The virtual power plant is an important part of the new power system. Its IoT components need to collect and process energy equipment measurement data in real time, which inevitably uses stream computing. This paper i...
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Text mining classifies, clusters, extract useful information, searches, and analyses natural language texts to uncover patterns. Text mining extracts and Natural language processing (NLP) may create organized data fro...
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The computing continuum is growing because multiple devices are added daily. Edge devices play a key role in this because computation is decentralized or distributed. Edge computing is advanced by using AI/ML algorith...
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ISBN:
(纸本)9798350304831
The computing continuum is growing because multiple devices are added daily. Edge devices play a key role in this because computation is decentralized or distributed. Edge computing is advanced by using AI/ML algorithms to become more intelligent. Besides, Edge data protocols are useful for transmitting or receiving data between devices. Since, computation efficiency is possible when the data is received at the Edge timely, and it is possible only when the data protocols are efficient, reliable and fast. Most edge data protocols are defined with static set of rules and their primary purpose is to provide standardized and reliable data communications. Edge devices need autonomous or dynamic protocols that enable interoperability, autonomous decision making, scalability, and adaptability. This paper examines the limitations of popular data protocols used in edge networks, the need for intelligent data protocols, and their implications. We also explore possible ways to simplify learning for edge devices and discuss how intelligent data protocols can mitigate challenges such as congestion, message filtering, message expiration, prioritization, and resource handling.
As the number of the measurement target sensors continues to grow and complicated, and the original track data association algorithm is complexity of the target increases, the process of track association is gradually...
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I provide a perspective on the development of quantum computing for data science, including a dive into state-of-the-art for both hardware and algorithms and the potential for quantum machine learning.
I provide a perspective on the development of quantum computing for data science, including a dive into state-of-the-art for both hardware and algorithms and the potential for quantum machine learning.
With the proliferation of intelligent vehicles, addressing the demands of computing-intensive and delay-sensitive vehicle tasks has become a formidable challenge. Vehicle edge computing (VEC) has been proposed as an a...
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ISBN:
(数字)9789819708116
ISBN:
(纸本)9789819708109;9789819708116
With the proliferation of intelligent vehicles, addressing the demands of computing-intensive and delay-sensitive vehicle tasks has become a formidable challenge. Vehicle edge computing (VEC) has been proposed as an advanced paradigm that leverages edge servers such as road side units (RSUs) to offload tasks, thereby enhancing vehicle services. However, similar computation tasks in the VEC environment result in computational redundancy, imposing additional burden on the limited edge resources. Moreover, the increased interdependency among different tasks of vehicle tasks adds complexity to the offloading strategy. To this end, we propose a collaborative task offloading and computation reuse framework, called TOC, which enables RSUs to reuse previous computations and design a task offloading scheme based on a Conflict Graph(CG) model. We also evaluate the efficiency and effectiveness of TOC using real-world datasets, and our results show that TOC is able to reduce the task completion time by 48.73% compared to baselines.
In recent years, distributed data-processing frameworks have become popular for processing big data. However, in an HPC, where the computation and storage nodes are separated, the bandwidth between the computation and...
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ISBN:
(纸本)9798350370621
In recent years, distributed data-processing frameworks have become popular for processing big data. However, in an HPC, where the computation and storage nodes are separated, the bandwidth between the computation and storage components is small, causing a reduction in dataprocessing throughput. Therefore, in this paper, data were stored on the computation node to solve the dataprocessing throughput degradation. We propose an I/O acceleration method that integrates Apache Arrow and CHFS. It leverages non-volatile memory, a state-of-the-art storage device, via CHFS and leverages CHFS from a distributed dataprocessing framework via Apache Arrow's abstract file system API. The evaluation results showed that the system achieved up to 11.60 times higher bandwidth than when reading data from the parallel file system Lustre. This study also compared with Apache Arrow with BeeOND and UnifyFS, other ad hoc filesystems. The proposed Apache Arrow CHFS showed up to 1.67x/1.23x better write performance. The implementation is published at https://***/tsukuba-hpcs/arrow-chfs
Now a Days the multimedia dataprocessing and protection is becoming a vital thing in IOT, RF (Radio frequency), etc… Communications. The significant encryption methodology is used to secure multimedia data either in...
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Quantum computers promise polynomial or exponential speed-up in solving certain problems compared to classical computers. However, in practical use, there are currently a number of fundamental technical challenges. On...
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ISBN:
(纸本)9789819709885;9789819709892
Quantum computers promise polynomial or exponential speed-up in solving certain problems compared to classical computers. However, in practical use, there are currently a number of fundamental technical challenges. One of them concerns the loading of data into quantum computers, since they cannot access common databases. In this vision paper, we develop a hybrid data management architecture in which databases can serve as data sources for quantum algorithms. To test the architecture, we perform experiments in which we assign data points stored in a database to clusters. For cluster assignment, a quantum algorithm processes this data by determining the distances between data points and cluster centroids.
Using trajectories of moving objects and performing contact event query during disease transmission is an effective method of prevention and control. Existing contact query processingalgorithms only consider single-s...
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ISBN:
(纸本)9798350307887
Using trajectories of moving objects and performing contact event query during disease transmission is an effective method of prevention and control. Existing contact query processingalgorithms only consider single-source (one-to-one) contact event and thus can not discover multi-source (n-to-one) contact. events. In this paper, we propose efficient multi-source contact. event query processing methods that are capable of querying multi-source contact events. The definition of multi-source contact events is first formulated. Then, a baseline multi-source contact event query processing algorithm is presented, which adopts the idea of sliding window-based sequential scanning. To improve the query efficiency, the 2-dimensional bitmap filter and the anchor time point scanning are designed and used in the optimized query processing algorithm. Comprehensive experiments on real-world data demonstrate that the proposed algorithms can find more potential contact events and have good performance in the manner of query tune cost.
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