A Generic Query Model for the Unified
Discovery of Heterogeneous Services
ABSTRACT:
Peer to peer network is the machine, which works as a client and also a
server in a connected network. In this project Query processing is used
to get the average, sum, minimum, maximum, count is the few aggregate
rules. For example, In an organization has the different databases in the
different countries. If you want to get the average salary for all the
employees who they getting above 10,000 rupees then you have to find
the average for each database and collect the all averages and find the
solution average. This process will find the average from the database
randomly. This project is fully based upon the aggregate rules.
Peer-to-peer (P2P) databases are becoming prevalent on the
Internet for distribution and sharing of documents, applications, and
other digital media. The problem of answering large-scale ad hoc
analysis queries, for example, aggregation queries, on these databases
poses unique challenges. Exact solutions can be time consuming and
difficult to implement, given the distributed and dynamic nature of P2P
databases. We present novel sampling-based techniques for range
answering of ad-hoc aggregation queries in such databases.
Computing a high-quality random sample of the database
efficiently in the P2P environment is complicated due to several factors:
the data is distributed (usually in uneven quantities) across many peers,
within each peer, the data is often highly correlated, and, moreover, even
collecting a random sample of the peers is difficult to accomplish. To
counter these problems, we have developed an adaptive two-phase
sampling approach based on random walks of the P2P graph, as well as
block-level sampling techniques. We present extensive experimental
evaluations to demonstrate the feasibility of our proposed solution.
EXISTING SYSTEM: In existing systems, doesn’t use any random selection of peer
algorithm. This will make the system latency. If any of the system
has disconnected means very difficult to identify that. So that was
not given the accurate output.
 Also they pass the queries to the peer network one by one. This is
also making Latency.
 They didn’t use Markov chain algorithm and random selection of
nodes at all. So the process only by done sequential.
 Then the average output of all peers and probability of output you
should have to find manually. To reduce the latency we are going
for range query processing.
PROPOSED SYSTEM
 In our proposed system, we are using range query processing to get
the result to decrease the latency.
 We are using Markov chain random walk algorithm to select the
peers from the network randomly. This will make the system faster
than the existing systems.
 In our proposed, we are passing aggregate rules to get the
information from the peers which are connected with the network.
 We are going to pass the aggregate queries to the common
database and get the information from all the peers in the same
network.
 If any of the peers has disconnected from the network then our
project shows which peer get disconnected.
 Random selection of peers will reduce the latency of the process.
HARDWARE REQUIRMENTS: SYSTEM
:
PENTIUM IV 2.4 GHz
 HARD DISK
:
40 GB
 RAM
:
256 MB
SOFTWARE REQUIRMENTS: IDE
:
VS .NET 2005
 FRONT END
:
C#.net
 BACK END
:
SQL SERVER 2000
 OPERATING SYSTEM
:
WINDOWS XP