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Query Planning for Continuous Aggregation Queries over a Network of Data Aggregators.

Platform : DATA MINING

IEEE Projects Years : 2012

Query Planning for Continuous Aggregation Queries over a Network of Data Aggregators.

ABSTRACT:
Continuous queries are used to monitor changes to time
varying data and to provide results useful for online decision making.
Typically a user desires to obtain the value of some aggregation function
over distributed data items, for example, to know value of portfolio for a
client; or the AVG of temperatures sensed by a set of sensors. In these
queries a client specifies a coherency requirement as part of the query.
We present a low-cost, scalable technique to answer continuous
aggregation queries using a network of aggregators of dynamic data
items. In such a network of data aggregators, each data aggregator
serves a set of data items at specific coherencies. Just as various
fragments of a dynamic web-page are served by one or more nodes of a
content distribution network, our technique involves decomposing a
client query into sub-queries and executing sub-queries on judiciously
chosen data aggregators with their individual sub-query incoherency
bounds. We provide a technique for getting the optimal set of sub-queries
with their incoherency bounds which satisfies client query’s coherency
requirement with least number of refresh messages sent from
aggregators to the client. For estimating the number of refresh messages,
we build a query cost model which can be used to estimate the number
of messages required to satisfy the client specified incoherency bound.
Performance results using real-world traces show that our cost based
query planning leads to queries being executed using less than one third
the number of messages required by existing schemes.
The Greedy algorithm for query plan selection
Result  Ø
While A ≠ Ø
Choose a sub-query a Ñ” A with criteria ψ
Result Result υ a
A  A-{a}
For each data element e Ñ” a
For each b Ñ” A
b  b-{e}
If b = Ø
A  A-{b}
Else
Calculate sumdiff for modified b
Return Result
Existing System:
Many data intensive applications delivered over the Web
suffer from performance and scalability issues. Content distribution
networks (CDNs) solved the problem for static content using caches at
the edge nodes of the networks. CDNs continue to evolve to serve more
and more dynamic applications. The static fragments are served from the
local caches whereas dynamic fragments are created either by using the
cached data or by fetching the data items from the origin data sources.
One important question for satisfying client requests through a network
of nodes is how to select the best node(s) to satisfy the request. For static
pages content requested, proximity to the client and load on the nodes
are the parameters generally used to select the appropriate node.
Disadvantage:
1. For data item which needs to be refreshed at an incoherency.
2. The exact data value at the corresponding data source need not be
reported as long as the query result satisfies user specified
accuracy requirements.
Proposed System:
Continuous queries are used to monitor changes to time varying
data and to provide results useful for online decision making. This paper
we present a low-cost, scalable technique to answer continuous
aggregation queries using a content distribution network of dynamic data
items.
Advantage:
1. It saves the time and the user spending low cost.
2. A continuous query cost model which can be used to estimate the
number of messages required to satisfy the client specified
incoherency bound.
3. We present to implementations of Continuous Aggregation in
optimized query.
MODULES
1. Security Module
2. Flow chat Module
3. Update Module
4. Client/Server Module
Security Module:
This module is used to help the user to provide the
security of access. Because once the user to logout or leave
our account automatically user password is changed and
server to send the password in our mail ID. Whenever the user
to logout the account automatically the security key is
changed based on the random function.
Flow Chat Module:
This module is used to help the user to view the BSE and
NSE value in bar flow chat based on the date. This chat to
display the aggregated value based on the companies sharing
values continuously. The companies value is changed
automatically chat value is changed.
Update Module:
This module is used to help the user to view the BSE and
NSE value to update in every minute. So the user waiting time
is reduced and sees the updated value in every minute. The
server to set the time when our form is updated.
Client/Server Module:
This module is used to help the client and server
interaction to the database. This module is used to
dynamically create the table based on the server entering
value. These values are assigning to the chat x and y position
and display the client. These values are changed in
dynamically based on the server entering values.
SYSTEM SPECIFICATION
Hardware Requirements:
• System : Pentium IV 2.4 GHz.
• Hard Disk : 40 GB.
• Floppy Drive : 1.44 Mb.
• Monitor : 14’ Colour Monitor.
• Mouse : Optical Mouse.
• Ram : 512 Mb.
• Keyboard : 101 Keyboard.
Software Requirements:
• Operating system : Windows XP.
• Coding Language : ASP.Net with C#
• Data Base : SQL Server 2005.


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