A Blast implementation in Hadoop MapReduce using low cost commodity hardware

Tadist, Khawla; Mrabti, Fatiha; Zahi, Azeddine; Arivazhagan, S.

doi:10.1016/j.procs.2018.01.099

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…e nationwide online physical education is also a precedent in the history of physical education in China, which shows that online physical education, as a new teaching method, is an inevitable product of today's network information era and is a necessary path for China's teaching to become technological and modernized. Online physical education is converted from teachers and students sharing the same space to teachers and students belonging to different spaces under the conditions of epidemic prevention and control, from 3D three-dimensional teaching by word and body to 2D at teaching and physical education action skills video display through online teaching platforms (Tencent Conference APP, Tencent Classroom APP, Nail APP, and so on), from face-to-face teaching in the same place and space at the same time to live classes or prescribed time periods for learning [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Closed Home Physical Education Teaching Model and Response Strategies Based on Big Data Technology

2022

Journal of Environmental and Public Health

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Online teaching is carried out nationwide in the context of the new crown epidemic prevention and control, and physical education, as one of the compulsory courses in schools and universities, is included within the content of online line lessons. Online physical education teaching is a new approach and attempt to switch from an auxiliary teaching tool to the main teaching tool. The subject matter, target audience, teaching methods, and content of physical education classes have changed dramatically. The development of online physical education has played an important role in popularizing the concept of “sports for life” and establishing the concept of lifelong exercise. Through the analysis and research on the characteristics of online physical education, we propose the measures to promote the overall online teaching ability and level of physical education teachers in the postepidemic period, gradually promote the reform of online and offline physical education, and promote the integration of school physical education and social sports, as well as to build a physical education teacher training system that integrates online and offline development and improve and strengthen the management of Internet physical education resources. Since the teaching effect of online teaching is limited by the teaching means and content, which leads to the low accuracy of the evaluation of online sports teaching effect, for this reason, this paper designs a model based on big data technology and artificial intelligence algorithm for enhancing closed home sports teaching, obtains the overall situation of online physical education by using convolutional neural network to process physical education video sequences, and improves the system performance by embedding the algorithm into the big data framework, to avoid the interference of teaching environment and complete the evaluation of high precision online sports teaching effect. The experimental results show that the proposed method evaluation can improve the computing rate based on ensuring the high accuracy evaluation of physical education online teaching effect.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Closed Home Physical Education Teaching Model and Response Strategies Based on Big Data Technology

2022

Journal of Environmental and Public Health

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“…Step 10: The pbest and the gbest are saved and used to update the particles' position and velocity according to Eqs. (6) and (7). If a particle shows tendency to converge exceeding the boundary, each space is bounded by the range [−x max , x max ] , velocity clamping limits the velocity to the range…”

Section: Our Proposed Approachmentioning

confidence: 99%

“…Big data refers to information with massive volume and high dimensional space [4], it is usually defined through its four characteristics: volume (physical size of data); variety (structure and diversity of data types); velocity (rate at which data is being generated and at which it needs to be processed) and value (the usefulness of analyzing big data). To overcome these challenges, frameworks such as the Hadoop's implementation of MapReduce [5] were developed, which are designed not only to address these issues but also to work with low commodity hardware [6]. Other prominent big data technologies such as Apache Spark [7] support the same applications and share the same parallelization background as Hadoop, while retaining the scalability and fault tolerance of MapReduce with more flexibility.…”

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confidence: 99%

SDPSO: Spark Distributed PSO-based approach for feature selection and cancer disease prognosis

et al. 2021

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The Dimensionality Curse is one of the most critical issues that are hindering faster evolution in several fields broadly, and in bioinformatics distinctively. To counter this curse, a conglomerate solution is needed. Among the renowned techniques that proved efficacy, the scaling-based dimensionality reduction techniques are the most prevalent. To insure improved performance and productivity, horizontal scaling functions are combined with Particle Swarm Optimization (PSO) based computational techniques. Optimization algorithms are an interesting substitute to traditional feature selection methods that are both efficient and relatively easier to scale. Particle Swarm Optimization (PSO) is an iterative search algorithm that has proved to achieve excellent results for feature selection problems. In this paper, a composite Spark Distributed approach to feature selection that combines an integrative feature selection algorithm using Binary Particle Swarm Optimization (BPSO) with Particle Swarm Optimization (PSO) algorithm for cancer prognosis is proposed; hence Spark Distributed Particle Swarm Optimization (SDPSO) approach. The effectiveness of the proposed approach is demonstrated using five benchmark genomic datasets as well as a comparative study with four state of the art methods. Compared with the four methods, the proposed approach yields the best in average of purity ranging from 0.78 to 0.97 and F-measure ranging from 0.75 to 0.96.

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