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检索条件"任意字段=2023 International Conference on Algorithms, Computing and Data Processing, ACDP 2023"

共 6952 条记录，以下是91-100 订阅

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Large-scale taxi trajectory data processing based on spark distributed computing 3

Large-scale taxi trajectory data processing based on spark d...

引用

3rd international conference on Advanced algorithms and Signal Image processing, AASIP 2023

作者： Sun, Kai Song, Changxin Science and Technology Department Shanghai Urban Construction Vocational College Shanghai201415 China School of Artificial Intelligence Application Shanghai Urban Construction Vocational College Shanghai201415 China

ISBN: (纸本)9781510668522

With the rapid development of urban transportation, taxi trajectory data has become an important resource. However, processing large-scale taxi trajectory data requires addressing issues such as error analysis and processing, and traditional data processing techniques are no longer sufficient to meet these requirements. Therefore, in order to efficiently process these massive amounts of data, this paper proposes a preprocessing model for massive taxi trajectory data based on Spark distributed computing. This model designs a dataset based on the elastic distributed dataset RDD, which is used to record the structural information of taxi trajectory data. At the same time, the model provides SQL statements for manipulating and managing taxi trajectory data and returns the processed results in the form of a dataframe. By utilizing Spark's parallel computing power and distributed storage characteristics, this model can effectively solve the preprocessing problem of massive taxi trajectory data. The experimental results show that the model has high reliability and processing efficiency in processing massive taxi trajectory data. It lays a solid foundation for subsequent trajectory data mining and analysis. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Urban transportation

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An Efficient Scheduling Algorithm for Multi-mode Tasks on Near-data processing SSDs 1

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23rd international conference on algorithms and Architectures for Parallel processing (ICA3PP)

作者： Li, Guo Chen, Xianzhang Liu, Duo Li, Jiali Tan, Yujuan Ren, Ao Chongqing Univ Chongqing 400044 Peoples R China

ISBN: (数字)9789819708628

ISBN: (纸本)9789819708611;9789819708628

Near-data processing (NDP) architectures have been proposed to alleviate the large overhead of data movement between the host and the Computational Storage Device (CSD) by offloading tasks to the CSD. In NDP architectures, each task can run in multiple modes according to the resource it takes for computing, such as the CPU of the host, the accelerator or the processor of the CSD. However, existing task scheduling algorithms on NDP architectures are unaware of the multi-mode tasks, leading to increased completion time of tasks and low resource utilization. In this paper, we propose a Multi-Mode Task Scheduling (MMTS) algorithm to optimize the completion time of the multi-mode tasks in NDP architectures. MMTS employs a greedy strategy to fully use the computing resources in the host and the CSD and align the completion time of the tasks by picking the proper modes. Our experimental results show that MMTS achieves 20.6% performance improvement on average over the state-of-the-art task scheduling algorithm on NDP-based system.

关键词： Near data processing Computational storage Multi-mode task scheduling

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Cirrhosis Prediction in Chronic Liver Disease Patients Using Machine Learning Techniques 3

Cirrhosis Prediction in Chronic Liver Disease Patients Using...

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3rd international conference on Pervasive computing and Social Networking, ICPCSN 2023

作者： Athish, V.P. Sree Nandha, S.S. Rajeswari, D. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Dept. of Data Science and Business Systems Tamilnadu Kattankulathur603203 India

ISBN: (纸本)9798350322842

Liver failure is a serious medical condition that can have life-threatening consequences. The liver is responsible for filtering blood and performing many other important functions. With the rise of machine learning in healthcare, there is an opportunity to use past datasets to predict liver failure early on. The main focus of this research work is to explore the application of supervised machine learning algorithms in predicting liver failure. The study involved pre-processing techniques such as univariate and bivariate analysis, and data visualization to gain better insights into the features of the dataset. Using these techniques, a multi-class classification model was built with machine learning algorithms, and the performance metrics of accuracy, F1 score, and recall were used to compare the algorithms. The results show that machine learning algorithms can accurately predict liver failure and can be an effective tool in healthcare. This study demonstrates the potential of machine learning in predicting liver failure and can contribute to the development of future models in the field. The results of this research study indicates that the proposed algorithm achieved an accuracy of 94.48%. The proposed work will be beneficial to researchers in this field and inspire them to build more advanced and accurate models. © 2023 IEEE.

关键词： Support vector machines

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Tiny-Impute: A Framework for On-device data Quality Validation, Hybrid Anomaly Detection, and data Imputation at the Edge 16

Tiny-Impute: A Framework for On-device Data Quality Validati...

引用

16th IEEE/ACM international conference on Utility and Cloud computing (UCC) / 10th IEEE/ACM international conference on Big data computing, Applications and Technologies (BDCAT)

作者： Al-Ameen, Shamil Sudharsan, Bharath Szydlo, Tomasz Al-Taie, Roua Shah, Tejal Ranjan, Rajiv Newcastle Univ Newcastle Upon Tyne Tyne & Wear England Univ Mosul Mosul Iraq Gen Motors Limerick Ireland

ISBN: (纸本)9798400702341

In the landscape of Internet of Things (IoT) systems, data quality degradation can occur continuously for several reasons, such as sensor malfunctions, intermittent network availability, device maintenance, or incomplete data collection. This paper proposes three efficient data quality validation and imputation algorithms that identify and replace noisy and missing values with better-quality data. We extensively stress-tested and evaluated our algorithms by deploying them on microcontrollers and small CPU-based IoT boards with memory limited to as little as 32KB. We simulated a sensor data stream using five real-world datasets, including data collected from the Newcastle Urban Observatory. In this setup, each algorithm excelled in different areas, consistently demonstrating high performance across 100 samples in terms of high energy efficiency (0.014 J), low computation time (85.94 ms), and low error rates (0.0019 MAE, 0.0027 RMSE). Remarkably, we found that on average, our algorithms running on hardware, costing less than $10, showed performance on par with state-of-the-art methods on high-end devices. The results also demonstrated that our algorithms enabled on-device cleansing of live streaming sensor data, eliminating the dependency on cloud services and allowing for real-time data quality validation and processing at the edge.

关键词： Edge computing IoT Devices TinyML data Imputation

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Design of Cloud Platform for Efficient Remote Sensing Big data processing Based on Open Stack

Design of Cloud Platform for Efficient Remote Sensing Big Da...

引用

2023 international conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Huang, Yan Zhang, Weimeng Xi'an Traffic Engineering Institute Shaanxi Xi'an China Xi'an Technical School Shaanxi Xi'an China

ISBN: (纸本)9798350315455

The rapid preparation of computing resources is an important step to realize efficient remote sensing image processing. It takes a lot of time for researchers to prepare hardware resources and deploy computing framework. It often mistakes in the preparation and careless deployment of the framework which can lead to high research costs. To solve this problem, this paper uses Open Stack to virtualize hardware resources and uses Linux Shell scripting language to quickly prepare and deploy the Spark parallel computing environment. In this paper, based on the remote sensing image change detection method of connection condition, for example, the data processing algorithm in advance import platform algorithms library, component library, used to support the remote sensing image change detection of business flow of creation and recognition, resources rapid preparation, execution and presentation of the results of the algorithm and implement of massive remote sensing image change detection and efficient. It effectively improves the efficiency of remote sensing data management. © 2023 IEEE.

关键词： linux shell open stack remote sensing data management spark

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Deep Learning-based Image Text processing Research 9

Deep Learning-based Image Text Processing Research

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9th IEEE international conference on Big data Security on Cloud (BigdataSecurity) / 9th IEEE international conference on High Performance and Smart computing (IEEE HPSC) / 9th IEEE international conference on Intelligent data and Security (IEEE IDS)

作者： Xiong, Huixuan Jin, Kai Liu, Jingnian Cai, Jiahong Xiao, Lijun Hunan Univ Sci & Technol Sch Comp Sci & Engn Hunan Key Lab Serv Comp & Novel Software Technol Xiangtan 411201 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410002 Peoples R China Guangzhou Coll Technol & Business Big data Dev & Res Ctr Guangzhou 510850 Peoples R China

ISBN: (纸本)9798350312935

Deep learning is a powerful multi-layer architecture that has important applications in image processing and text classification. This paper first introduces the development of deep learning and two important algorithms of deep learning: convolutional neural networks and recurrent neural networks. The paper then introduces three applications of deep learning for image recognition, image detection, and image forensics, as well as three text classification methods based on convolutional neural networks, recurrent neural networks, and other text classification methods. Finally, the development trend of deep learning in the field of text and image processing and the difficulties to be further researched are summarised and prospected.

关键词： Deep learning image processing text classification Artificial Intelligence

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Innovation and Growth of Multi-Source data Integration in Visual Communication Design

Innovation and Growth of Multi-Source data Integration in Vi...

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2023 international conference on Evolutionary algorithms and Soft computing Techniques, EASCT 2023

作者： Zhang, Yingyu Sun, Hongbo Changchun Humanities College Visual Communication Design Changchun China

ISBN: (纸本)9798350313413

In visual communication design (VCD), multi-source data integration plays an important role in innovation and growth. This paper studies and discusses the means of information dissemination in the new media era by analyzing typical digital interaction methods and combining current social hotspots. With the guiding ideology of 'Multiple Sources', this paper proposes a brand-new, efficient method that meets the needs of the public, aiming to achieve a visual communication art form with multiple expressive effects and a design language system with rich connotations. In the process of conception, this paper covers the technical level of image digital conversion and fusion and interactive data integration, designs a VCD model, and conducts testing and research on multi-source data integration algorithms. The test results show that the data processing time of the multi-source data integration algorithm is within 6 seconds, and the fastest time is 3 seconds. The application of these technologies makes it possible to continuously change and enrich the content and form of VCD. The research on the integration of multi-source data in visual communication design covers multiple important findings. The research needs to explore data processing and integration methods in order to effectively process and integrate data from different data sources. It also considers users' feedback and evaluation of multi-source data integration visual communication design to understand their perception and attitude towards design effectiveness, usability, and information presentation. © 2023 IEEE.

关键词： multi-source data integration visual communication visual design visual innovation

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data processing Pipeline of Short-Term Depression Detection with Large-Scale dataset

Data Processing Pipeline of Short-Term Depression Detection ...

引用

IEEE international conference on Big data and Smart computing (BigComp)

作者： Lee, Yonggeon Noh, Youngtae Lee, Uichin KENTECH Energy AI Naju Jeollanam Do South Korea Korea Adv Inst Sci & Technol Sch Comp Daejeon South Korea

ISBN: (纸本)9781665475785

Depression is a common, recurring mental disorder that causes significant impairment in people's lives. In recent years, ubiquitous computing using mobile phones can monitor behavioral patterns relevant to depressive symptoms in-the-wild. In this paper, we propose data processing pipeline of short-term depression detection using mobile sensor data. We build a group model classified by depression severity for capturing depressive mood in a short-period time to handle data quality and data imbalance problem in a large-scale dataset. We expect the group model to identify and characterize digital phenotype representing each depressive group as a middle step toward personalization.

关键词： Large-scale data processing Short-Term Depression Detection Positive computing Mobile computing

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MCMSys: Multimodal data Closed-Loop Management System for Autonomous Driving 16

MCMSys: Multimodal Data Closed-Loop Management System for Au...

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international conferences on Internet of Things (iThings) / Green computing and Communications (GreenCom) / Cyber, Physical and Social computing (CPSCom) / Smart data, Smartdata and Congress on Cybermatics (Cybermatics)

作者： Li, He Zhou, Zhaogao Chen, Pin-tong Yan, Jinjie Yu, Rong Hu, Ziwei Guangdong Univ Technol Sch Automat Guangzhou Peoples R China

ISBN: (纸本)9798350309461

In order to safely and efficiently drive in various complex and diverse environments, autonomous driving requires the installation of multiple sensors to obtain information from the surrounding environment. Currently, most autonomous driving solutions are installed LiDAR, cameras and other hardware sensors to acquire multimodal data individually. The acquisition and application of multimodal data require consideration of data synchronization in temporal and spatial dimensions. In this paper, we design a multimodal data closed-loop management system for autonomous driving, which we named MCMSys, including multimodal data acquisition, multimodal data processing, and multimodal data management and application. We investigate how to install multiple sensors on the vehicle, actuate these sensors to acquire multimodal data, and temporally synchronize and spatially align the multimodal data. We also designed processing methods for the acquired multimodal data, including preprocessing and perception using advanced multimodal fusion algorithms. We investigated options for multimodal data management and application, and we created a vast data transmission scheme with high-speed data transmission capability and storage capacity to actualize the storage and application of large-scale multiple-sensor data.

关键词： autonomous driving multimodal data management

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Performance Study of Large-Scale GPS Trajectory Query Method and Compression Method Based on Spark Platform

Performance Study of Large-Scale GPS Trajectory Query Method...

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2023 IEEE international conference on Image processing and Computer Applications, ICIPCA 2023

作者： Wang, Xin Tianjin University of Technology Tianjin China

ISBN: (纸本)9798350314670

Buses and taxis in cities generate a large amount of GPS trajectory data every day. How to query and manage these large-scale trajectory data is a challenging problem. Based on the RDD component of Spark, a big data computing engine, we design and implement a distributed 3D R-Tree to accelerate trajectory query through global index and local index. The experimental results show that the three queries have the shortest latency under the condition of spatial-temporal grid partition, and the latency of trajectory point query, substring query and region query are 2.33s, 3.33s and 6.88s, respectively. In addition, the massive GPS trajectory data generated by these vehicles bring heavy storage cost pressure to the transportation big data platform. The compression processing of GPS trajectory data is imminent. In this paper, the big data computing engine Spark was used. A set of classical trajectory compression algorithms are implemented in parallel. Finally, the method is verified on a very large-scale real data set. Experimental results on compressed datasets show that the proposed method performs well. It only takes 438 seconds to compress 117.5GB GPS trajectory data of 31000 vehicles for one month on a 14-node Spark cluster. © 2023 IEEE.

关键词： Big data

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