The world revolves around data. Hence the importance of storing data becomes highly essential, but in order to store the data as such requires a lot of storage space which brings up the role of data Compression. It be...
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This article proposes an intelligent data directory construction scheme aimed at supporting the design of ultra-high voltage converter stations. The scheme utilizes visual techniques such as knowledge graphs to analyz...
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In the era of new media, the high-speed communication of information brings new challenges to the guidance of network public opinion, and the characteristics of network public opinion communication change with the dev...
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The increasing need for causal analysis in large-scale industrial datasets necessitates the development of efficient and scalable causal algorithms for real-world applications. This paper addresses the challenge of sc...
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Efficient workload prediction is essential for enabling timely resource provisioning in cloud computing environments. However, achieving accurate predictions, ensuring adaptability to changing conditions, and minimizi...
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ISBN:
(纸本)9798400702341
Efficient workload prediction is essential for enabling timely resource provisioning in cloud computing environments. However, achieving accurate predictions, ensuring adaptability to changing conditions, and minimizing computation overhead pose significant challenges for workload prediction models. Furthermore, the continuous streaming nature of workload metrics requires careful consideration when applying machine learning and data mining algorithms, as manual hyperparameter optimization can be time-consuming and suboptimal. We propose an automated parameter tuning and adaptation approach for workload prediction models and concept drift detection algorithms utilized in predicting future workload. Our method leverages a pre-built knowledge-base based on historical data statistical features, enabling automatic adjustment of model weights and concept drift detection parameters. Additionally, model adaptation is facilitated through a transfer learning approach. We evaluate the effectiveness of our automated approach by comparing it with static approaches using synthetic and real-world datasets. By automating the parameter tuning process and integrating concept drift detection, in our experiments the proposed method enhances the accuracy and efficiency of workload prediction models by 50%.
Imbalanced class distribution is common issue in machine learning and data mining. It affects various applications like fraud detection, medical diagnosis, and network intrusion detection. The mentioned problem occurs...
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With the rapid growth of data from heterogeneous, distributed sources, data streams need to be increasingly processed in the cloud-edge continuum. processing is distributed between diverse edge environments and homoge...
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ISBN:
(纸本)9798400704437
With the rapid growth of data from heterogeneous, distributed sources, data streams need to be increasingly processed in the cloud-edge continuum. processing is distributed between diverse edge environments and homogeneous, but powerful data centers, to optimally utilize available resources, alleviate infrastructure bottlenecks and follow the principle of data locality. Compared to cloud infrastructure, compute and data resources on the edge are distributed across geographical regions, infrastructures and organizational units with independent dataprocessing systems. However, existing data stream processing frameworks provide integrated systems, which require matching software components to be used across the whole, distributed infrastructure or even assume centralized control over all resources. We argue, that this cloud-like, centralized approach does not fit to the decentralized nature of the edge environment. Focusing on fully integrated systems, which are either limited to single organizational units or require a certain degree of homogeneity limits data sovereignty and the overall potential of distributed data stream processing on the edge. Instead, we propose to develop data stream processing as part of data ecosystems, and connect locally independent and sovereign systems through a lightweight set of common standards, protocols, and semantic descriptions.
The classification of agricultural crops has several upbeat. With the growing demand for more agricultural yield to meet food need, appropriate usage of land has become an utmost requisite. Thus, to provide prompt inf...
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The integration of mobile learning and machine learning, driven by the proliferation of smart handheld devices and the vast amount of learning resources available on the internet, has revolutionized education. This st...
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ISBN:
(纸本)9798400716225
The integration of mobile learning and machine learning, driven by the proliferation of smart handheld devices and the vast amount of learning resources available on the internet, has revolutionized education. This study explores the use of mobile learning, specifically leveraging mobility and big data mining techniques, to improve college English vocabulary acquisition among university students. By addressing the challenges faced in mobile learning and applying innovative methodologies, we have developed a personalized recommendation system based on data mining algorithms. This system suggests English vocabulary words to students based on their past performance, harnessing the power of big data mining. Through a comprehensive evaluation, we have demonstrated the effectiveness of our approach in enhancing vocabulary acquisition and retention compared to traditional book-based learning methods. The findings of this research not only contribute to the advancement of mobile learning but also provide valuable insights for educators and policymakers on the effective utilization of mobility and big data mining for enhanced student learning outcomes.
The study examines the application of machine learning algorithms, namely Decision Trees, Random Forest, and k-Nearest Neighbors, for improving predictive analytics in customer complaint management at Orange Telecom. ...
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