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IEEE International Conference on big data and Smart Computing (bigComp)

作者： Lee, Byeongcheon Oh, Jinyeong Shon, Woojin Moon, Jihoon Soonchunhyang Univ Dept AI & Big Data Asan South Korea

ISBN: (纸本)9781665475785

A collection of information technology (IT) services known as "cloud computing" is offered to customers across a network on a subscription basis with the flexibility to scale up or down based on their service needs. The emergence of cloud computing technology, which has tremendous advantages, is one of the major advancements in recent times. Many computers and servers are specifically devoted to meeting the demands of businesses in a cloud computing system for internal communications. Users can access their services via an internet connection. Registered users have remote access to both hardware and software, thanks to the cloud service, which has made essential adjustments to how information is stored and made accessible. This paper investigates the use of Amazon Web Services (AWS) for big data processing and analytics in South Korea. We collected several domestic journal and conference papers that studied local cloud services based on AWS to introduce distributed systems and cloud computing technologies. This study can provide researchers with a compact version of the extensive AWS-based data processing literature and potential future insights. It can also provide stakeholders tailored services, information about cutting-edge solutions that can influence academics, and details about current research needs.

关键词： Amazon Web Services (AWS) big data processing cloud computing distributed system

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EODIE-Earth Observation data Information Extractor

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SOFTWAREX 2023年 23卷

作者： Wittke, Samantha Fouilloux, Anne Lehti, Petteri Varho, Juuso Kivimaki, Arttu Karhu, Maiju Karjalainen, Mika Vaaja, Matti Puttonen, Eetu Aalto Univ Dept Built Environm Espoo Finland Natl Land Survey Finland Finnish Geospatial Res Inst Dept Remote Sensing & Photogrammetry Helsinki Finland Univ Oslo Dept Geosci Oslo Norway Aalto Univ Dept Appl Phys Espoo Finland

Remote sensing satellites provide a vast amount of data to monitor and observe Earth's surface and events on it. To use these data efficiently in subsequent analysis and decision-making, highly automated easy-to-use tools are needed. Here, we present Earth Observation data Information Extractor (EODIE). EODIE is a toolkit to extract object-level time-series information from several multispectral satellite remote sensing platforms and to produce analysis-ready products for subsequent data analysis. EODIE has a modular design that makes it adjustable for end-user requirements. Users have a possibility to exchange and add modules in EODIE for flexible processing in different computing environments. With EODIE, remote sensing data can be processed to object level array, geotiff or statistics information of different (vegetation) indices or plain wavelength intervals. & COPY;2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Remote sensing big data processing Earth observation Open-source software

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Non-linear block least-squares adjustment for a large number of observations

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SURVEY REVIEW 2022年第387期54卷 479-489页

作者： Mahboub, Vahid Ebrahimzadeh, Somayeh Islamic Azad Univ Dept Surveying Engn Zanjan Branch Zanjan Iran Univ Tehran Fac Geog Dept Remote Sensing & GIS Tehran Iran

In this contribution two algorithms are developed to solve non-linear system of equations which can contain a large number of measurements. These algorithms are based on nonlinear block least-squares (BLS). Although block least squares was investigated by some researchers, the non-linear case was not examined by now. The first algorithm is proposed to solve a special case of non-linear problems that do not require linearization. Such an algorithm can be called total block least-squares. The second algorithm is based on linearization within a general nonlinear mixed model using a new notation which is in agreement with the rigorous linearization presented by Pope. Both of these algorithms can handle constraints on the parameters. By use of these algorithms, big data processing is feasible with inexpensive computers. Furthermore, expensive processors can solve systems with a large number of equations faster. Two case studies with more than 120,000 equations show that fast and accurate computations are possible by applying these algorithms without any loss of accuracy.

关键词： Non-linear block least squares Constraints General non-linear model Large number of measurements big data processing

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IEEE bigdata Cup 2023 Report: Object Recognition with Muon Tomography Using Cosmic Rays

IEEE BigData Cup 2023 Report: Object Recognition with Muon T...

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2023 IEEE International Conference on big data, bigdata 2023

作者： Wnuk, Mateusz Dziuba, Jan Janusz, Andrzej Slezak, Dominik Institute of Informatics University of Warsaw Poland Qed Software Poland

ISBN: (纸本)9798350324457

We summarize the results of the IEEE bigdata 2023 Cup: Object Recognition with Muon Tomography using Cosmic Rays - a data mining competition organized at the *** platform in association with the IEEE bigdata 2023 conference. We describe the challenge at the heart of the competition task, as well as the data acquisition and preparation steps. We present the entire process of preparing experiments and subsequent data analysis for the purpose of recognizing X-rayed objects using muon tomography techniques. We conclude this analysis by presenting the baseline as well as the winning solution of the object segmentation algorithms for the research space reconstruction and object classification. © 2023 IEEE.

关键词： big data processing Computer Simulations data Granulation data Mining Competitions Image processing Image Segmentation Muon Tomography Object Recognition

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Challenges of big data and Vehicle data 2

Challenges of Big Data and Vehicle Data

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2nd IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS)

作者： Prehofer, Christian DENSO Automot Germany Berlin Germany

ISBN: (纸本)9781665443937

In this short paper, we outline the challenges of big data processing for vehicle data, considering different use cases as well as different locations. On an architectural level, we show the benefits and possible limitations of the different approaches for in-vehicle processing, edge computing as well as cloud processing. We also discuss different approaches for big data processing, including batch processing as well as distributed stream processing.

关键词： big data processing batch processing stream processing

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Evaluating Serverless Architecture for big data Enterprise Applications 8

Evaluating Serverless Architecture for Big Data Enterprise A...

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8th IEEE/ACM International Conference on big data Computing, Applications and Technologies (BDCAT)

作者： Bhat, Aimer Park, Heeki Roy, Madhumonti Univ Illinois Champaign IL 61820 USA

ISBN: (纸本)9781450391641

Migration of enterprise applications to the cloud has been driven by a myriad of benefits ranging from availability of infinite computing resources to the elimination of upfront CapEx cost. However, many users still face the burden of complex framework knowledge requirement to efficiently deploy applications in the cloud. In this paper, we investigate serverless computing for performing large scale data processing with cloud-native primitives. Serverless computing environment abstracts all infrastructure handling, simplifying developers' work who aspire to deploy applications on the cloud. With dynamic input load on the system, serverless architecture has promise to provide better resource utilization and lower costs.

关键词： Function as a Service Serverless computing Enterprise applications load variations AWSLambda big data processing cloud computing MapReduce

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Characterization of the Spatiotemporal Behavior of a Sweeping System Using Supervised Machine Learning Enhanced with Feature Engineering 1

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23rd IFIP WG 5.5 Working Conference on Virtual Enterprises (PRO-VE)

作者： Ben Daya, Bechir Audy, Jean-Francois Lamghari, Amina UQTR Business Sch 3351 Blvd Forges Trois Rivieres PQ G8Z 4M3 Canada

ISBN: (数字)9783031148446

ISBN: (纸本)9783031148460;9783031148446;9783031148439

This paper focuses on geolocation data processing to infer the behavior of a mechanical sweeping system. A framework based on the feature engineering (FE) andmachine-learning (ML) tools for geolocation data processing is proposed. A supervised multi-classification machine learning using a large range of classifiers, input variables, training and data test sets is used to predict the sweeping system behavior. The results showed that Logistic Regression (LR) and Support Vector Machine (SVM) are the best classifiers for predicting the sweeping behavior and some simulated instances constituted the best training sets. The sweeping state prediction accuracy provided with LR and SVM classifiers, when trained with historical data, were in average 86.22% and 86.13%, respectively. These predictions using the same classifiers, when trained with simulated data, were in average 87.40% and 87.22%. These promising results illustrate the potential of integrating FE and simulation to enhance the performance the ML tools when studying the behavior of complex logistics systems.

关键词： Supervised machine learning Feature engineering Multi-classification big data processing Geolocation data Sweeping system

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A Novel Distributed Software Architecture for Managing Customer Behavior data: A Case Study in Banking Sector 21

A Novel Distributed Software Architecture for Managing Custo...

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21st International Conference on Computational Science and Its Applications (ICCSA)

作者： Kargili, Ozer Batu Arik, Ahmet Okan Bekler, Merve Kose, Osman Uygar Aktas, Mehmet S. GTech R&D Dept Istanbul Turkey Yeditepe Univ Management Informat Syst Istanbul Turkey Yildiz Tech Univ Comp Engn Istanbul Turkey

ISBN: (纸本)9781665458436

We propose a Lambda-based, distributed, big data analysis architecture explicitly designed for the banking sector within the scope of this study. The proposed architecture is designed to extract useful information from user behavior data. The results derived from user behavior data obtained from many sources aim to increase the performance and efficiency of digital banking applications. The prototype implementation of the proposed architecture is designed, developed, and implemented. Performance and scalability tests were carried out on the prototype application. The results obtained reveal the usability of the proposed architecture in the banking sector.

关键词： Distributed big data Systems Software Architectures Lambda Architecture big data processing Banking Sector

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Mining and Analysis of Ship Overtaking Behavior Based on AIS data 23

Mining and Analysis of Ship Overtaking Behavior Based on AIS...

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Proceedings of the 2023 11th International Conference on Information Technology: IoT and Smart City

作者： Zhicheng Ma Daqiang Zhang Yan Lu Jie Song Liye Zhang Shandong University of Science and Technology China China Communications First Highway Engineering Co. Ltd China Institute of High Performance Computing (IHPC) Agency for Science Technology and Research (A*STAR) Singapore

ISBN: (纸本)9798400709043

To achieve efficient and accurate identification of ship encounter behavior information in massive Automatic Identification System (AIS) data, understand and analyze the navigation process of encounter ships, and identify potential safety risk points within ports and waterways. This article takes the AIS data of the main channel area in the Qingdao sea area as the research object, uses Douglas Pucker algorithm and differential algorithm to extract the behavioral feature points of ships overtaking in the main channel, quantitatively analyzes the relationship between behavior change points and overtaking points, understands the behavioral characteristics of ships overtaking process, and provides a clearer and comprehensive understanding of the overtaking process of ships.

关键词： AIS data big data processing Ship data Mining Ship Overtaking Behavior

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Insights with big data Analysis for Commercial Buildings Flexibility in the Context of Smart Cities 10

Insights with Big Data Analysis for Commercial Buildings Fle...

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10th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS)

作者： Oprea, Simona-Vasilica Bara, Adela Ceaparu, Catalin Ducman, Anca Alexandra Diaconita, Vlad Ene, Gabriela Dobrita Bucharest Univ Econ Studies Dept Econ Informat & Cybernet Romana Sq 6 Bucharest 010374 Romania

ISBN: (纸本)9789897585128

The commercial buildings generate a significant volume of data that can be processed to assess the flexibility of the electricity consumption and their potential contribution to flatten the load curve or provide ancillary services. With the constant increase of the volatile output of the Renewable Energy Sources (RES) and numerous Electric Vehicles (EV), the flexibility potential of the commercial buildings has to be investigated to create smarter green cities. However, the volume of consumption data is significantly increasing when various activities are profiled, such as cooling, heating, fans, lights, equipment, etc. In this paper, we propose a big data processing framework or methodology to extract interesting insights from very large datasets and identify the flexibility of the commercial buildings (of several types from the United State of America - U.S.A.) and its market value in correlation with the Demand Response (DR) capabilities at the state and Independent System Operator (ISO) level. This is a theoretical approach combining several aspects, such as: large datasets processing techniques, DR programs, consumption data, flexibility potential estimation, scenarios and DR enabling technologies costs. Applying one of the DR programs, significant results in terms of savings are revealed from simulations.

关键词： big data processing Analytics Load Flexibility Market Value Commercial Buildings

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