An Econometric Time Series Forecasting Framework for Web Services Recommendation

Singh, Vijendra; Pandey, Manish Kumar; Singh, Pangambam Sendash; Karthikeyan, S.

doi:10.1016/j.procs.2020.03.372

Cited by 5 publications

(3 citation statements)

References 36 publications

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“…Singh et al [16] show a better prediction of the QoS metric values but does not show the efficiency than the recent popular approaches. The subjective and manual labeling of data proposed is time-consuming [30].…”

Section: Rq2-what Are the Challenges To The Ensemble Learning Models In The Studied Approaches?mentioning

confidence: 91%

“…We find several studies that answer to RQ1. Singh et al [16] proposed to use Bayesian Information Criteria (BIC) along with Akaike Information Criteria (AIC) as an evaluation metric for prediction of response time and throughput values. The latter mentioned approach used invocation time of web services with similar functionalities and then forecasted the best web services with convincing quality of services (QoS) values.…”

Section: Rq1-what Are State-of-the-art Approaches That Employed Ensemble Learning Models In the Context Of Web Services Classification Anmentioning

confidence: 99%

See 1 more Smart Citation

Ensemble Learning Models for Classification and Selection of Web Services: A Review

Hasnain¹,

Ghani²,

Jeong³

et al. 2022

Computer Systems Science and Engineering

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This paper presents a review of the ensemble learning models proposed for web services classification, selection, and composition. Web service is an evolutionary research area, and ensemble learning has become a hot spot to assess web services' earlier mentioned aspects. The proposed research aims to review the state of art approaches performed on the interesting web services area. The literature on the research topic is examined using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) as a research method. The study reveals an increasing trend of using ensemble learning in the chosen papers within the last ten years. Naïve Bayes (NB), Support Vector Machine' (SVM), and other classifiers were identified as widely explored in selected studies. Core analysis of web services classification suggests that web services' performance aspects can be investigated in future works. This paper also identified performance measuring metrics, including accuracy, precision, recall, and f-measure, widely used in the literature.

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Section: Rq2-what Are the Challenges To The Ensemble Learning Models In The Studied Approaches?mentioning

confidence: 91%

Section: Rq1-what Are State-of-the-art Approaches That Employed Ensemble Learning Models In the Context Of Web Services Classification Anmentioning

confidence: 99%

Ensemble Learning Models for Classification and Selection of Web Services: A Review

Hasnain¹,

Ghani²,

Jeong³

et al. 2022

Computer Systems Science and Engineering

View full text Add to dashboard Cite

show abstract

“…Indirectly QoS is playing the role of distinguishing the correctness of web services. Depending on a critical feature like quality of service (QoS), web services can be useful for service requesters 4 . In different circumstances, various factors can affect the QoS of the same web services such as network conditions based on the user's location affecting the QoS of the same services that the user used previously.…”

Section: Introductionmentioning

confidence: 99%

Analyzing theQoSprediction for web service recommendation using time series forecasting with deep learning techniques

Gnanasekaran

Chinnasamy

Parasuraman³

2022

Concurrency and Computation

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Quality of service (QoS) is widely adopted to characterize the performance of services invoked by users. For this purpose, the QoS prediction of services constitutes a decisive tool to allow end-users to optimally choose high-quality cloud services aligned with their needs. The fact is that users only consume a few of the broad range of existing services. Thereby, performing a highly accurate service recommendation becomes a challenging task. To tackle the aforementioned challenges, a hybrid deep learning technique is proposed to predict future QoS parameters. Initially, the data is collected from the WSDream data repository, however, the collaborating filtering (CF) technique is used to pre-process the collected data. Accordingly, the filtering technique resolves missing or undermined values and cleans the node. Additionally, this pre-processing technique reduces the error and promotes prediction efficiency. Subsequently, data clustering is performed by introducing a modified k-medoids algorithm, which groups the user services into different clusters. Accordingly, a future QoS is predicted based on the proposed hybrid deep learning method like convolutional neural network-long short-term memory. The proposed method is implemented using Python software and it evaluates response time, throughput, root mean square error, and mean absolute error (MAE). The proposed method is compared with the existing collaborative filtering-k medoids, support vector regression, self-exciting threshold auto-regressive, genetic guided clustering algorithm algorithms, and novel deep learning-based hybrid approach. The result of the proposed method presents higher accuracy and low consumption time. Therefore, the performance of the proposed method is 6% higher than the other existing methods. Accordingly, the MAE, mean absolute percentage error, mean absolute scaled error, and root mean squared error of the proposed method produces 7%, 7.5%, 2%, and 1.5% higher performance than the other existing methods.Subsequently, the precision and recall of the proposed method are approximately 3.5% and 3% high than the other state of art methods. The experimental results highlight that the viability of the proposed approach contrasted with the regular methodologies in real-time. Consequently, by introducing this proposed method the QoS prediction performance is improved and it provides higher-quality solutions.

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A Probe into Performance Analysis of Real-Time Forecasting of Endemic Infectious Diseases Using Machine Learning and Deep Learning Algorithms

Pandey

Srivastava

2021

Lecture Notes on Data Engineering and Communications Technologies

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An Econometric Time Series Forecasting Framework for Web Services Recommendation

Cited by 5 publications

References 36 publications

Ensemble Learning Models for Classification and Selection of Web Services: A Review

Ensemble Learning Models for Classification and Selection of Web Services: A Review

Analyzing theQoSprediction for web service recommendation using time series forecasting with deep learning techniques

A Probe into Performance Analysis of Real-Time Forecasting of Endemic Infectious Diseases Using Machine Learning and Deep Learning Algorithms

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