Big Data Cleaning Algorithms in Cloud Computing

Zhang, Feng; Hui-feng, Xue; Xu, Dan; Zhang, Yongheng; You, Fei

doi:10.3991/ijoe.v9i3.2765

Cited by 16 publications

(11 citation statements)

References 9 publications

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“…16,17 Sansom, 9 Nguyen K et al, 18 Price D et al, 19 analyzed theoretically how memory residency (the ratio between live memory and the total heap) affects the performance of both collectors, and adopted a dual-mode collector, which switches between copying and compacting according to the memory usage. 16,17 Sansom, 9 Nguyen K et al, 18 Price D et al, 19 analyzed theoretically how memory residency (the ratio between live memory and the total heap) affects the performance of both collectors, and adopted a dual-mode collector, which switches between copying and compacting according to the memory usage.…”

Section: Space Efficient Collectorsmentioning

confidence: 99%

“…As copying-based collectors need to reserve half of the available heap, whereas compacting-based collectors do not; for any particular program, there must be a point in the time-space tradeoff where the two kinds of collectors achieve the same performance. 16,17 Sansom, 9 Nguyen K et al, 18 Price D et al, 19 analyzed theoretically how memory residency (the ratio between live memory and the total heap) affects the performance of both collectors, and adopted a dual-mode collector, which switches between copying and compacting according to the memory usage. 20,21 His algorithm works well and its copying space can make use of our algorithm to further reduce the reserved amount.…”

Section: Space Efficient Collectorsmentioning

confidence: 99%

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SOGC: Implementing surrogate object garbage collector management for a Mobile Cloud Environment

Jeevan¹,

Mohamed²

2018

Concurrency and Computation

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Summary The aim of an ideal distributed Mobile Cloud environment, the surrogate object, is an agent for a particular Mobile Host in the wired or wireless network with specific data structures and methods. The Surrogate object is a software entity that is hosted on mobile support station and acts on behalf of a mobile host during the disconnection operation. The mobile hosts are registered with their own unique identifier in their cloud environment with their mobile support system. If the registered Mobile host is migrated from one cloud to another, the surrogate object handovers the data or resource management to another cloud with a new object with the same entities of old surrogate object, which leads inefficient. The granularity of object migration the old object is useless after the old object switched over into the new object. The collection of unused surrogate object is a big challenge. This problem will be achieved through the garbage collector technique in Mobile Cloud computing. In this research, we propose the Surrogate Object Garbage Collector (SOGC) model with new middleware consists in detecting the unused surrogate object by Mark‐Compact Garbage Collection technique to recycle their object resources. SOGC periodically recycles the idle object and automatically reuses it; whenever it is needed to manage cloud environment, it results in quality‐of‐execution, low latency, and cost optimization in data management.

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Section: Space Efficient Collectorsmentioning

confidence: 99%

Section: Space Efficient Collectorsmentioning

confidence: 99%

SOGC: Implementing surrogate object garbage collector management for a Mobile Cloud Environment

Jeevan¹,

Mohamed²

2018

Concurrency and Computation

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“…Brain imaging big data is a collection of data that has a wide range of sources, diverse types, complex structures, and potential value, and is difficult to apply common methods of processing and analysis, after integrating its own characteristics such as regional, seasonal, diversity, and periodicity of agriculture [1]. Brain imaging big data retains the basic characteristics of big data, such as huge volume, variety, low value, fast processing speed, high veracity and high complexity, and big data application research in brain imaging is still relatively small [2].…”

Section: Introductionmentioning

confidence: 99%

Brain Imaging Big Data Mining and Fusion Method Based on Cognitive Intelligence

Zhang

2020

Preprint

Self Cite

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In order to improve the utilization rate of brain imaging big data and solve the fusion problem of multi-source and heterogeneous brain imaging big data, an improved brain imaging big data ant colony optimization algorithm (BigDataACO) is proposed to complete the multi-source brain imaging big data information in the feature layer and decision-making and the problem of multi-source data fusion was solved. The swarm intelligence algorithm is a process of simulating the complex problem of populations in nature through the mutual cooperation between individuals. The algorithm has potential parallelism and strong robustness, and the algorithm does not depend on specific problems. The definition, principle and implementation method of brain imaging big data fusion problem are studied. Then the insufficiency of big data fusion modeling algorithm is analyzed. Finally, the source and core steps of ant colony big data fusion algorithm are studied. The experimental results show that the improved BigDataACO algorithm is verified by the measured data. Compared with K-means, D-S evidence theory and Bayesian algorithm, the uncertainty of data fusion is greatly reduced by the improved algorithm proposed in this paper.

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“…However, the source of network public opinion information is diversified and heterogeneous. Effectively integrating such information to achieve better decision-making is a technical problem that needs to be solved urgently [2].…”

Section: Introductionmentioning

confidence: 99%

Public Opinion Data Fusion Method based on Ontology Semantics

Wang¹,

Hui-feng²,

Yu³

et al. 2019

Int J Performability Eng

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In order to improve the decision-making level for public opinion responses and realize the semantic fusion of multi-level and multi-source heterogeneous public opinion information, an ontology-based public opinion information fusion method is proposed. Firstly, aiming at quick response decision-making, the situation assessment model of public opinion information fusion is studied, and the information fusion system is constructed. The multi-level evaluation model of situation recognition, situation understanding, and situation prediction is formed. Then, the multi-indicator ontology model and method for public opinion decision-making are constructed, and the public opinion data fusion model based on ontology semantics is proposed, which realizes the relevance analysis and semantic fusion of domain knowledge. Finally, a multi-level public opinion data fusion model is constructed, and the construction of the underlying emergency information knowledge base to support the above functions is deeply studied. The simulation results show that the feasibility and efficiency of the situation assessment problem are solved by this method, the time complexity and space complexity of attribute reduction and value reduction are reduced, and the matching efficiency of situation assessment rules is improved.

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Big Data Cleaning Algorithms in Cloud Computing

Cited by 16 publications

References 9 publications

SOGC: Implementing surrogate object garbage collector management for a Mobile Cloud Environment

SOGC: Implementing surrogate object garbage collector management for a Mobile Cloud Environment

Brain Imaging Big Data Mining and Fusion Method Based on Cognitive Intelligence

Public Opinion Data Fusion Method based on Ontology Semantics

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