Concurrent Data Processing Research Articles

Efficient Edge Computing: A Survey of High-Throughput Concurrent Processing Strategies for Graph Data

This paper reviews the strategies for high-throughput concurrent processing of graph data in edge computing environments. As information technology rapidly advances, particularly in areas such as the Internet of Things (IoT), smart cities, and autonomous driving, the need for real-time and efficient data processing continues to grow. Edge computing is a distributed computing paradigm that can process data at or near its origin, thereby reducing network latency, improving application performance, and reducing the load on central data centers. The discussion includes the application of edge computing in graph data processing, highlighting parallel computing models such as the Bulk Synchronous Parallel (BSP) model and other parallel optimization techniques like data partitioning and task scheduling. The paper also addresses specific challenges of parallel processing in edge computing, such as resource constraints, data communication delays, and strategies for security and privacy protection. Despite the significant potential edge computing has demonstrated in processing graph data, numerous challenges remain. Future research directions involve the development of new resource optimization algorithms, low-latency communication protocols, and technologies to enhance data security, aiming to achieve more efficient and secure graph data processing. This paper provides clear directions and a foundation for the efficient implementation of graph data processing in edge computing environments, positioning edge computing to play an increasingly significant role in real-time data analysis and intelligent applications.

Data Transmission Control Method of Electrical Equipment of Automobile Based on the 5G Communication Technology

Conflicts emerge when the data transmission occurs between electrical equipment in an automobile. Hence, transmission delays appear as issues. For this reason, a method, called data transmission control, for electrical equipment of automobiles based on the 5G communication technology is proposed in this paper. Firstly, the wavelet feature decomposition was utilized to partition the frequency spectrum, and thus the statistical characteristics of electrical equipment of automobiles concerning the transmitted data were obtained. Then, the high-order approximate distribution method was adopted to construct a channel for a 5G network data transmission. Afterward, the control logic structure of the data transmission was built, and the problem, called the conflict of the data transmission, was alleviated through concurrent data collection and processing methods. On this basis, coding coefficients of a constructed global coding matrix were selected to encode and transmit source information. Also, the number of redundant data packets at each layer was adjusted. Finally, the data transmission control of the electrical equipment of the automobile was realized through the linear combination of the network nodes. The simulation results showed that the throughput of the proposed method was always higher than 7.7 MB/s, the bit error rate was around 0 when the signal-to-noise ratio was lower than 3, and the transmission delay was always below 0.5 s, which could provide a reference for the efficient and safe operation of automobiles.

Statement of retraction: key technologies of massive concurrent data processing in smart city based on cloud computing

This article refers to:RETRACTED ARTICLE: Key technologies of massive concurrent data processing in smart city based on cloud computing

Management of medical and health big data based on integrated learning-based health care system: A review and comparative analysis

PurposeWe present a Health Care System (HCS) based on integrated learning to achieve high-efficiency and high-precision integration of medical and health big data, and compared it with an internet-based integrated system. MethodThe method proposed in this paper adopts the Bagging integrated learning method and the Extreme Learning Machine (ELM) prediction model to obtain a high-precision strong learning model. In order to verify the integration efficiency of the system, we compare it with the Internet-based health big data integration system in terms of integration volume, integration efficiency, and storage space capacity. ResultsThe HCS based on integrated learning relies on the Internet in terms of integration volume, integration efficiency, and storage space capacity. The amount of integration is proportional to the time and the integration time is between 170-450 ms, which is only half of the comparison system; whereby the storage space capacity reaches 8.3×28TB. ConclusionThe experimental results show that the integrated learning-based HCS integrates medical and health big data with high integration volume and integration efficiency, and has high space storage capacity and concurrent data processing performance.

Design of Monitoring System for Electric Vehicle Charging Facilities Based on Cloud Platform

Faced with the increasingly severe pressure of traditional energy consumption and environmental problems, electric vehicles have become the hotspot and trend of the development of the world automobile industry due to their energy-saving and environmental protection characteristics. The cloud platform in the paper was introduced into the monitoring system of electric vehicle charging facilities, and a model of electric vehicle charging facility monitoring system based on cloud platform was proposed. Aiming at the difficulty of charging facility monitoring system in large concurrent data processing, a M/M/C queuing theory model was presented. Data concurrency control algorithm. The transmission process of the data frame in the monitoring system was analyzed, and the concurrent processing algorithm was applied to the large charging station for analysis experiments. The experimental results showed that the proposed algorithm not only satisfied the real-time and concurrency requirements of data frame transmission, but also proved that the gateway optimization method could accurately derive the optimal number of gateways.

An Efficient Machine Learning Techniques Based on IoT for Effective Load Forecasting

Internet of Things (IoT) networks (IoT) are computer networks which have an acute IT protection problem and in particular a computer attack detection problem. In order to solve this issue, the paper recommends the combination of machine learning approaches and concurrent data processing. The framework is developed and a new approach to the combination of the main classifiers intended for attacks on IoT networks. In which the accuracy ratio to the training time is the integral measure of efficacy, the problem classification statement is developed. We recommend the use of the data processing and multithreaded mode provided by Spark to accelerate the speed of training and testing. In addition, a technique is suggested for preprocessing data set, which results in a large reduction in the sample length. An experimental examination of the proposed approach reveals that the precision of IoT networks’ attack detection is 100 percent and the processing speed of data sets increases proportionally to the number of parallel threads.

Retracted Article: Key technologies of massive concurrent data processing in smart city based on cloud computing

We, the Editor and Publisher of the International Journal of Computers and Applications, have retracted the following article which was part of the Special Issue on Advanced Security Techniques for Cloud Computing and Big Data - New Directions: Yongqiang He & Xuyang Zhang (2020) Key technologies of massive concurrent data processing in smart city based on cloud computing, International Journal of Computers and Applications, DOI: 10.1080/1206212X.2020.1743040 Since publication, it came to our attention that the articles published in this Special Issue were not reviewed fully in line with the journal's peer review standards and policy. We did not find any evidence of misconduct by the authors. However, in order to ensure full assessment has been conducted, we sought expert advice on the validity and quality of the published articles from independent peer reviewers. Following this post publication peer review, the Editor has determined that the articles do not meet the required scholarly standards to remain published in the journal, and therefore has taken the decision to retract. The concerns raised have been shared with the authors and they have been given the opportunity to respond. The authors have been informed about the retraction of the article. We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions. The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’.

Design and implementation of distributed asynchronous data aided computer information interaction system

Under the influence of novel corona virus pneumonia epidemic prevention and control, it has put forward higher requirements for data storage and processing for personnel management system. The distributed asynchronous data aided computer information interaction system can solve the problem of multi node concurrent data processing. The traditional computer information interaction system has poor real-time performance, low precision and asynchronous data processing ability. The invocation features of message queuing asynchronous caching mode are combined with the standardization of Web services and cross language with cross platform access features in this paper. Through the combination of the two technologies, a flexible and universal asynchronous interaction architecture of distributed system is established. Based on Web service technology and system to system access, the call and response of tasks between modules are carried out in the system, which makes the interaction between the whole system have the characteristics of message driven. The test result shows that the system proposed in this paper has good real-time performance and strong data processing ability. It is suitable for the data interaction of distributed personal management system under the influence of novel corona virus pneumonia epidemic prevention and control.

Real-Time Middleware for Cyber-Physical Event Processing

Cyber-physical systems (CPS) involve tight integration of cyber (computation) and physical domains, and both the effectiveness and correctness of a cyber-physical system application may rely on successful enforcement of constraints such as bounded latency and temporal validity subject to physical conditions. For many such systems (e.g., edge computing in the Industrial Internet of Things), it is desirable to enforce such constraints within a common middleware service (e.g., during event processing). In this article, we introduce CPEP, a new real-time middleware for <underline>c</underline>yber-<underline>p</underline>hysical <underline>e</underline>vent <underline>p</underline>rocessing, with (1) extensible support for complex event processing operations, (2) execution prioritization and sharing, (3) enforcement of time consistency with load shedding, and (4) efficient memory management and concurrent data processing. We present the design, implementation, and empirical evaluation of CPEP and show that it can (1) support complex operations needed by many applications, (2) schedule data processing according to consumers’ priority levels, (3) enforce temporal validity, and (4) reduce processing delay and improve throughput of time-consistent events.

Real-time collaborative reconstruction of digital building models with mobile devices

Many software-based building processes require digital building models. Since the building stock does not have sufficient data in this regard, the demand for Scan-to-BIM processes is increasing. In this paper we present a system for the reconstruction of ‘as-built’ BIM content of house interiors based on the Google Tango technology. The strength of our approach is the use of low-cost mobile scanning devices and a client-server system that allows for a real-time collaborative scanning and reconstruction of indoor scenes. We developed a server application that continuously aggregates scan data of multiple scanning devices (clients) and applies the data stream to a real-time post-processing pipeline to reconstruct rooms, walls, doors and windows. The reconstruction result is then distributed to all clients, where it is visualized in real time. The collaborative workflow and real-time data processing make our system especially useful in situations that are time-critical and require concurrent collection and processing of data. One of our targeted use cases therefore is the model generation for crime scene documentation. The effectiveness of our approach was demonstrated on three test sites. Our results compare well to other state-of-art methods regarding the reconstruction of walls, but they also revealed potential for improvement regarding the detection of doors and windows in occluded and cluttered environments.

Mobile GPU-based implementation of automatic analysis method for long-term ECG

BackgroundLong-term electrocardiogram (ECG) is one of the important diagnostic assistant approaches in capturing intermittent cardiac arrhythmias. Combination of miniaturized wearable holters and healthcare platforms enable people to have their cardiac condition monitored at home. The high computational burden created by concurrent processing of numerous holter data poses a serious challenge to the healthcare platform. An alternative solution is to shift the analysis tasks from healthcare platforms to the mobile computing devices. However, long-term ECG data processing is quite time consuming due to the limited computation power of the mobile central unit processor (CPU).MethodsThis paper aimed to propose a novel parallel automatic ECG analysis algorithm which exploited the mobile graphics processing unit (GPU) to reduce the response time for processing long-term ECG data. By studying the architecture of the sequential automatic ECG analysis algorithm, we parallelized the time-consuming parts and reorganized the entire pipeline in the parallel algorithm to fully utilize the heterogeneous computing resources of CPU and GPU.ResultsThe experimental results showed that the average executing time of the proposed algorithm on a clinical long-term ECG dataset (duration 23.0 ± 1.0 h per signal) is 1.215 ± 0.140 s, which achieved an average speedup of 5.81 ± 0.39× without compromising analysis accuracy, comparing with the sequential algorithm. Meanwhile, the battery energy consumption of the automatic ECG analysis algorithm was reduced by 64.16%. Excluding energy consumption from data loading, 79.44% of the energy consumption could be saved, which alleviated the problem of limited battery working hours for mobile devices.ConclusionThe reduction of response time and battery energy consumption in ECG analysis not only bring better quality of experience to holter users, but also make it possible to use mobile devices as ECG terminals for healthcare professions such as physicians and health advisers, enabling them to inspect patient ECG recordings onsite efficiently without the need of a high-quality wide-area network environment.

Harnessing Multi-Source Data about Public Sentiments and Activities for Informed Design

The intelligence of Smart Cities (SC) is represented by its ability in collecting, managing, integrating, analyzing, and mining multi-source data for valuable insights. In order to harness multi-source data for an informed place design, this paper presents “Public Sentiments and Activities in Places” multi-source data analysis flow (PSAP) in an Informed Design Platform (IDP). In terms of key contributions, PSAP implements 1) an Interconnected Data Model (IDM) to manage multi-source data independently and integrally, 2) an efficient and effective data mining mechanism based on multi-dimension and multi-measure queries (MMQs), and 3) concurrent data processing cascades with Sentiments in Places Analysis Mechanism (SPAM) and Activities in Places Analysis Mechanism (APAM), to fuse social network data with other data on public sentiment and activity comprehensively. As proved by a holistic evaluation, both SPAM and APAM outperform compared methods. Specifically, SPAM improves its classification accuracy gradually and significantly from 72.37 to about 85 percent within nine crowd-calibration cycles, and APAM with an ensemble classifier achieves the highest precision of 92.13 percent, which is approximately 13 percent higher than the second best method. Finally, by applying MMQs on “Sentiment&Activity Linked Data”, various place design insights of our testbed are mined to improve its livability.

Massive Concurrent Data Processing Technology in the Web System

Starting with the high concurrent, massive data requirements in the web system, the analysis of the system structure of the cache technology, module processing technology, thread technology and the appropriate application of indexing technology to solve the large number of concurrent data real-time processing problems. To meet the high load, high concurrency, large amount of data to improve the overall system performance requirements for the effective solution to large quantities of concurrent data processing provides a convenience.

Deep Temporal Multimodal Fusion for Medical Procedure Monitoring Using Wearable Sensors

Process monitoring and verification have a wide range of uses in the medical and healthcare fields. Currently, such tasks are often carried out by a trained specialist, which makes them expensive, inefficient, and time-consuming. Recent advances in automated video- and multimodal-data-based action and activity recognition have made it possible to reduce the extent of manual intervention required to effectively carry out process supervision tasks. In this paper, we propose algorithms for automated egocentric human action and activity recognition from multimodal data, with a target application of monitoring and assisting a user perform a multistep medical procedure. We propose a supervised deep multimodal fusion framework that relies on concurrent processing of motion data acquired with wearable sensors and video data acquired with an egocentric or body-mounted camera. We demonstrate the effectiveness of the algorithm on a public multimodal dataset and conclude that automated process monitoring via the use of multiple heterogeneous sensors is a viable alternative to its manual counterpart. Furthermore, we demonstrate that the application of previously proposed adaptive sampling schemes to the video processing branch of the multimodal framework results in significant performance improvements.

Massive Concurrent Data Processing Technology in the Web System

Massive Concurrent Data Processing Technology in the Web System

Special issue on Big Data Security and Intelligent Data in Clouds (BDS‐IDC)

Special issue on Big Data Security and Intelligent Data in Clouds (<scp>BDS‐IDC</scp>)

Passive acoustic source localization with multiple horizontal arrays in shallow water

This paper considers concurrent matched-field processing of data from multiple, spatially separated acoustic arrays with application to towed-source data received on two bottom-moored horizontal line arrays from the SWellEx-96 shallow water experiment. Matched-field processors are derived for multiple arrays and multiple-snapshot data using maximum-likelihood estimates for unknown complex-valued source strengths and unknown error variances. Starting from a coherent processor where phase and amplitude is known between all arrays, likelihood expressions are derived for various assumptions on relative source spectral information (amplitude and phase at different frequencies) between arrays and from snapshot to snapshot. Processing the two arrays with a coherent-array processor (with inter-array amplitude and phase known) or with an incoherent-array processor (no inter-array spectral information) both yield improvements in localization over processing the arrays individually. The best results with this data set were obtained with a processor that exploits relative amplitude information but not relative phase between arrays. The localization performance improvement is retained when the multiple-array processors are applied to short arrays that individually yield poor performance.

Cloud Computing and Big Data for Genomics: A Review

The Human Genome Project has led to the massive growth and deluge of genomic sequencing data. Though new methods have decreased gene sequencing costs, this has increased the need of large-scale genomic data analysis. Such data sets are valuable and informative for scientists and medical researchers for extracting valuable associations about genes and diseases. As a result, proficient technologies capable of handling such biological big data are the need of the hour. Cloud computing and big data both serve to manage, store and analyze such data. This is because such technologies offer concurrent and distributed data processing for evaluating petabyte (PB) range genomic data sets. This paper provides a review and comparative study of cloud computing and big data approaches in Genomics that have been proposed in the last decade. This paper can be used by other researchers to develop more futuristic and robust data genomic storage systems in the future. Keywords: cloud computing; big data; genomics; gene sequencing; bioinformatics

Multiple-array passive acoustic source localization in shallow water.

This paper considers concurrent matched-field processing of data from multiple, spatially-separated acoustic arrays with application to towed-source data received on two bottom-moored horizontal line arrays from the SWellEx-96 shallow water experiment. Matched-field processors are derived for multiple arrays and multiple-snapshot data using maximum-likelihood estimates for unknown complex-valued source strengths and unknown error variances. Starting from a coherent processor where phase and amplitude is known between all arrays, likelihood expressions are derived for various assumptions on relative source spectral information (amplitude and phase at different frequencies) between arrays and from snapshot to snapshot. Processing the two arrays with a coherent-array processor (with inter-array amplitude and phase known) or with an incoherent-array processor (no inter-array spectral information) both yield improvements in localization over processing the arrays individually. The best results with this data set were obtained with a processor that exploits relative amplitude information but not relative phase between arrays. The localization performance improvement is retained when the multiple-array processors are applied to short arrays that individually yield poor performance.

Running state of the high energy consuming equipment and energy saving countermeasure for chinese petroleum industry in cloud computing

SummaryThe energy consumption of the high energy consuming petroleum equipment in oil field possesses a high proportion in the total energy consumption of oil industry, so these equipment act as an important role in oil field energy saving. This paper analyzes the energy consumption constitutions in different production process. It is based on the study focusing on the running state and energy saving countermeasure of the high energy consuming mechanical equipment. The research is organized by the Chinese Academy of Engineering. The high energy consumption cause is studied by investigating and analyzing the running state of the principle energy consuming equipment in oil field and the design and manufacture level of the key large equipment in cloud computing. This thesis provides concurrent data processing model for shortening the production cycle and the fuzzy weighting subspace clustering algorithm to implement the equipment comparability, applying the big data on the running status statistics and applying cloud computation analysis on the corresponding energy saving countermeasure. The proposed approach is providing the method of eliminating equipment and the advice of the research directions in order to address the energy saving counter measure issues. The findings of this paper can be referenced as the energy saving counter measure in the petroleum industry. Copyright © 2016 John Wiley &amp; Sons, Ltd.