, and W.-C. Lee. Location-dependent skyline query. In Proc.

MDM, pages 148155, 2008.

[7]. Evgeny Milanov, The RSA Algorithm, 2009.

[8]. United States National Security Agency , The SHA-1 Algorithm,NIST, 2009.

[9].www.google.com

[10].www.wikipidia.com

[11].Data Mining concepts: Jiawei Han and Micheline Kamber

[12].The Unified Modeling Language User Guide: By Grady Booch.
http://en.wikipedia.org/wiki/National_Security_Agencyhttp://en.wikipedia.org/wiki/National_Institute_of_Standards_and_Technologyhttp://en.wikipedia.org/wiki/National_Institute_of_Standards_and_Technologyhttp://en.wikipedia.org/wiki/National_Institute_of_Standards_and_Technologyhttp://en.wikipedia.org/wiki/National_Security_Agency

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