A HIGHLY ACCURATE PREDICTION ALGORITHM FOR UNKNOWN WEB SERVICE QOS VALUES

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.

Ø  We are trying to solve these problems and will propose our approaches in the future work.

Ø  We propose a prediction algorithm to realize these characteristics, allowing the unknown QoS values to be predicted accurately.

Ø  Doctor and patient relationship measured by hospital management in case Qos values denoted which one is the better approach. 

Ø  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.

v We now discuss the computational complexity of predicting one unknown QoS value using our prediction algorithm.

v These corollaries are the theoretical foundation of our proposed algorithm HAPA. HAPA includes user-based and item-based prediction according to Corollaries and respectively.

v 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.

v 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 

Ø  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.

Ø  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.

Ø  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.  

Ø  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. 

Ø  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.  

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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 :