scholarly journals Personalized decision making for QOS-based web service selection

2021 ◽  
Author(s):  
Muhammad S Saleem
Keyword(s):  
Decision Making ◽  
Learning To Rank ◽  
Service Selection ◽  
Decision Strategy ◽  
The Past ◽  
Web Service Selection ◽  
Selection Systems ◽  
Ranking Model ◽  
Service Ranking ◽  
Multiple Qos

In order to choose from a list of functionally similar services, users often need to make their decisions based on multiple QoS criteria they require on the target service. In this process, different users may follow different decision making strategies, some are compensatory and some are non-compensatory. Most of the current QoS-based service selection systems do not consider these decision strategies in the ranking process, which we believe are crucial for generating accurate ranking results for individual users. In this thesis, we propose a decision strategy based service ranking model. Furthermore, considering that different users follow different strategies in different contexts at different times, we apply a learning to rank algorithm to learn a personalized ranking model for individual users based on how they select services in the past. Our experiment result shows the effectiveness of the proposed approach.

scholarly journals Personalized decision making for QOS-based web service selection

2021 ◽  
Author(s):  
Muhammad S Saleem
Keyword(s):  
Decision Making ◽  
Learning To Rank ◽  
Service Selection ◽  
Decision Strategy ◽  
The Past ◽  
Web Service Selection ◽  
Selection Systems ◽  
Ranking Model ◽  
Service Ranking ◽  
Multiple Qos

In order to choose from a list of functionally similar services, users often need to make their decisions based on multiple QoS criteria they require on the target service. In this process, different users may follow different decision making strategies, some are compensatory and some are non-compensatory. Most of the current QoS-based service selection systems do not consider these decision strategies in the ranking process, which we believe are crucial for generating accurate ranking results for individual users. In this thesis, we propose a decision strategy based service ranking model. Furthermore, considering that different users follow different strategies in different contexts at different times, we apply a learning to rank algorithm to learn a personalized ranking model for individual users based on how they select services in the past. Our experiment result shows the effectiveness of the proposed approach.

Personalized Decision-Strategy based Web Service Selection using a Learning-to-Rank Algorithm

2015 ◽  
Vol 8 (5) ◽  
pp. 727-739 ◽  
Author(s):  
Muhammad Suleman Saleem ◽  
Chen Ding ◽  
Xumin Liu ◽  
Chi-Hung Chi
Keyword(s):  
Web Service ◽  
Learning To Rank ◽  
Service Selection ◽  
Decision Strategy ◽  
Web Service Selection

Ranking with Genetics

2020 ◽  
Vol 10 (3) ◽  
pp. 20-34
Author(s):  
Lawrence Master
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Question Answering ◽  
Learning To Rank ◽  
Approaches To Learning ◽  
Genetic Optimization ◽  
Regularization Technique ◽  
The Past ◽  
Ranking Algorithms ◽  
Ranking Model

There are many applications for ranking, including page searching, question answering, recommender systems, sentiment analysis, and collaborative filtering, to name a few. In the past several years, machine learning and information retrieval techniques have been used to develop ranking algorithms and several list wise approaches to learning to rank have been developed. We propose a new method, which we call GeneticListMLE++ and GeneticListNet++, which build on the original ListMLE and ListNet algorithms. Our method substantially improves on the original ListMLE and ListNet ranking approaches by incorporating genetic optimization of hyperparameters, a nonlinear neural network ranking model, and a regularization technique.

Study on Filter Algorithm of QoS-Based Fuzzy Multi-Attribute Web Service Composition

2012 ◽  
Vol 182-183 ◽  
pp. 2131-2135
Author(s):  
Xiang Rong Fang
Keyword(s):  
Decision Making ◽  
Web Service ◽  
Information Matrix ◽  
Service Selection ◽  
Fuzzy Decision Making ◽  
Interval Numbers ◽  
Functional Attributes ◽  
Web Service Selection ◽  
Multi Attribute Decision Making ◽  
Definition Of

In this paper, an in-depth research has been done on the physical meaning and value features of the QoS attribute values under the definition of the functional attributes and non-functional attributes of Web services, giving the computation formulae and matching method expressing QoS composition as interval numbers, providing a more comprehensive and objective information matrix for fuzzy multi-attribute decision making. For multi-attributes fuzzy decision-making matrix with weight information known and the preference information of composition solution given in the form of interval numbers, calculating the overall attribute values, and sorting solutions by using the formula of probability degree, a QoS-driven multi-level composition Web Service selection algorithm is proposed, and simulation runs indicate the feasibility and effectiveness of the algorithm.

scholarly journals Data dependent web service selection

2021 ◽  
Author(s):  
Navati Jain
Keyword(s):  
Web Service ◽  
Service Selection ◽  
Selection Methods ◽  
Web Service Selection ◽  
Selection Approach ◽  
Meta Learning ◽  
Service Ranking ◽  
Selection Methodology ◽  
Data Analytic

Data mining applications and services are becoming increasingly important, especially in this age of Big Data. QoS (Quality of Service) properties such as latency, reliability, response time of such services can vary based on the characteristics of the dataset being processed. The existing QoS-based web service selection methods are not adequate for ranking these types of services since they do not consider these dataset characteristics. We have proposed a service selection methodology to predict the QoS values for data analytic services based on the attributes of the dataset involved by incorporating a meta-learning approach. Subsequently we rank the services according to the predicted QoS values. The outcome of our experiments proves the effectiveness of this approach with an improvement of above 20% in service ranking when compared to the traditional QoS selection approach.

scholarly journals Data dependent web service selection

2021 ◽  
Author(s):  
Navati Jain
Keyword(s):  
Web Service ◽  
Service Selection ◽  
Selection Methods ◽  
Web Service Selection ◽  
Selection Approach ◽  
Meta Learning ◽  
Service Ranking ◽  
Selection Methodology ◽  
Data Analytic

Data mining applications and services are becoming increasingly important, especially in this age of Big Data. QoS (Quality of Service) properties such as latency, reliability, response time of such services can vary based on the characteristics of the dataset being processed. The existing QoS-based web service selection methods are not adequate for ranking these types of services since they do not consider these dataset characteristics. We have proposed a service selection methodology to predict the QoS values for data analytic services based on the attributes of the dataset involved by incorporating a meta-learning approach. Subsequently we rank the services according to the predicted QoS values. The outcome of our experiments proves the effectiveness of this approach with an improvement of above 20% in service ranking when compared to the traditional QoS selection approach.

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