A HIGHLY ACCURATE PREDICTION ALGORITHM FOR
UNKNOWN WEB SERVICE QOS VALUES
ABSTRACT
Quality of Service (QoS) guarantee is an important component of service
recommendation. Generally, some QoS values of a service are unknown to its users who
has never invoked it before, and therefore the accurate prediction of unknown QoS values
is significant for the successful deployment of Web service-based applications.
Collaborative filtering is an important method for predicting missing values, and has thus
been widely adopted in the prediction of unknown QoS values. However, collaborative
filtering originated from the processing of subjective data, such as movie scores. The
QoS data of Web services are usually objective, meaning that existing collaborative
filtering-based approaches are not always applicable for unknown QoS values. Based on
real world Web service QoS data and a number of experiments, in this paper, we
determine some important characteristics of objective QoS datasets that have never been
found before. We propose a prediction algorithm to realize these characteristics, allowing
the unknown QoS values to be predicted accurately. Experimental results show that the
proposed algorithm predicts unknown Web service QoS values more accurately than
other existing approaches
EXISTING SYSTEM
The QoS data of Web services are usually objective, meaning that existing
collaborative filtering-based approaches are not always applicable for unknown QoS
values. Inspired by the successes of CF achieved by existing commercial recommender
systems, many studies have used CF-based methods to predict unknown QoS values.
Most existing QoS prediction methods are inspired by these CF ideas, for which we call
traditional CF methods to distinguish our proposed algorithm. The existing CF based
prediction methods for un-known QoS values have not realized the above differences
between subjective and objective data and therefore cannot predict objective QoS values
accurately. In allusion to this problem this paper presents a highly accurate prediction
algorithm (HAPA) for unknown Web service QoS values. HAPA is also CF-based, i.e.
also use similar users and similar items to make prediction, but with fundamental changes
from traditional CF approaches to adapt to the characteristics of objective QoS data.
PROPOSED SYSTEM
Based on these characteristics, we proposed our HAPA ( Hapa is a term used to
describe a person of mixed ethnic heritage) .The prediction accuracy of HAPA was
shown to outperform that of many of existing QoS prediction methods. As the definition
of Objective Data, Web service QoS is determined as a result of some objective factors,
such as network traffic, bandwidth, when and where a user accessed a Web service. Our
proposed HAPA does not predict unknown QoS values by these objective factors, but
directly by the known QoS values. We can make predictions even more accurately if we
know the relationship between these objective factors and the final QoS. To work out this
relationship, we still have some important problems to solve, such as finding the core
objective factors, how observe these objective factors, how probe user context and how
learn this relationship. we propose a Web service QoS value prediction algorithm HAPA
to realize these characteristics, allowing the unknown QoS values to be predicted
accurately. Finally, we conduct several real world experiments to verify our prediction
accuracy.
Specifically, our key contributions are as follows.
 We are trying to solve these problems and will propose our approaches in the
future work.
 We propose a prediction algorithm to realize these characteristics, allowing the
unknown QoS values to be predicted accurately.
 Doctor and patient relationship measured by hospital management in case Qos
values denoted which one is the better approach.
 Experimental results show that the proposed algorithm predicts unknown Web
service QoS values more accurately than other existing approaches.
PROPOSED SYSTEM ALGORITHMS
It is difficult to mine the peculiarities of Web service QoS values, and the
prediction accuracy of previous algorithms cannot be trusted without believable and
sufficient real-world Web service QoS data.
 We now discuss the computational complexity of predicting one unknown QoS
value using our prediction algorithm.
 These corollaries are the theoretical foundation of our proposed algorithm HAPA.
HAPA includes user-based and item-based prediction according to Corollaries and
respectively.
 All similarities are generally calculated in advance since it is very time-consuming
with a large dataset. Therefore similarities calculations are not included in the
complexity of our prediction algorithm.
 To validate the accuracy of our algorithm, we predict only the known values, so
that we can evaluate the error between the predicted values and real values.
ADVANTAGES
 Save Time – Do you have the specific list that you want to buy? With just a
couple of clicks of the mouse, you can purchase your shopping orders and
instantly move to other important things, which can save time.
 Save Fuel – The market of fuel industries battles from increasing and decreasing
its cost every now and again, but no matter how much the cost of fuel are it does
not affect your shopping errands. One of the advantages of shopping online is that
there is no need for vehicles, so no purchase of fuel necessary.
 Save Energy – Admit it, it is tiresome to shop from one location and transfer to
another location. What is worse is that there are no available stocks for the
merchandise you want to buy. In online shopping, you do not need to waste your
precious energy when buying.
 Comparison of Prices – The advanced innovation of search engine allows you
to easily check prices and compare with just a few clicks.
It is very
straightforward to conduct price comparisons from one online shopping website to
another. This gives you the freedom to determine which online store offers the
most affordable item you are going to buy.
 24/7 Availability – Online shopping stores are open round the clock of 24/7, 7
days a week and 365 days. It is very rare to find any conventional retail stores that
are open 24/7. The availability of online stores give you the freedom to shop at
your own pace and convenience.
 Hate Waiting in Lines – When buying items online, there are no long lines you
have to endure, just to buy your merchandise. The idea of shopping online is
cutting down those bad habits of standing in a long line and just waiting. Every
online store is designed with unique individual ordering features to purchase the
item
SAMPLE ARCHITECTURE
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.
SOFTWARE REQUIREMENTS:
Operating system
: Windows 7 Ultimate.
Coding Language
: ASP.Net with C#
Front-End
: Visual Studio 2010 Professional.
Data Base
: SQL Server 2008.
Output:
Missing Item Shopping :