Finding All You Need: Web APIs Recommendation in Web of Things Through Keywords Search

Qi, Lianyong; He, Qiang; Chen, Feifei; Dou, Wanchun; Wan, Shaohua; Zhang, Xuyun; Xu, Xiaolong

doi:10.1109/tcss.2019.2906925

Cited by 145 publications

(104 citation statements)

References 19 publications

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“…is process would repeat until the number of iterations reached . Based on the utility value of each solution, the alternative solutions are sorted and we select the optimal solution which has the highest utility value (Lines [16][17][18][19][20]. e ow chart of selecting the optimal strategy is shown in Figure 4.…”

Section: Methods Overviewmentioning

confidence: 99%

“…With the adoption of fossil fuels, various problems such as air pollution and rapid resource consumption have aroused, jeopardizing the sustainable development of environment [1,15]. To improve the environment, renewable energy is used as a substitute for fossil fuels [15,16]. However, the supply of renewable energy depends on many uncontrollable natural factors, which is intermittent.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Joint Optimization of Energy Conservation and Migration Cost for Complex Systems in Edge Computing

Xue

Cui

et al. 2019

Complexity

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By means of the complex systems, multiple renewable energy sources are integrated to provide energy supply for users. Considering that there are massive services needed to process in complex systems, the mobile services are offloaded from mobile devices to edge servers for efficient implementation. In spite of the benefits of complex systems and edge servers, massive resource requirements for implementing the increasing resource requests decrease the execution efficiency and affect the whole resource usage of edge servers. Therefore, it remains an issue to achieve dynamic scheduling of the computing resources across edge servers. With the consideration of this issue, a Balanced Resource Scheduling Method, named BRSM, for trade-offs between virtual machine (VM) migration cost and energy consumption of VM migrations for edge server management, named BRSM, is designed in this paper. Technically, we analyze the load conditions of edge servers and formulate the energy consumption of VM migrations and VM migration cost as a multi-objective optimization problem. Then, we propose a dynamic resource scheduling method for WMAN to deal with the multi-objective optimization problem. In addition, nondominated sorting genetic algorithm III (NSGA-III) is adopted to generate optimal resource scheduling strategies. Finally, we conduct experiment simulations to testify the efficiency of the proposed method BRSM.

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Section: Methods Overviewmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Joint Optimization of Energy Conservation and Migration Cost for Complex Systems in Edge Computing

Xue

Cui

et al. 2019

Complexity

Self Cite

View full text Add to dashboard Cite

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“…Prior knowledge of rainfall behavior can help farmers and policy formulation to minimize crop damage. Moreover, it also plays an important role in disaster warning and relief [7][8][9]. The rain gauge is a simple and effective way to measure precipitation.…”

Section: Introductionmentioning

confidence: 99%

Ground radar precipitation estimation with deep learning approaches in meteorological private cloud

Tian¹,

Lei²,

Liu³

et al. 2020

J Cloud Comp

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Accurate precipitation estimation is significant since it matters to everyone on social and economic activities and is of great importance to monitor and forecast disasters. The traditional method utilizes an exponential relation between radar reflectivity factors and precipitation called Z-R relationship which has a low accuracy in precipitation estimation. With the rapid development of computing power in cloud computing, recent researches show that artificial intelligence is a promising approach, especially deep learning approaches in learning accurate patterns and appear well suited for the task of precipitation estimation, given an ample account of radar data. In this study, we introduce these approaches to the precipitation estimation, proposing two models based on the back propagation neural networks (BPNN) and convolutional neural networks (CNN) respectively, to compare with the traditional method in meteorological service systems. The results of the three approaches show that deep learning algorithms outperform the traditional method with 75.84% and 82.30% lower mean square errors respectively. Meanwhile, the proposed method with CNN achieves a better performance than that with BPNN for its ability to preserve the spatial information by maintaining the interconnection between pixels, which improves 26.75% compared to that with BPNN.

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“…Due to the developments of internet of things and sensor technologies, researchers can gather and store a quantity of hydrological data (like rainfall, runoff, soil moisture, evaporation) from different locations. Extract patterns from large historical hydrological data with intelligent methods help improve accuracy of flood prediction and could benefit from further development of the latest techniques, like deep learning human-centric representation [6][7][8] and intelligent human-centered computing [9][10][11][12][13][14][15]. Specifically, we refer to patterns as inherent non-linear functional relationship between hydrological data and flood generation, which is too complex to explain with functional system other than implicit description by machine learning models.…”

Section: Introductionmentioning

confidence: 99%

SMOTE-Boost-based sparse Bayesian model for flood prediction

Ding

Feng

2020

J Wireless Com Network

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With a significant development of big data analysis and cloud-fog-edge computing, human-centered computing (HCC) has been a hot research topic worldwide. Essentially, HCC is a cross-disciplinary research domain, in which the core idea is to build an efficient interaction among persons, cyber space, and real world. Inspired by the improvement of HCC on big data analysis, we intend to involve related core and technologies to help solve one of the most important issues in the real world, i.e., flood prediction. To minimize the negative impacts brought by floods, researchers pay special attention to improve the accuracy of flood forecasting with quantity of technologies including HCC. However, historical flood data is essentially imbalanced. Imbalanced data causes machine learning classifiers to be more biased towards patterns with majority samples, resulting in poor classification of pattern with minority samples. In this paper, we propose a novel Synthetic Minority Over-sampling Technique (SMOTE)-Boost-based sparse Bayesian model to perform flood prediction with both high accuracy and robustness. The proposed model consists of three modules, namely, SMOTE-based data enhancement, AdaBoost training strategy, and sparse Bayes model construction. In SMOTE-based data enhancement, we adopt a SMOTE algorithm to effectively cover diverse data modes and generate more samples for prediction pattern with minority samples, which greatly alleviates the problem of imbalanced data by involving experts' analysis and users' intentions. During AdaBoost training strategy, we propose a specifically designed AdaBoost training strategy for sparse Bayesian model, which not only adaptively and inclemently increases prediction ability of Bayesian model, but also prevents its over-fitting performance. Essentially, the design of AdaBoost strategy helps keep balance between prediction ability and model complexity, which offers different but effective models over diverse rivers and users. Finally, we construct a sparse Bayesian model based on AdaBoost training strategy, which could offer flood prediction results with high rationality and robustness. We demonstrate the accuracy and effectiveness of the proposed model for flood prediction by conducting experiments on a collected dataset with several comparative methods.

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Finding All You Need: Web APIs Recommendation in Web of Things Through Keywords Search

Cited by 145 publications

References 19 publications

Joint Optimization of Energy Conservation and Migration Cost for Complex Systems in Edge Computing

Joint Optimization of Energy Conservation and Migration Cost for Complex Systems in Edge Computing

Ground radar precipitation estimation with deep learning approaches in meteorological private cloud

SMOTE-Boost-based sparse Bayesian model for flood prediction

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