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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.
http://www-db.deis.unibo.it/Mtree/guide.html#tophttp://www-db.deis.unibo.it/Mtree/guide.html#rangehttp://www-db.deis.unibo.it/Mtree/complex.htmlhttp://www-db.deis.unibo.it/Mtree/guide.html#queryhttp://www-db.deis.unibo.it/Mtree/guide.html#basepredhttp://www-db.deis.unibo.it/Mtree/guide.html#andhttp://www-db.deis.unibo.it/Mtree/guide.html#orhttp://www-db.deis.unibo.it/Mtree/guide.html#nothttp://www-db.deis.unibo.it/Mtree/guide.html#predhttp://www-db.deis.unibo.it/Mtree/guide.html#cursorhttp://www-db.deis.unibo.it/Mtree/guide.html#queryhttp://www-db.deis.unibo.it/Mtree/guide.html#basepredhttp://www-db.deis.unibo.it/Mtree/guide.html#mthttp://www-db.deis.unibo.it/Mtree/complex.htmlhttp://www-db.deis.unibo.it/Mtree/guide.html#queryhttp://www-db.deis.unibo.it/Mtree/guide.html#basepredhttp://www-db.deis.unibo.it/Mtree/guide.html#entryhttp://www-db.deis.unibo.it/Mtree/guide.html#queryhttp://www-db.deis.unibo.it/Mtree/guide.html#entryhttp://www-db.deis.unibo.it/Mtree/guide.html#entryhttp://www-db.deis.unibo.it/Mtree/guide.html#objecthttp://www-db.deis.unibo.it/Mtree/guide.html#queryhttp://www-db.deis.unibo.it/Mtree/guide.html#basepredhttp://www-db.deis.unibo.it/Mtree/guide.html#entryhttp://www-db.deis.unibo.it/Mtree/guide.html#entryhttp://www-db.deis.unibo.it/Mtree/guide.html#mthttp://www-db.deis.unibo.it/Mtree/guide.html#tophttp://www-db.deis.unibo.it/Mtree/complex.htmlhttp://www-db.deis.unibo.it/Mtree/guide.html#basepredhttp://www-db.deis.unibo.it/Mtree/guide.html#entryhttp://www-db.deis.unibo.it/Mtree/guide.html#entryhttp://www-db.deis.unibo.it/Mtree/guide.html#basepredhttp://www-db.deis.unibo.it/Mtree/guide.html#entryhttp://www-db.deis.unibo.it/Mtree/guide.html#basepredhttp://www-db.deis.unibo.it/Mtree/guide.html#entryhttp://www-db.deis.unibo.it/Mtree/guide.html#entryhttp://www-db.deis.unibo.it/Mtree/guide.html#basepredhttp://www-db.deis.unibo.it/Mtree/guide.html#entryhttp://www-db.deis.unibo.it/Mtree/guide.html#basepredhttp://www-db.deis.unibo.it/Mtree/guide.html#entryhttp://www-db.deis.unibo.it/Mtree/guide.html#basepredhttp://www-db.deis.unibo.it/Mtree/complex.htmlhttp://www-db.deis.unibo.it/Mtree/guide.html#tophttp://www-db.deis.unibo.it/Mtree/guide.html#mthttp://www-db.deis.unibo.it/Mtree/guide.html#entryhttp://www-db.deis.unibo.it/Mtree/guide.html#entryhttp://www-db.deis.unibo.it/Mtree/guide.html#basepredhttp://www-db.deis.unibo.it/Mtree/guide.html#queryhttp://www-db.deis.unibo.it/Mtree/guide.html#objecthttp://www-db.deis.unibo.it/Mtree/guide.html#entryhttp://www-db.deis.unibo.it/Mtree/guide.html#entryhttp://www-db.deis.unibo.it/Mtree/guide.html#queryhttp://www-db.deis.unibo.it/Mtree/guide.html#entryhttp://www-db.deis.unibo.it/Mtree/guide.html#basepredhttp://www-db.deis.unibo.it/Mtree/guide.html#queryhttp://www-db.deis.unibo.it/Mtree/complex.htmlhttp://www-db.deis.unibo.it/Mtree/guide.html#mthttp://www-db.deis.unibo.it/Mtree/guide.html#basepredhttp://www-db.deis.unibo.it/Mtree/guide.html#queryhttp://www-db.deis.unibo.it/Mtree/guide.html#cursorhttp://www-db.deis.unibo.it/Mtree/guide.html#predhttp://www-db.deis.unibo.it/Mtree/guide.html#nothttp://www-db.deis.unibo.it/Mtree/guide.html#orhttp://www-db.deis.unibo.it/Mtree/guide.html#andhttp://www-db.deis.unibo.it/Mtree/guide.html#basepredhttp://www-db.deis.unibo.it/Mtree/guide.html#queryhttp://www-db.deis.unibo.it/Mtree/complex.htmlhttp://www-db.deis.unibo.it/Mtree/guide.html#rangehttp://www-db.deis.unibo.it/Mtree/guide.html#top7/28/2019 Document (2)mklgbiohl
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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_space7/28/2019 Document (2)mklgbiohl
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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._Newman7/28/2019 Document (2)mklgbiohl
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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.
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
3.4 SOFTWARE REQUIREMENT SPECIFICATION
3.4.1 Purpose
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.
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.
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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.
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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