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作者： Hall, R. C. Sandia National Labs. Albuquerque NM (USA)

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Architecture and Algorithm Design for Civil Aviation data Real-Time Analysis System

IEEE ACCESS 2024年 12卷 66382-66397页

作者： Zhang, Yifeng Xi, Qi Wang, Jing Gu, Shuhuai South China Normal Univ Sch Data Sci & Engn Shanwei 516600 Peoples R China

Currently, the analysis of civil aviation flight data relies primarily on post-flight data. This involves transmitting onboard data to the servers of the airline and aircraft manufacturer after the aircraft has landed, and the engines have been shut down, using ground-based cellular stations or wireless hotspots. Real-time data analysis during flight remains a challenge. With the advent of integrated communication networks in the aerospace sector, the real-time transmission of onboard data is poised to become a future trend. But there is a gap in real-time civil aviation data analysis system. This study is based on Kappa distributed computing architecture, established a real-time aviation data analysis system, which inputs Quick Access Recorder (QAR) data stream and achieves fast data decoding and multiple real-time analysis algorithms, including flight trajectory outlier repair, flight phase identification, steady cruising state determination, and engine lubricating oil quantity monitoring. The results of simulation experiments indicate that the maximum average latency for individual algorithms is consistently below 250 milliseconds, meeting real-time performance requirements. And all real-time algorithms have similar performances with or better than existing excellent post-flight algorithms. Specifically, the flight trajectory outlier repair algorithm can repair all outliers and cost 0.2ms to process an outlier. The flight phase identification achieved 97.8% accuracy referring to commercial software's results. The steady cruising state determination exhibits computational efficiency approximately five times faster than the naive method. The proposed engine lubricating oil quantity monitoring method based on the Clustering Optimized Transformer (CO-Transformer) neural network yields an average mean square error of approximately 0.057 L2 between predicted and actual values, respectively 7.36%, 66.01% and 66.05% better than common Transformer, LSTM and GRU neural net

关键词： distributed data processing machine learning aviation data analysis

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On Fault Tolerance for distributed Iterative dataflow processing

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IEEE TRANSACTIONS ON KNOWLEDGE AND data ENGINEERING 2017年第8期29卷 1709-1722页

作者： Xu, Chen Holzemer, Markus Kaul, Manohar Soto, Juan Markl, Volker TU Berlin Database Syst & Informat Management DIMA Grp D-10623 Berlin Germany TU Berlin D-10623 Berlin Germany TU Berlin DIMA Grp D-10623 Berlin Germany Acellere D-60325 Frankfurt Hessen Germany IIT Hyderabad Comp Sci & Engn Dept Telangana 502285 India

Large-scale graph and machine learning analytics widely employ distributed iterative processing. Typically, these analytics are a part of a comprehensive workflow, which includes data preparation, model building, and model evaluation. General-purpose distributed dataflow frameworks execute all steps of such workflows holistically. This holistic view enables these systems to reason about and automatically optimize the entire pipeline. Here, graph and machine learning analytics are known to incur a long runtime since they require multiple passes over the data until convergence is reached. Thus, fault tolerance and a fast-recovery from any intermittent failure is critical for efficient analysis. In this paper, we propose novel fault-tolerant mechanisms for graph and machine learning analytics that run on distributed dataflow systems. We seek to reduce checkpointing costs and shorten failure recovery times. For graph processing, rather than writing checkpoints that block downstream operators, our mechanism writes checkpoints in an unblocking manner that does not break pipelined tasks. In contrast to the conventional approach for unblocking checkpointing (e.g., that manage checkpoints independently for immutable datasets), we inject the checkpoints of mutable datasets into the iterative dataflow itself. Hence, our mechanism is iteration-aware by design. This simplifies the system architecture and facilitates coordinating checkpoint creation during iterative graph processing. Moreover, we are able to rapidly rebound, via confined recovery, by exploiting the fact that log files exist locally on healthy nodes and managing to avoid a complete recomputation from scratch. In addition, we propose replica recovery for machine learning algorithms, whereby we employ a broadcast variable that enables us to quickly recover without having to introduce any checkpoints. In order to evaluate our fault tolerance strategies, we conduct both a theoretical study and experimental analyses us

关键词： Fault tolerance distributed data processing iterative computation graph processing machine learning analytics

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SOME ASPECTS OF PARALLEL AND distributed ITERATIVE ALGORITHMS - A SURVEY

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AUTOMATICA 1991年第1期27卷 3-21页

作者： BERTSEKAS, DP TSITSIKLIS, JN MIT DEPT ELECT ENGN & COMP SCICAMBRIDGEMA 02139

We consider iterative algorithms of the form x:=f(x), executed by a parallel or distributed computing system. We first consider synchronous executions of such iterations and study their communication requirements, as well as issues related to processor synchronization. We also discuss the parallelization of iterations of the Gauss-Seidel type. We then consider asynchronous implementations whereby each processor iterates on a different component of x, at its own pace, using the most recently received (but possibly outdated) information on the remaining components of x. While certain algorithms may fail to converge when implemented asynchronously, a large number of positive convergence results is available. We classify asynchronous algorithms into three main categories, depending on the amount of asynchronism they can tolerate, and survey the corresponding convergence results. We also discuss issues related to their termination.

关键词： COMPUTATIONAL METHODS distributed data processing ITERATIVE METHODS PARALLEL processing ASYNCHRONOUS ALGORITHMS PARALLEL ALGORITHMS distributed ALGORITHMS

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BPL - A SET-BASED LANGUAGE FOR distributed SYSTEM PROTOTYPING

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INTERNATIONAL JOURNAL OF COMPUTER & INFORMATION SCIENCES 1985年第2期14卷 83-105页

作者： HOOPER, JW 1. Computer Science Department The University of Alabama in Huntsville 35899 Huntsville Alabama

Rapid prototyping of software and hardware is recognized as a very important step in timely, cost-effective system development. The complexity inherent in designing distributed computing systems and distributed problem solutions emphasizes the need for effective rapid prototyping tools. ADL/ADS is a testbed user interface tool for experimentation with critical research and design issues pertaining to distributed data processing (DDP). The experimenter expresses a candidate distributed system in terms of “experiment objects”, along with attributes and relationships. The experiment objects represent hardware, software, and “behavior”. This paper provides a brief overview of ADL/ADS, and gives especial emphasis to the Behavior Prototyping Language (BPL) within ADL/ADS. BPL is designed for expressing Behavior Modules (B_MODULEs)—the most versatile of the behavior objects. BPL incorporates set-theoretical approaches, and provides syntax designed for effectively describing manipulations of experiment objects, and their attributes and relationships.

关键词： Rapid prototyping distributed data processing set-theoretical language entity-attribute-relationship model ADL BPL

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Architectures and algorithms for track association and fusion

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IEEE AEROSPACE AND ELECTRONIC SYSTEMS MAGAZINE 2000年第1期15卷 5-13页

作者： Chong, CY Mori, S Barker, WH Chang, KC Booz Allen & Hamilton Inc San Francisco CA 94111 USA Raytheon Syst Co Adv C3I Syst San Jose CA 95126 USA George Mason Univ Dept Syst Engn Fairfax VA 22030 USA

Target tracking using multiple sensors can provide better performance than using a single sensor. One approach to multiple target tracking with multiple sensors is to first perform single sensor tracking and then fuse the tracks from the different sensors. Two processing architectures for track fusion are presented: sensor to sensor track fusion, and sensor to system track fusion. Technical issues related to the statistical correlation between track estimation errors are discussed. Approaches for associating the tracks and combining the track state estimates of associated tracks that account for this correlation are described and compared by both theoretical analysis and Monte Carlo simulations.

关键词： sensor fusion target tracking distributed tracking/fusion distributed data processing

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DPFMDA: distributed and privatized framework for miRNA-Disease association prediction

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PATTERN RECOGNITION LETTERS 2018年第Jul.15期109卷 4-11页

作者： Chen, Lixin Liu, Bingtao Yan, Chenggang Hangzhou Dianzi Univ Hangzhou Zhejiang Peoples R China

In this paper, we developed a novel distributed and privatized framework for miRNA-disease association prediction (DPFMDA) to predict potential miRNA-disease associations. DPFMDA assumes that diseases and miRNAs can be represented as feature matrices. Through independent factorization, collaborative analysis and matrix re-factorization, feature matrices are estimated and potential associations in miRNA-disease association matrix are predicted. The proposed computational model is tested and verified by leave-one-out cross validation (LOOCV). The AUCs of global and local LOOCV are 0.8859 and 0.8267 respectively, which outperform the state-of-the-art computational models. DPFMDA is further evaluated with case studies of four important human complex diseases. We test Esophageal Neoplasms and Prostate Neoplasms using DPFMDA in the HMDD v2.0 database. For Breast Neoplasms, we use the HMDD v2.0 database which removes all the known miRNAs-disease associations on Breast Neoplasms. For Hepatocellular Carcinoma, HMDD v1.0 database is used as the input of DPFMDA and predicted results are verified with HMDD v2.0, dbDEMC and miR2Disease databases. The results show that 90% (Esophageal Neoplasms), 88% (Prostate Neoplasms), 98% (Breast Neoplasms) and 86% (Hepatocellular Carcinoma) of top 50 predicted miRNA-disease associations are confirmed by recent experimental studies respectively. It is anticipated that DPFMDA would be an effective and extensible method for potential miRNA-disease association prediction. (C) 2017 Elsevier B.V. All rights reserved.

关键词： miRNA-disease association prediction Computational model distributed data processing

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INTEGRATED DECENTRALIZED FLEXIBLE data-processing IN PATHOLOGY

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MEDICAL INFORMATICS 1982年第4期7卷 307-314页

作者： LOY, V Institut für Pathologie Klinikum Steglitz Freie Universität Berlin D 1000 Berlin 45 FR Hindenburgdamm 30 Germany

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关键词： distributed data processing Pathology Neuropathology Autopsy Biopsy Cytology Natural language system Coding System Morbid anatomy Histopathology

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A Performance Evaluation of the Synchronized Provisioning with an Adaptive Buffer Resilience Scheme over Grid Networks

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IEEE COMMUNICATIONS LETTERS 2012年第4期16卷 557-559页

作者： Kim, Byungsang Youn, Chan-Hyun Korea Adv Inst Sci & Technol Dept Informat & Commun Engn Taejon 305701 South Korea Korea Adv Inst Sci & Technol Dept Elect Engn Taejon 305701 South Korea

For widely distributed data analysis applications over that run the Internet, both the instability of the data transfer time and the dynamics of data processing rate require a more sophisticated data provisioning scheme to maximize parallel efficiency, in particular, under conditions in which real-time and limited data buffer (storage) constraints are given. In this letter, we propose a synchronized data provisioning scheme that implicitly avoids the data buffer overflow as well as explicitly controls the data buffer underflow by optimally adjusting the buffer resilience. In order to guarantee the designated quality of service, we further exploit an adaptive buffer resilience control algorithm based on sample path analysis of the state of the data buffer and the demand queue. The simulation results show that the proposed scheme is suitably efficient to apply to an environment that can not postulate the stochastic characteristics of the data transfer time and data processing rate.

关键词： Grid network distributed data processing parallel efficiency data provisioning buffer resilience

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A self-adapted swarm architecture to handle big data for "factories of the future"

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IFAC-PapersOnLine 2019年第13期52卷 916-921页

作者： Guerreiro, Guilherme Costa, Ruben Figueiras, Paulo Graça, Diogo Jardim-Gonçalves, Ricardo CTS UNINOVA Dep. de Eng. Eletrotécnica Faculdade de Ciências e Tecnologia FCT Universidade Nova de Lisboa Caparica2829-516 Portugal Volkswagen Autoeuropa Quinta da Marquesa Quinta do Anjo2954-024 Portugal

Currently, the manufacturing sector is facing a technological evolution with the so-called Industry 4.0. This poses a paradigm shift, enabling companies to be more competitive by taking advantage of innovative technologies(cloud computing, cyber-physical systems, big data analytics and deep learning), pursuing near-zero fault, near real-time reactivity to any problem, better traceability, more predictability in manufacturing, while working to achieve cheaper product customization. The challenges arise when the dimensionality of the data generated by manufacturing processes grows, affecting the performance of algorithms, decreasing it quickly as the dimension of the search space increases. Handling large datasets with a good performance in a limited time should be the main concern in Big data analytics. This paper focuses on a logistic process of car manufacturing, where batteries are unloaded from trucks to warehouse, and then to the point of fit, where they are assembled into the car. It presents a complete data-driven architecture, using a swarm approach for distributed data processing among all data stages, where processing nodes with different tasks and technologies can work cooperatively to complete a job. The work presented in this paper is funded by the EU project BOOST4.0, focusing on a smart manufacturing scenario for the automotive sector. © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Manufacturing data processing Advanced Analytics Automotive industry Big data Cyber Physical System data Analytics data handling Deep learning Embedded systems Engineering education Large dataset Manufacture Real time systems Swarm intelligence data driven architectures distributed data processing Factories of the futures Industry4.0 Innovative technology Performance of algorithm Product customization Technological evolution

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