On Skyline Groups
The novel problem of finding the skyline k-tuple groups from an n-tuple data set—i.e.,
groups of k tuples which are not dominated by any other group of equal size, based on
aggregate-based group dominance relationship. The major technical challenge is to
identify effective anti-monotonic properties for pruning the search space of skyline
groups. To this end, we first show that the anti-monotonic property in the well-known
Apriori algorithm does not hold for skyline group pruning. Then, we identify two antimonotonic properties with varying degrees of applicability: order-specific property
which applies to SUM, MIN, and MAX as well as weak candidate-generation
property which applies to MIN and MAX only. Experimental results on both real and
synthetic data sets verify that the proposed algorithms achieve orders of magnitude
performance gain over the baseline method.
In the Existing System there is no individuality and equality among the domains.No
use of aggregate functions .
The traditional skyline tuple problem has been
extensively investigated in recent years Consider a database table of n tuples and
m numeric attributes. The domain of each attribute has an application specific
preference order, with “better” values being preferred over “worse” values. A
tuple t1 dominates t2 if and only if every attribute value of t1 is either better than
or equal to the corresponding value of t2 according to the preference order and t1
has better value on at least one attribute. The set of skyline tuples are those
tuples that are not dominated by any other tuples in the table
In the Proposed System the main theme is to increase the output of the domain
which is present in the skyline groups. Skyline group nothing but it is a data
base stores
information about some particular domains these domains will
considered as tuples. Each tuple can maintain individuality .No domain can
never dominate another. In this novel model by using aggregate functions easily
will determine the output of the particular domain whether the output is
minimized or maximized. Here five aggregate functions used such as sum, avg ,
max, min, count . In skyline groups all the domains should follow the antimonotonic properties .
• Maximizing the final outcome of the particular domain and maintain the equality
among the groups. increasing the performance of domain.
1. In this On Skyline Groups project the domainas will stored in a data base, and the
domains should maintain individuality and equality.
The problem of subspace skyline analysis. Skylines in subspaces can be concisely
summarized by skyline groups. Skylines in subspaces can be concisely summarized
by skyline groups. The representatives in skyline groups. They catch the “contour” of the
The theoretical intractability, finding all skyline groups matching a MAX skyline vector
v is usually efficient in practice. This is mainly because the number of tipples that “hit”
the MAX attribute values in v is typically small. As such, even a brute-force enumeration
can be efficient, as demonstrated by experimental results in Nonetheless, when a large
number of tuples “hit” the MAX attribute values, it is unclear how one can efficiently
find and store all skyline groups—e.g., by using certain efficient indexing schemes. We
leave the design of such indexing schemes as an open problem for future research.
Number of Modules
After careful analysis the system has been identified to have the following modules:
1. Skyline Queries
2. Skyline Groups
3. Dynamic Programming Algorithm
Based on Order-Specific Property
1. Skyline Queries:
Sky line is set of tuples of information. Skyline is defind as those points which
are not dominated by any other point. A point dominates another point if it is
good or better in all dimension and better in atleast one dimension. Skyline
queries are a popular way to obtain preferred answers from the database by
providing only the order ings of attribute values.
2. Skyline Groups:
A multi-dimensional dataset of tuples skyline computation returns a subset of
tuples that are not dominated by any other when all domains are considered
together Conventional skyline computation, however, is inadequate to answer
various queries that need to analyze not just individual tuples of a dataset but
also their combinations. we study group skyline computation which is based on
the notion of dominance relation between groups of the same number of tuples.
It determines the dominance relation between two groups by comparing their
aggregate values such as sums or averages of elements of individual dimensions,
and identifies a set of skyline groups that are not dominated by any other
3. Dynamic Programming Algorithm:
Dynamic programming is a method for solving complex problems by breaking
them down into simpler subproblems. It is applicable to problems exhibiting the
properties of overlapping sub problems and optimal substructure .The idea
behind dynamic programming is quite simple. In general to solve a given
problem, we need to solve different parts of the problem then combine the
solutions of the subproblems to reach an overall solution. The dynamic
programming approach seeks to solve each subproblem only once, thus reducing
the number of computations: once the solution to a given subproblem has been
computed, it is stored .The next time the same solution is needed, it is simply
looked up. This approach is especially useful when the number of repeating
subproblems grows exponentially as a function of the size of the input. Dynamic
programming algorithms are used for optimization.
Operating System
: Windows
: Java and J2EE
Web Technologies
: Html, JavaScript, CSS
: My Eclipse
Web Server
: Tomcat
Tool kit
: Android Phone
: My SQL
Java Version
: J2SDK1.5
1.1 GHz
Hard Disk
20 GB
Floppy Drive
1.44 MB
Key Board
Standard Windows Keyboard
Two or Three Button Mouse
We proposed the novel problem of finding skyline groups which lends itself to
many real-world applications. We developed novel algorithmic techniques on
output compression, input pruning, and search space pruning to address the
problem. For search space pruning, we identified a number of anti-monotonic
properties to efficiently remove non-skyline groups from consideration. Based on
the properties, we developed dynamic programming and iterative algorithms for
skyline group search.Experimental results on real and synthetic data sets verify
that the proposed algorithms achieve orders of magnitude performance gain
over the baseline method.