Smart Intra-query Fault Tolerance for Massive Parallel Processing Databases

Ji, Yunhong; Chai, Yunpeng; Zhou, Xuan; Ren, Lipeng; Qin, Yajie

doi:10.1007/s41019-019-00114-z

Cited by 12 publications

(4 citation statements)

References 18 publications

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“…Optical flow allows for the recovery of geometric aspects of facial emotions and movement features, while wavelet decomposition minimizes noise. For typical ML approaches, such as DL methods, significant computational and memory resources are unnecessary, leading to the creation of embedded devices that can classify in real-time with minimal resources and produce appropriate outcomes [21][22][23][24][25][26][27][28][29][30][31][32][33][34].…”

Section: Existing Systemsmentioning

confidence: 99%

“…Weight sharing is a critical concept employed by CNNs that reduces the number of parameters needed to be prepared, resulting in smoother operations and avoiding overfitting [16,32]. Compared to artificial neural network (ANN) models, CNNs are easier to train for larger systems [36] and have been widely employed in various fields due to their impressive performance, including image classifiers [5,12], question placement [34], head-to-head position [34], speech recognition, facial recognition [23], vehicle recognition [30], diabetic retinopathy [29], and more. The purpose of this study is to train a hypothetical system that can improve understanding and comprehension of CNNs, discussing basic concepts, three popular models, learning algorithms, and new techniques like ADAM is introduced [11].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

See 1 more Smart Citation

Sustainable Facial Authentication and Expression Prediction using Deep Learning Techniques

Rajasekhar,

Sreekar,

Revanth

et al. 2023

E3S Web Conf.

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To make machines more human-like, computer vision is essential. Computer vision is a field that focuses on mimicking the capabilities of the human visual framework to capture high-level understanding from enhanced images or recordings. Such a computer vision application gives the machine the ability to recognize a person and detect their emotions in order to process them appropriately. Facial verification can be a process of identifying or validating an object through an image, video, or any audio-visual component of their face. It could be a biometric discriminant proof strategy that works directly and confronts measures of discriminating individuals through their facial design and biometric information. The innovation collects a unique set of biometric information from each individual regarding their face and facial expressions to identify an individual. Facial sensory recognition can be an innovation used to analyse estimates from a variety of sources, such as images and recordings. It makes a difference as machines get better the way humans do and treat them according to their emotions. We are using a deep learning computation called Convolutional Neural Networks (CNN) to prepare for this demonstration that determines the sentiment of certain input images. We need to pre-process the images to prepare and test the model. For pre-processing, we do image enhancement combining resizing, equalizing and converting the image to grayscale for the machine to achieve. This demonstration can have multiple applications in both surveillance and feedback systems.

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Section: Existing Systemsmentioning

confidence: 99%

Section: Convolutional Neural Networkmentioning

confidence: 99%

Sustainable Facial Authentication and Expression Prediction using Deep Learning Techniques

Rajasekhar,

Sreekar,

Revanth

et al. 2023

E3S Web Conf.

View full text Add to dashboard Cite

show abstract

“…The traditional database is more suitable for data transactional operation, while the system is mainly used to query massive arrival and departure report data, so the system uses Doris database. The database is an SQL analytical database system based on MPP technology, which can provide millisecond query response performance for arrival and departure reports [5]. The system uses 11 servers to build Doris database cluster.…”

Section: Architecture Of Data Storagementioning

confidence: 99%

Design and Implementation of Data Analysis System for Ship Arrival and Departure Report

Chen¹,

Chen²,

Lv³

2022

SJT

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In the context of building a smart maritime scene, through building a maritime smart service, improve the efficiency of ships entering and leaving the port, and reduce the waiting time for ships to load and unload goods in the port. In this paper, the basic function of querying the ship arrival and departure reports is provided, and the data of the ship arrival and departure reports are visually displayed by analyzing the data of the ship arrival and departure reports. Analyze the traffic of ships between provinces, and analyze the historical freight data of ships to draw a preliminary ship portrait, as to analyze and study the dependence of different provinces on goods and the positioning of provinces in the industrial chain. The back-end of the system is based on the spring boot framework, and the front-end uses the Vue framework to realize the front-end and back-end separate development of the system. For the storage and visualization of port entry and exit report data, the system selects Doris database cluster to store and manage historical port entry and exit report data. The open-source visualization chart Library of Echarts is used for visual drawing to realize the preliminary development and exploration of the data analysis system of ship entry and exit report.

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“…Data quality has attracted increasing attention since it is pivotal to promoting data management and analysis technology, optimize the output of big data and artificial intelligence, and reduce the restrictions on subsequent analysis and research caused by data problems. Ji et al [12] put forward the query and fault-tolerance mechanism, but this mechanism must compromise on fault-tolerance, performance and implementation cost, without dealing with the data usage outside the scope of data query. In addition, many researchers have introduced a variety of detection and repairing techniques for data anomalies.…”

Section: Related Workmentioning

confidence: 99%

Time Series Data Cleaning under Multi-Speed Constraints

Gao¹,

Gao²,

Song³

et al. 2021

International Journal of Software and Informatics

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As the basis of data management and analysis, data quality issues have increasingly become a research hotspot in related fields, which contributes to optimization of big data and artificial intelligence technology. Generally, physical failures or technical defects in data collectors and recorders cause anomalies in collected data. These anomalies will strongly impact on subsequent data analysis and artificial intelligence processes; thus, data should be processed and cleaned accordingly before application. Existing repairing methods based on smoothing will cause a large number of originally correct data points being over-repaired into wrong values. The constraint-based methods such as sequential dependency and SCREEN cannot accurately repair data under complex conditions since the constraints are relatively simple. A time series data repairing method under multi-speed constraints is further proposed based on the principle of minimum repairing. Then, dynamic programming is used to solve the problem of data anomalies with optimal repairing. Specifically, multiple speed intervals are set to constrain time series data, and a series of candidate repairing points are formed for each data point according to the speed constraints. Next, the optimal repair solution is selected from these candidates based on the dynamic programming method. With regard to the feasibility study of this method, an artificial dataset, two real datasets, and another real dataset with real anomalies are employed for experiments in case of different rates of anomalies and data sizes. Experimental results demonstrate that, compared with the existing methods based on smoothing or constraints, the proposed method has better performance in terms of RMS errors and time cost. In addition, the investigation of clustering and classification accuracy with several datasets reveals the impact of data quality on subsequent data analysis and artificial intelligence. The proposed method can improve the quality of data analysis and artificial intelligence results.

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Smart Intra-query Fault Tolerance for Massive Parallel Processing Databases

Cited by 12 publications

References 18 publications

Sustainable Facial Authentication and Expression Prediction using Deep Learning Techniques

Sustainable Facial Authentication and Expression Prediction using Deep Learning Techniques

Design and Implementation of Data Analysis System for Ship Arrival and Departure Report

Time Series Data Cleaning under Multi-Speed Constraints

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