scholarly journals QoS-based data analytic service selection: a comparative study of different learning models

2021 ◽  
Author(s):  
Bayan Alghofaily
Keyword(s):  
Learning Algorithm ◽  
Selection Process ◽  
Service Selection ◽  
Machine Learning Algorithms ◽  
Good Prediction ◽  
Web Service Selection ◽  
Good Prediction Accuracy ◽  
The Impact ◽  
The Given ◽  
Data Analytic

QoS-based web service selection has been studied in the service computing community for some time; however, data characteristics are not considered. In this work, we have studied the use of different machine learning algorithms as meta-learners in predicting the performance of data analytic services for the given dataset. We used a meta-learning algorithm to incorporate meta-features in the selection process and we used clustering services as an example of data analytic services. We have also investigated the impact of the number of data features on the performance of the meta-learners. We found that, out of the 5 classification models, SVM showed the best results in predicting the recommended service for the given dataset with an accuracy of 78%. When it comes to regression models, MLP was the best regressor. We recommend considering only simple meta-features that can be collected for most datasets, as those proved to be sufficient to achieve good prediction accuracy.

scholarly journals QoS-based data analytic service selection: a comparative study of different learning models

2021 ◽  
Author(s):  
Bayan Alghofaily
Keyword(s):  
Learning Algorithm ◽  
Selection Process ◽  
Service Selection ◽  
Machine Learning Algorithms ◽  
Good Prediction ◽  
Web Service Selection ◽  
Good Prediction Accuracy ◽  
The Impact ◽  
The Given ◽  
Data Analytic

QoS-based web service selection has been studied in the service computing community for some time; however, data characteristics are not considered. In this work, we have studied the use of different machine learning algorithms as meta-learners in predicting the performance of data analytic services for the given dataset. We used a meta-learning algorithm to incorporate meta-features in the selection process and we used clustering services as an example of data analytic services. We have also investigated the impact of the number of data features on the performance of the meta-learners. We found that, out of the 5 classification models, SVM showed the best results in predicting the recommended service for the given dataset with an accuracy of 78%. When it comes to regression models, MLP was the best regressor. We recommend considering only simple meta-features that can be collected for most datasets, as those proved to be sufficient to achieve good prediction accuracy.

scholarly journals A User-Centric QoS-Based Web Service Selection Framework

2021 ◽  
Author(s):  
Delnavaz Mobedpour
Keyword(s):  
Query Language ◽  
Focal Point ◽  
Selection Process ◽  
Service Selection ◽  
Mixed Integer ◽  
User Support ◽  
Web Service Selection ◽  
Selection Framework ◽  
Description Languages ◽  
The One

With the proliferation of web services, the selection process, especially the one based on the non-functional properties (e.g. Quality of Service – QoS attributes) has become a more and more important step to help requestors locate a desired service. There have been many research works proposing various QoS description languages and selection models. However, the end user is not generally the focal point of their designs and the user support is either missing or lacking in these systems. The QoS language sometimes is not flexible enough to accommodate users’ various requirements and is too complex so that it puts extra burden on users. In order to solve this problem, in this thesis we design a more expressive and flexible QoS query language (QQL) targeted for non-expert users, together with the user support on formulating queries and understanding services in the registry. An enhanced selection model based on Mixed Integer Programming (MIP) is also proposed to handle the QQL queries. We performed experiments with a real QoS dataset to show the effectiveness of our framework.

scholarly journals Service Discovery and Selection Based on Dynamic QoS in the Internet of Things

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Naiheng Zhang
Keyword(s):  
Service Quality ◽  
Web Service ◽  
Service Discovery ◽  
Hamming Distance ◽  
Service Selection ◽  
Development Trend ◽  
Service Invocation ◽  
Web Service Selection ◽  
Service Portfolio ◽  
The Impact

Web services are self-describing and self-contained modular applications based on the network. With the deepening of web service applications, service consumers have gradually increased their requirements for service functions and service quality. Aiming at how to select the optimal plan from a large number of execution plans with the same function and different QoS characteristics, this paper proposes a web service selection algorithm that supports QoS global optimization and dynamic replanning. The algorithm uses position matrix coding to represent all execution paths and replanning information of the service combination. By calculating the Hamming distance of the service quality between individuals, the quality of the service portfolio is improved. By specifying the total user time limit and implementing a good solution retention strategy, the problem of the impact of algorithm running time on service quality is solved. The experimental results show that the method proposed in this paper is effectively integrated into the development trend of QoS and close to the requester’s needs and can better meet user needs. This algorithm improves the user’s satisfaction with the returned service to a certain extent and improves the efficiency of service invocation.

A QoS Aware, Cognitive Parameters Based Model for the Selection of Semantic Web Services

2010 ◽  
pp. 320-335
Author(s):  
Sandeep Kumar ◽  
Kuldeep Kumar
Keyword(s):  
Web Services ◽  
Semantic Web ◽  
Web Service ◽  
Selection Process ◽  
Service Selection ◽  
Semantic Web Services ◽  
Semantic Web Service ◽  
Web Service Selection ◽  
The One ◽  
Selection Of

Semantic Web service selection is considered as the one of the most important aspects of semantic web service composition process. The Quality of Service (QoS) and cognitive parameters can be a good basis for this selection process. In this paper, we have presented a hybrid selection model for the selection of Semantic Web services based on their QoS and cognitive parameters. The presented model provides a new approach of measuring the QoS parameters in an accurate way and provides a completely novel and formalized measurement of different cognitive parameters.

scholarly journals Hoax News Classification using Machine Learning Algorithms

2019 ◽  
Vol 9 (2) ◽  
pp. 3938-3944
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Learning Algorithm ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
The Impact ◽  
F Measure

Hoax news on social media has had a dramatic effect on our society in recent years. The impact of hoax news felt by many people, anxiety, financial loss, and loss of the right name. Therefore we need a detection system that can help reduce hoax news on social media. Hoax news classification is one of the stages in the construction of a hoax news detection system, and this unsupervised learning algorithm becomes a method for creating hoax news datasets, machine learning tools for data processing, and text processing for detecting data. The next will produce a classification of a hoax or not a Hoax based on the text inputted. Hoax news classification in this study uses five algorithms, namely Support Vector Machine, Naïve Bayes, Decision Tree, Logistic Regression, Stochastic Gradient Descent, and Neural Network (MLP). These five algorithms to produce the best algorithm that can use to detect hoax news, with the highest parameters, accuracy, F-measure, Precision, and recall. From the results of testing conducted on five classification algorithms produced shows that the NN-MPL algorithm has an average of 93% for the value of accuracy, F-Measure, and Precision, the highest compared to five other algorithms, but for the highest Recall value generated from the algorithm SVM which is 94%. the results of this experiment show that different effects for different classifiers, and that means that the more hoax data used as training data, the more accurate the system calculates accuracy in more detail.

Deep Learning Techniques and Optimization Strategies in Big Data Analytics

2020 ◽  
pp. 142-153 ◽  
Author(s):  
Murugan Krishnamoorthy ◽  
Bazeer Ahamed B. ◽  
Sailakshmi Suresh ◽  
Solaiappan Alagappan
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Learning Algorithm ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Machine Learning Algorithm ◽  
Learning Techniques ◽  
Sample Data ◽  
Increase Productivity ◽  
The Given

Construction of a neural network is the cardinal step to any machine learning algorithm. It requires profound knowledge for the developer in assigning the weights and biases to construct it. And the construction should be done for multiple epochs to obtain an optimal neural network. This makes it cumbersome for an inexperienced machine learning aspirant to develop it with ease. So, an automated neural network construction would be of great use and provide the developer with incredible speed to program and run the machine learning algorithm. This is a crucial assist from the developer's perspective. The developer can now focus only on the logical portion of the algorithm and hence increase productivity. The use of Enas algorithm aids in performing the automated transfer learning to construct the complete neural network from the given sample data. This algorithm proliferates on the incoming data. Hence, it is very important to inculcate it with the existing machine learning algorithms.

Analysis of the impact of inflation expectations based on machine learning intelligent models

2020 ◽  
pp. 1-12
Author(s):  
Nan Lin
Keyword(s):  
Machine Learning ◽  
Economic Growth ◽  
Central Bank ◽  
Information Disclosure ◽  
Impact Analysis ◽  
Learning Algorithm ◽  
Impulse Response Function ◽  
Machine Learning Algorithms ◽  
Inflation Expectations ◽  
The Impact

Our country’s economic growth is overly dependent on government investment, and bank credit and money supply lack a strict monitoring mechanism. Therefore, rapid economic growth is always accompanied by inflation risks. In order to study the effect of inflation impact analysis, based on machine learning algorithms, this paper combines artificial intelligence technology to analyze the impact of inflation expectations, and constructs the central bank information disclosure index and inflation expectations index. Moreover, this paper will perform ADF unit root test on the data. In addition, after confirming that the data is stable, this paper uses the Markov Regime Transfer Vector Autoregressive (MSVAR) model and state-dependent impulse response function to test and analyze the effect of China’s central bank communication in guiding the formation of inflation expectations. Through research, we can see that the machine learning algorithm constructed in this paper has significant effects, which can provide a reference for the analysis of the impact of inflation expectations.

Context-Aware Personalization Recommendation of Web Services

2013 ◽  
Vol 765-767 ◽  
pp. 1490-1493
Author(s):  
Qiang Dong ◽  
Xiu Guo Zhang ◽  
Yuan Yuan ◽  
Ting Ting Han ◽  
Zhi Yi Zhu
Keyword(s):  
Web Services ◽  
Web Service ◽  
Selection Process ◽  
Service Selection ◽  
Research Area ◽  
Context Aware ◽  
Recommendation Algorithm ◽  
Web Service Selection ◽  
Recommendation Strategy ◽  
The Web

Web service selection has been a hot research area in recent years. In order to improve users satisfaction of service selection, optimization algorithm and recommendation algorithm have been used in the web service selection process. This paper uses context information in selecting and implementing process of Web services recommendation to make the recommendation result more accurate. According to different users different requests, combining with contexts obtained from environment, we give matching recommendation strategy suitable for the current situation, personalize the recommendation process and make it possible to improve the accuracy of the recommendation result.

Dynamic Transaction Aware Web Service Selection

2014 ◽  
Vol 23 (03) ◽  
pp. 1450004 ◽  
Author(s):  
Kanchana Rajaram ◽  
Chitra Babu ◽  
Arun Adiththan
Keyword(s):  
Web Service ◽  
Service Selection ◽  
Dynamic Selection ◽  
Web Service Selection ◽  
Composite Web Service ◽  
Requirements Change ◽  
Selection Approach ◽  
Internet Environment ◽  
Run Time ◽  
The Impact

Web service composition, that recursively constructs a composite web service out of the existing services based on a business workflow has been acknowledged as a promising approach to meet the user demands, whenever a single service alone cannot fulfil the needs. In view of frequent failures in the internet environment where the composed service is executed, reliability of the composed service must be ensured. The reliability is determined by the behavioral or transactional properties of component services. The component services for each activity of the workflow must be selected based on their behavior so that their execution results in a consistent termination. Service selection must happen at run-time in order to consider the services available in a service registry at the time of execution. Towards this need, a dynamic transaction aware web service selection approach is proposed in this paper. Further, whenever user requirements change, a long running transaction must be interrupted and cancelled which is not addressed by any of the existing works. Hence, service cancellability property is proposed in this paper and incorporated in the dynamic selection approach. The overhead of the proposed run-time selection approach is assessed and the impact of increased services on its performance is also measured.

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