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An edge streaming data processing framework for autonomous driving

CONNECTION SCIENCE 2021年第2期33卷 173-200页

作者： Zhao, Hang Yao, LinBin Zeng, ZhiXin Li, DongHua Xie, JinLiang Zhu, WeiLing Tang, Jie South China Univ Technol Sch Comp Sci & Engn Guangzhou Peoples R China

In recent years, with the rapid development of sensing technology and the Internet of Things (IoT), sensors play increasingly important roles in traffic control, medical monitoring, industrial production and etc. They generated high volume of data in a streaming way that often need to be processed in real time. Therefore, streaming data computing technology plays an indispensable role in the real-time processing of sensor data in high throughput but low latency. However, there are two problems in deploying streaming data process ability in cloud computing data centre. Firstly, massive sensor nodes simultaneously upload data to the remote cloud computing data centre, which requires a large number of bandwidth resources supports. The existing network infrastructure cannot provide enough bandwidth at a reasonable price. Secondly, due to the geographical distribution characteristics of the cloud computing data centre, there will inevitably be large transmission delay during the process of data transmission. Such end-to-end delay is intolerable to mobile applications especially for those latency sensitive tasks. In view of the above problems, this paper proposes an autonomous driving oriented edge streaming data processing framework, which migrates the computing and storage capability from the remote cloud data centre to the edge data centre. It focuses on the change of vehicle flow in a specific geographical area, and uses the computing power sunk to edge node to process the massive streaming data generated by autonomous vehicles nearby. The proposed framework is implemented on top of Spark streaming, which builds up a gray model based traffic flow monitor, a traffic prediction orientated prediction layer and a fuzzy control based Batch Interval dynamic adjustment layer for Spark streaming. It could forecast the variation of sensors data arrive rate, make streaming Batch Interval adjustment in advance and implement real-time streaming process by edge. Therefore, it can

关键词： Edge computing streaming data processing spark streaming autonomous driving

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Feature-based Dividing Symbolic Time Series Representation for streaming data processing 9

Feature-based Dividing Symbolic Time Series Representation f...

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9th International Conference on Information Technology in Medicine and Education (ITME)

作者： Zhan, Peng Hu, Yupeng Zhang, Qi Zheng, Jiecai Li, Xueqing Shandong Univ Sch Software Jinan Shandong Peoples R China Shandong Sprot Univ Sch Sport Commun & Informat Technol Jinan Shandong Peoples R China

ISBN: (纸本)9781538677438

Along with the continuous development of information technology, massive numbers of detecting instruments or systems in various fields are continuously producing plenty number of streaming time series data. In recent years, many representation approaches for time series has been proposed with the main objective of dimensionality reduction to support various data mining algorithms in the domain of time series data processing. Symbolic Aggregate approXimation(SAX) is a major symbolic representation and dimensionality reduction algorithm which has been widely used in many application scenarios of time series data mining, such as motifs discovery, outlier detection, etc. In this paper, we propose a symbolic representation method of streaming time series based on VTPdiving with sliding window and a similarity measurement algorithm for the proposed representation method which lower bounding the Euclidean distance on the original data. Extensive experiments on different kinds of typical time series datasets have been conducted to demonstrate the advantages of our proposed method.

关键词： Symbolic Aggregate Approximation Time Series Dimensionality Reduction streaming data processing

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Detecting Anomalous Users via streaming data processing in Smart Grid

Detecting Anomalous Users via Streaming Data Processing in S...

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作者： Yu Kang Xinting Wang Xiu Cao Yangfan Zhou Zhichao Lai Yuhao Li Xuqi Zhang Wei Geng School of Computer Science Fudan University China Shanghai Jiao Tong University School of Computer Science Fudan University Shanghai Inspection &Maintenance of Power Company South Kent School

Anomalous user detection is an important concern in smart grid. Conduct the anomaly detection in real time with streaming data processing technology is a hot research field for smart grid maintenance. We first study the characteristics of power measurement data. Based on our findings, we adopt Clustream, an outlier detection model, for finding anomalous users. Combined with grid based DBSCAN algorithm, we achieve a two-phase flow processing architecture including offline and online phases for the goal of detecting anomalous user. The implementation details of our system are introduced. The experimental results show that our system can achieve better detection results.

关键词： Smart grid operation anomaly detection streaming data processing energy theft

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Developing a streaming data processing workflow for querying space-time activities from geotagged tweets

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COMPUTERS ENVIRONMENT AND URBAN SYSTEMS 2016年 59卷 256-268页

作者： Wachowicz, Monica Arteaga, M. Dolores Cha, Sangwhan Bourgeois, Yves Univ New Brunswick People Mot Lab Fredericton NB E3B 5A3 Canada Univ New Brunswick Urban & Community Studies Inst Fredericton NB E3B 5A3 Canada

The critical dimensions in describing space-time activities are "what", "where", "when", and "who", which are frequently applied to collect data about basic functions people per... 详细信息

The critical dimensions in describing space-time activities are "what", "where", "when", and "who", which are frequently applied to collect data about basic functions people perform in space in the course of a day. Collecting data about these dimensions using activity-based surveys has presented researchers with a number of technical and social limitations, ranging from the restricted period of time participants have to record their activities to the level of accuracy with which participants complete a survey. This paper, proposes a new streaming data processing workflow for querying space-time activities (STA) as a by-product of microblogging communication. It allows exploring a large volume of geotagged tweets to discover STA patterns of daily life in a systematic manner. A sequence of tasks have been implemented using different cloud-based computing resources for handling over one million of daily geotagged tweets from Canada for a period of six months. The STA patterns have revealed activity choices that might be attributable to personal motivations for communicating an activity in social networks. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Space-time activity Twitter streaming data processing Cloud computing

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Continuous Histogram Computing on streaming data Approach

Continuous Histogram Computing on Streaming Data Approach

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2024 Conference of Young Researchers in Electrical and Electronic Engineering, ElCon 2024

作者： Azarevich, Artem D. Efremov, Mikhail A. St. Petersburg Electrotechnical University 'LETI' St. Petersburg Russia St. Petersburg Electrotechnical University 'LETI' Mathematical Support and Computer Applications Department St. Petersburg Russia

ISBN: (纸本)9798350360646

In federative distributed learning, when combining trained models into one, information about the distribution of training samples is used. However, in some cases (for example, when training with reinforcement), it is impossible to obtain information about the distribution using standard methods due to the large volume of data or their generation during training. This paper describes the method of computing a histogram for the case of a random value with a previously unknown min and max values in one pass through the streaming data. © 2024 IEEE.

关键词： cumulative distribution function histogram streaming data processing

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Development of an Artificial Intelligence-Supported Hybrid data Management Platform for Monitoring Depression and Anxiety Symptoms in the Perinatal Period: Pilot-Scale Study

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IEEE ACCESS 2023年 11卷 31456-31466页

作者： Ogur, Nur Banu Ceken, Celal Ogur, Yavuz Selim Yuvaci, Hilal Uslu Yazici, Ahmet Bulent Yazici, Esra Sakarya Univ Fac Comp & Informat Sci Dept Comp Engn TR-54050 Sakarya Turkiye Sakarya Univ Fac Med Dept Psychiat TR-54050 Sakarya Turkiye Sakarya Univ Fac Med Dept Obstet & Gynecol TR-54050 Sakarya Turkiye

One of the forces driving science and industry is machine learning, but the proliferation of Big data necessitates paradigm shifts from conventional approaches in applying machine learning techniques to this massive amount of data with varying velocity. Computers are now capable of accurately diagnosing a variety of medical conditions thanks to the availability of immense healthcare datasets and advancements in machine learning techniques. The study's primary aim is to identify the most compelling questions on anxiety and depression in pregnant women by extracting features through performance-optimized algorithms. In this way, it is aimed to reach the result in a shorter time with fewer questions. The next goal of this work is to create an instant remote health status prediction system for depression and anxiety in pregnant women based on the Apache Spark Big data processing engine, which concentrates on using machine learning models on streaming Big data. In this scalable system, the application receives data from pregnant women to forecast the patient's health condition. It then applies the Naive Bayes machine learning algorithm that produces the best results for this dataset with accuracy and precision 90.8% and 81.71% respectively. With the assistance of this big data platform, the time-consuming anxiety and depression detection procedure in a pregnant woman can be replaced with a computer-based technique that works in an instant with a respectable amount of accuracy.

关键词： Big data Machine learning algorithms Anxiety disorders Pregnancy Depression Diseases Classification algorithms data analysis Bioinformatics data analytics streaming data processing machine learning health informatics perinatal period anxiety depression

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streaming data Priority Scheduling Framework for Autonomous Driving by Edge 45

Streaming Data Priority Scheduling Framework for Autonomous ...

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45th Annual International IEEE-Computer-Society Computers, Software, and Applications Conference (COMPSAC)

作者： Yao, Lingbing Zhao, Hang Tang, Jie Liu, Shaoshan Gaudiot, Jean-Luc South China Univ Technol Sch Comp Sci & Engn Guangzhou Peoples R China PerceptIn Fremont CA USA Univ Calif Irvine Dept Elect Engn & Comp Sci Irvine CA USA

ISBN: (纸本)9781665424639

In recent years, intelligent vehicles like autonomous vehicles generate a huge amount of sensing data continuously. The computations on those data streams are far beyond the processing capacity of on-board computing. To deal with the streaming data process in real-time, the deployment of streaming data processing system by edge turns to the first choice in terms of performance. However, the existing frameworks cannot satisfy the complicated demands from autonomous driving tasks and lack the ability in supporting the task priority scheduling. In this paper, we propose a streaming data priority scheduling framework for autonomous driving by edge on Spark streaming and make an implementation on Spark 2.3.0. The proposed framework can identify the priorities among different data processing tasks and implement the task scheduling based on non-preemptive priority queuing theory. To meet differentiated service level requirements, the proposed non-preemptive priority queuing scheduling mechanism considers the priority category of tasks, the distance between vehicles and edge nodes, and the priority weight of vehicles. Experiments show that this mechanism can effectively identify the priority information of different tasks from different vehicles and reduce the end-to-end latency of high-priority tasks by up to 46% than low-priority tasks.

关键词： Autonomous driving streaming data processing Priority scheduling Spark streaming

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streaming Matrix Transposition on FPGAs Using Distributed Memories 9

Streaming Matrix Transposition on FPGAs Using Distributed Me...

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9th IEEE Nordic Circuits and Systems Conference (NorCAS)

作者： Henriksson, Mikael Gustafsson, Oscar Linkoping Univ Dept Elect Engn Linkoping Sweden

ISBN: (纸本)9798350337570

Matrix transposition, the procedure of swapping rows and columns of a matrix, has applications in various signal processing applications, such as massive multiple-input multiple-output (MIMO) communication systems, data compression, and multidimensional fast Fourier transforms - which are used in MIMO radar systems. In low-latency high-throughput streaming applications, specialized circuits for matrix transposition are needed in order to perform transposition in real-time. This is in contrast to "slower" applications, where transposition can be adequately performed by storing a matrix in a shared memory and afterward reading it back in a transposed order. In this paper, a design procedure for streaming matrix transposition on field-programmable gate arrays (FPGAs) using distributed memories is presented. It is shown that significantly fewer FPGA resources are required for small- to medium-sized streaming matrix transpositions compared to recent related works.

关键词： streaming data processing matrix transposition linear arithmetic interleaver FPGA distributed memories

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Digital Content Profiling Based on User Engagement Features 19th

Digital Content Profiling Based on User Engagement Features

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19th European, Mediterranean, and Middle Eastern Conference (EMCIS)

作者： Misiorek, Pawel Ciesielczyk, Michal Rzycki, Bartosz Poznan Univ Tech Fac Comp & Telecommun Ul Piotrowo 2 PL-61138 Poznan Poland Deep BI Poland Warsaw Poland

ISBN: (纸本)9783031306938;9783031306945

Exploring audience engagement with digital media content may lead to many various benefits. In this paper, we study how adding engagement-based features to the article description can influence the efficiency of algorithms aimed at detecting digital media readers' propensity to buy a subscription. Based on the propensity score, the publishers can optimize a decision to display a paywall. Moreover, it is observed that more and more page views are of new or anonymous users. Consequently, the decision concerning the paywall application has to rely only on digital content features. In order to address this application scenario, we propose a novel digital content enrichment framework based on the engagement statistics of users reading a given article. We experimentally evaluate the performance of machine learning algorithms for predicting the propensity to subscribe using the dataset based on events describing the behavior of users exploring the digital news site of the large media publisher. The results of experiments demonstrate that enrichment of article profiles with user engagement features significantly improves prediction models' efficiency.

关键词： Digital content scoring Behavioural profiles for articles Subscription for digital content streaming data processing Big data

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Modeling User Engagement Profiles for Detection of Digital Subscription Propensity 18th

Modeling User Engagement Profiles for Detection of Digital S...

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18th European, Mediterranean, and Middle Eastern Conference on Information Systems (EMCIS)

作者： Misiorek, Pawel Warmuz, Jakub Kaczmarek, Dominik Ciesielczyk, Michal Poznan Univ Tech Fac Comp & Telecommun Inst Comp Ul Piotrowo 2 PL-61138 Poznan Poland Deep BI Poland Warsaw Poland

ISBN: (纸本)9783030959470;9783030959463

In this paper, we study how the application of a dynamic user engagement profiling can influence the efficiency of systems aimed at detecting the user's propensity to buy a subscription. Specifically, we address a task of identifying the digital media readers who are involved enough in the publisher's offer to pay for access to the content of a given webpage. We present the user engagement profile updating framework responsible for enriching raw events with time-agnostic temporal features. In particular, we experimentally evaluate the performance of machine learning algorithms for the task of predicting the user propensity to subscribe using the synthetic dataset based on publicly available data streams on users of KKBox's music service. Additionally, we provide the results of online tests in which the propensity-to-subscribe prediction model is used to control the paywall displays on a digital media website with live traffic. The results of experiments have proven that enrichment of data with engagement profiles leads to higher performance of prediction models than relying just on raw features and tuning the model's hyperparameters.

关键词： Big data User engagement modeling User profiling Subscription for digital content streaming data processing

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