Data-Aware Web Service Recommender System for Energy-Efficient Data Mining Services

Al-Zanbouri, Zainab

doi:10.1109/soca.2018.00015

Cited by 7 publications

(4 citation statements)

References 15 publications

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“…We also have conducted various preliminary experiments relating to select the baseline model that we need to use in our evaluations. We started with matrix factorization model as the main baseline model and we showed that in our work presented in [84]. Afterwards, we used the Pearson Correlation Coefficient (PCC) model in two cases, the case of considering the data as context and the case of its standard form where no data (no context is considered) as we presented in [84].…”

Section: Experiments Designmentioning

confidence: 99%

“…We started with matrix factorization model as the main baseline model and we showed that in our work presented in [84]. Afterwards, we used the Pearson Correlation Coefficient (PCC) model in two cases, the case of considering the data as context and the case of its standard form where no data (no context is considered) as we presented in [84]. Furthermore, we used the tensor factorization model (TF) as an example of independent contextual modeling and this work is presented in [13].…”

Section: Experiments Designmentioning

confidence: 99%

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A Recommendation Model for Energy-Efficient Data Mining Services Based on Data Properties

Al-Zanbouri¹

2021

Preprint

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Currently, there is a big increase in the usage of data analytics applications and services because of the growth in the data produced from different sources. The QoS properties such as response time and latency of these services are important factors to decide which services to select. As a result of IT expansion, energy consumption has become a big issue. Therefore, establishing a QoS-based web service recommender system that considers energy consumption as one of the essential QoS properties represents a significant step towards selecting the energy efficient web services. This dissertation presents an experimental study on energy consumption levels and latency behavior collected from a set of data mining web services running on different datasets. Our study shows that there is a strong relation between the dataset properties and the QoS properties. Based on the findings from this study, a recommender system is built which considers three dimensions (user, service, dataset). The energy consumption values of candidate services invoked by specific users can be predicted for a given dataset. Afterwards, these services can be ranked according to their predicted energy values and presented to users. We propose three approaches to build our recommender system and we treat it as a context-aware recommendation problem. The dataset is considered as contextual information and we use a context-aware matrix factorization model to predict energy values. In the first approach, we adopt the pre-filtering model where the contextual information serves as a query for filtering relevant rating data. In the second approach, we propose a new method for the pre-filtering implementation. Finally, in the last approach, we adopt the contextual modeling method and we explore different ways of representing dataset information as contextual factors to investigate their impacts on the recommendation accuracy. We compare the proposed approaches with the baseline approaches and the results show the effectiveness of the proposed ones. Also, we compare the performance of the three approaches to discover the best-fit approach when being measured using different metrics. Both prediction and recommendation accuracy of the proposed approaches are significantly better than the baseline models.

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Section: Experiments Designmentioning

confidence: 99%

Section: Experiments Designmentioning

confidence: 99%

A Recommendation Model for Energy-Efficient Data Mining Services Based on Data Properties

Al-Zanbouri¹

2021

Preprint

Self Cite

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“…Otherwise, SVD can produce a low dimensional representation of the original user-service space and then compute the neighborhood in reduced space [22]. Zainab Al-Zanbouri [24] has implemented a context-aware matrix factorization model to build a recommender system where energy consumption is the main Quality of Services (QoS) attribute considered for energy-efficient service recommendation. By considering these different techniques, we selected the SVD-based recommendation since it has a high accuracy with minimum wastage time [25].…”

Section: Collaborative Filteringmentioning

confidence: 99%

Improving Web Service Recommendation using Clustering with K-NN and SVD Algorithms

2021

KSII TIIS

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In the advent of the twenty-first century, human beings began to closely interact with technology. Today, technology is developing, and as a result, the world wide web (www) has a very important place on the Internet and the significant task is fulfilled by Web services. A lot of Web services are available on the Internet and, therefore, it is difficult to find matching Web services among the available Web services. The recommendation systems can help in fixing this problem. In this paper, our observation was based on the recommended method such as the collaborative filtering (CF) technique which faces some failure from the data sparsity and the cold-start problems. To overcome these problems, we first applied an ontology-based clustering and then the k-nearest neighbor (KNN) algorithm for each separate cluster group that effectively increased the data density using the past user interests. Then, user ratings were predicted based on the model-based approach, such as singular value decomposition (SVD) and the predictions used for the recommendation. The evaluation results showed that our proposed approach has a less prediction error rate with high accuracy after analyzing the existing recommendation methods.

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“…In [31], the matrix factorization approach is deployed to develop a recommender system for advising energy-saving e-service. The authors advocated to practice database features as a contextual data which could be incorporated in the e-service based recommender system since most of related works did not consider data characteristics in developing e-service recommender systems.…”

Section: Related Workmentioning

confidence: 99%

Reshaping consumption habits by exploiting energy-related micro-moment recommendations: A case study

Sardianos¹,

Varlamis²,

Chronis³

et al. 2020

Preprint

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The environmental change and its effects, caused by human influences and natural ecological processes over the last decade, prove that it is now more prudent than ever to transition to more sustainable models of energy consumption behaviors. User energy consumption is inductively derived from the time-to-time standards of living that shape the user's everyday consumption habits. This work builds on the detection of repeated usage consumption patterns from consumption logs. It presents the structure and operation of an energy consumption reduction system, which employs a set of sensors, smart-meters and actuators in an office environment and targets specific user habits. Using our previous research findings on the value of energy-related micro-moment recommendations, the implemented system is an integrated solution that avoids unnecessary energy consumption. With the use of a messaging API, the system recommends to the user the proper energy saving action at the right moment and gradually shapes user's habits. The solution has been implemented on the Home Assistant open source platform, which allows the definition of automations for controlling the office equipment. Experimental evaluation with several scenarios shows that the system manages first to reduce energy consumption, and second, to trigger users' actions that could potentially urge them to more sustainable energy consumption habits. KeywordsAppliances identification • feature extraction • time-domain descriptor • dimensionality reduction • FNPA-QR • bagging decision tree (BDT).

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Data-Aware Web Service Recommender System for Energy-Efficient Data Mining Services

Cited by 7 publications

References 15 publications

A Recommendation Model for Energy-Efficient Data Mining Services Based on Data Properties

A Recommendation Model for Energy-Efficient Data Mining Services Based on Data Properties

Improving Web Service Recommendation using Clustering with K-NN and SVD Algorithms

Reshaping consumption habits by exploiting energy-related micro-moment recommendations: A case study

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