Hash semi cascade join for joining multi-way map reduce

Mohamed, Marwa Hussien; Khafagy, Mohamed Helmy

doi:10.1109/intellisys.2015.7361167

Cited by 7 publications

(4 citation statements)

References 10 publications

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“…Comparative evaluations against existing methods confirmed the qualitative and quantitative superiority of this approach. The research in [18] focused on image animation, generating a video sequence to animate an object within a source image based on a driving video's motion. Unlike previous methods, this framework accomplished this task without requiring annotations or prior object knowledge.…”

Section: Related Workmentioning

confidence: 99%

“…Marwa et al recommended a system crucial for e-commerce websites to increase product sales by helping users find items of interest based on their history or similar user profiles [16]. They utilized educational data mining to assess the effectiveness of e-learning courses [17], and examined various two-way join algorithms, proposing a new multi-way join algorithm, the hash semi-cascade join, which efficiently joins multiple datasets [18].…”

Section: (%)mentioning

confidence: 99%

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An Approach to Deepfake Video Detection Based on ACO-PSO Features and Deep Learning

Alhaji,

Celik,

Goel

2024

Electronics

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The rapid advancement of deepfake technology presents significant challenges in detecting highly convincing fake videos, posing risks such as misinformation, identity theft, and privacy violations. In response, this paper proposes an innovative approach to deepfake video detection by integrating features derived from ant colony optimization–particle swarm optimization (ACO-PSO) and deep learning techniques. The proposed methodology leverages ACO-PSO features and deep learning models to enhance detection accuracy and robustness. Features from ACO-PSO are extracted from the spatial and temporal characteristics of video frames, capturing subtle patterns indicative of deepfake manipulation. These features are then used to train a deep learning classifier to automatically distinguish between authentic and deepfake videos. Extensive experiments using comparative datasets demonstrate the superiority of the proposed method in terms of detection accuracy, robustness to manipulation techniques, and generalization to unseen data. The computational efficiency of the approach is also analyzed, highlighting its practical feasibility for real-time applications. The findings revealed that the proposed method achieved an accuracy of 98.91% and an F1 score of 99.12%, indicating remarkable success in deepfake detection. The integration of ACO-PSO features and deep learning enables comprehensive analysis, bolstering precision and resilience in detecting deepfake content. This approach addresses the challenges involved in facial forgery detection and contributes to safeguarding digital media integrity amid misinformation and manipulation.

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Section: Related Workmentioning

confidence: 99%

Section: (%)mentioning

confidence: 99%

An Approach to Deepfake Video Detection Based on ACO-PSO Features and Deep Learning

Alhaji,

Celik,

Goel

2024

Electronics

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“…MapReduce is a popular programming model developed by Google. It can process massive datasets in a parallel manner and achieves a high performance [35], [36]. The main idea of MapReduce comes from the divide and conquer algorithms which are used to divide a large problem into smaller subproblems.…”

Section: B Mapreduce Svm Classification Phasementioning

confidence: 99%

Arabic Semantic Similarity Approach for Farmers’ Complaints

Farouk¹,

Khafagy²,

Ali³

et al. 2021

IJACSA

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“…Regarding the multiway join, a cost-based using histogram on MapReduce, JOMR, with ordering consideration was proposed to rearrange the joinable tables within multiway join query to minimize shuffle time [43,44]. Also, the simultaneous pipeline technique was introduced in QPipe in advance which can efficiently evaluate query execution plans produced by a multiquery optimizer [45,46].…”

Section: Join Optimization Different Mapreduce Join Strategiesmentioning

confidence: 99%

Exploiting Sharing Join Opportunities in Big Data Multiquery Optimization with Flink

et al. 2020

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Multiway join queries incur high-cost I/Os operations over large-scale data. Exploiting sharing join opportunities among multiple multiway joins could be beneficial to reduce query execution time and shuffled intermediate data. Although multiway join optimization has been carried out in MapReduce, different design principles (i.e., in-memory Big Data platforms, Flink) are not considered. To bridge the gap of not considering the optimization of Big Data platforms, an end-to-end multiway join over Flink, which is called Join-MOTH system (J-MOTH), is proposed to exploit sharing data granularity, sharing join granularity, and sharing implicit sorts within multiple join queries. For sharing data, our previous work, Multiquery Optimization using Tuple Size and Histogram (MOTH) system, has been introduced to consider the granularity of sharing data opportunities among multiple queries. For sharing sort, our previous work, Sort-Based Optimizer for Big Data Multiquery (SOOM), has been introduced to consider the implicit sorts among join queries. For sharing join, additional modules have been tailored to the J-MOTH optimizer to optimize sharing work by exploiting shared pipelined multiway join among multiple multiway join queries. The experimental evaluation has demonstrated that the J-MOTH system outperforms the naive and the state-of-the-art techniques by 44% for query execution time using TPC-H queries. Also, the proposed J-MOTH system introduces maximal intermediate data size reduction by 30% in average over Hadoop-like infrastructures.

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Hash semi cascade join for joining multi-way map reduce

Cited by 7 publications

References 10 publications

An Approach to Deepfake Video Detection Based on ACO-PSO Features and Deep Learning

An Approach to Deepfake Video Detection Based on ACO-PSO Features and Deep Learning

Arabic Semantic Similarity Approach for Farmers’ Complaints

Exploiting Sharing Join Opportunities in Big Data Multiquery Optimization with Flink

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