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Industrial-oriented machine learning big data framework for temporal-spatial error prediction and control with DTSMGCN model

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JOURNAL OF INTELLIGENT MANUFACTURING 2024年第3期35卷 1173-1196页

作者： Gui, Hongquan Liu, Jialan Ma, Chi Li, Mengyuan Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400044 Peoples R China Chongqing Univ State Key Lab Mech Transmiss Chongqing 400044 Peoples R China

The thermal error reduces the machining accuracy of machine tools, and should be effectively controlled. But the accurate prediction of the thermal error is challenging because of the complex and dynamic running conditions. In previous studies, the temporal feature of the thermal error is considered, and the spatial feature of the thermal error is not considered. However, the thermal error has temporal-spatial (TS) behaviors, and then it is not sufficient to consider the temporal feature only, leading to a low prediction accuracy and poor robustness. Furthermore, most studies on the TS modeling ignore the complexity of the spatial feature, resulting in the spatial feature of the thermal error not being comprehensively captured. When building a TS model, many studies simply connect the spatial model with the temporal model in series. However, the spatial feature changes with the running time, and the series model cannot truly reflect the TS features. To address these challenges, a new dynamic TS memory graph convolutional network (DTSMGCN) model is proposed to learn the dynamic TS features of the thermal error in this study. The generation mechanism of the thermal error is demonstrated by solving the heat conduction equation, and the dynamic TS behaviors are revealed by the Laplace transform. The designed DTSMGCN cell consists of the marginal unit, joint unit, and hybrid adjacency matrix, and can capture the temporal feature of each variable and the TS features among variables. Moreover, to expedite the training process and improve the executing efficiency, an industrial-oriented machine learning big data framework (IOMLBDF) is designed. The proposed DTSMGCN model is embedded into the designed IOMLBDF. The results show that the DTSMGCN model outperforms other machine learning models, and the designed IOMLBDF can improve the training efficiency when increasing the number of virtual machine nodes and achieve the real-time control of the thermal error.

关键词： Temporal-spatial modeling big data framework Machine learning Virtual machine node

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FLORUS: An Efficient big data framework for Telecom Customer Behavior Analysis

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SN Computer Science 2024年第6期5卷 732页

作者： Vo, Hong-Phuc Nguyen, Khoa-Gia-Cat Le, Thanh-Van Ho Chi Minh City University of Technology (HCMUT) Vietnam National University Ho Chi Minh City (VNU-HCM)

The rapid development of telecommunications services is increasingly attracting millions of users due to the convenience of interaction, promotion and communication. The abundance of daily transaction information has led to the creation of large data sources that are collected over time. This data source is a valuable resource for analyzing and understanding user habits and needs, devising a strategy to maintain and attract potential customers. Therefore, it is necessary to have a suitable system capable of collecting, storing and analyzing large datasets with efficient performance. In this article, we introduce Florus, a big data framework based on Lakehouse architecture, which can tackle these challenges. By applying this framework, we are able to propose an approach to analyzing customer behaviors in the telecommunication industry with a large dataset. Our work focuses on specific analysis of a huge volume of data presented in tables of different schemas, reflecting the business operation over time. Clustering based on the Bisecting K-Means algorithm will support the exploration of customer segments varying in density and complexity, and then characterize them into homogeneous groups to gain a better understanding of the market demand. Furthermore, the enterprise can forecast the revenue income at different levels, which can be applied to every customer. The work was tested with the Gradient Boosted Tree at the end of a data enriching and transformation pipeline. Overall, this work highlights the potential of Florus in supporting customer analysis experiments. Implementing the framework would significantly enhance our ability to conduct comprehensive analyses across the entire customer lifecycle. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： big data framework Customer behavior analysis data analysis pipeline Distributed system

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Hybrid Personalized Book Recommender System Based on big data framework 25

Hybrid Personalized Book Recommender System Based on Big Dat...

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25th International Conference on Advanced Communication Technology (ICACT) - New Cyber Security Risks for Enterprise Amidst COVID-19 Pandemic

作者： Liu, Fan Asaithambi, Suriya Priya R. Venkatraman, Ramanathan Natl Univ Singapore NUS ISS Singapore Singapore

ISBN: (纸本)9791188428106

With the rapid growth of e-books and e-libraries, it becomes difficult for people to find right books from the flood of information. Personalized recommender system has been developed to help users to find appropriate books based on user' ratings and interests. Most of the existing book recommender systems are based on content-based or collaborative filtering algorithms. However, those recommender systems provide book recommendations only based on books' contents and users' personal interests. This paper proposes a hybrid personalized book recommender system which combines both book popularity and personalized interests to recommend books for users. Book popularity is identified by the collected tweets to indicate current reading trends. The book popularity is represented by Lucene score of each book based on the collected tweets. The proposed book recommender system combines model-based recommender algorithms such as item-based and Alternating Least Squares (ALS) with book popularity. A scalable architecture using big data framework is developed to ingest, store, process and analyze both batch and streaming data. Open-source big data tools such as Apache Solr, Apache Kafka, and Apache Spark MLlib are used to implement the proposed hybrid book recommender system. PySpark is used to implement data pre-processing and big data analysis. The ALS recommender algorithm is implemented by using Apache Spark MLlib. The results show that the accuracy of the proposed hybrid book recommender system is acceptable.

关键词： Hybrid recommender algorithm big data framework Collaborative filtering Alternating Least Squares (ALS) PySpark

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Predicting Crowdedness Level of the Mass Rapid Transit (MRT) Platform Using big data framework: A Case Study in Singapore

Predicting Crowdedness Level of the Mass Rapid Transit (MRT)...

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2023 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2023

作者： Liu, F. Asaithambi, S.P.R. Venkatraman, R. NUS-ISS National University of Singapore Singapore

ISBN: (纸本)9798350323153

A reliable and cost-effective public transportation system plays an essential role in many cities. Understanding the crowd density for public transport is crucial for smart city and urban planner. The main objective of this paper is to monitor and predict the crowdedness level on the Mass Rapid Transit (MRT) platforms in Singapore. Firstly, we design and implement a scalable big data framework to support this task. Secondly, in order to address the issue of class imbalance, Synthetic Minority Over-Sampling Technique (SMOTE) is integrated with a balanced random forest classifier to predict the crowdedness level of station platforms. Extensive experiments are conducted to evaluate the performance of the proposed approach on real datasets from Singapore Land Transport Authority (LTA) dataMall API. The results demonstrate the accuracy and efficiency of the proposed approach. © 2023 IEEE.

关键词： big data framework crowdedness level prediction random forest classifier SMOTE

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A Systemic big data framework for the Charging Pile Business 7

A Systemic Big Data Framework for the Charging Pile Business

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7th International Conference on Information Systems Engineering (ICISE)

作者： Xu, Licheng Xie, Shuai Univ Malaya Dept Software Engn Fac Comp Sci & Informat Technol Kuala Lumpur 50603 Malaysia Univ Malaya Dept Artificial Intelligence Fac Comp Sci & Informat Technol Kuala Lumpur 50603 Malaysia

ISBN: (纸本)9781450397889

There are some problems in the electric vehicle (EV) charging pile industry, such as the unreasonable location of charging station construction, low utilization rate of charging piles, and imprecise marketing strategies, which may elicit a negative response from EV users, cause colossal waste of resources and hinder the development of EVs. Based on the relevant charging pile data to be analyzed, this paper combines big data-related technologies to propose a big data framework for analyzing the charging pile data to solve the common charging service problems from a systemic perspective. The purpose of the framework is to provide decision-making reference information for charging pile operators, charging pile application operators and charging service marketers to improve the charging pile business more effectively. The framework is demonstrated by functional structure and technical structure.

关键词： big data framework Charging pile data Charging business improvement Decision-making reference

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A big data framework for E-Government in Industry 4.0

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OPEN COMPUTER SCIENCE 2021年第1期11卷 461-479页

作者： Cu Kim Long Agrawal, Rashmi Ha Quoc Trung Hai Van Pham Hanoi Univ Sci & Technol Sch Informat & Commun Technol Hanoi Vietnam Manav Rachna Int Inst Res & Studies Faridabad India Informat Commun Ctr MOST Hanoi Vietnam

The next generation of E-Government and healthcare has the potential to increase the more intelligent governance with improvements in transparency, accountability, efficiency, and effectiveness. It enables organizations to use the benefits of information via big data analysis to settle the difficulties effectively. big data has emerged which plays a significant role in many sectors around the world. Global trends in taking advantage of the benefits from big data are considered with an overview of the US, European Union, and several developing countries. To deeply understand the utilization of big data in several domains, this study has presented a brief survey of key concepts (such as IoT-enabled data, blockchain-enabled data, and intelligent systems data) to deeply understand the utilization of big data in several domains. Our analysis sets out also the similarities and differences in these concepts. We have also surveyed state-of-the-art technologies including cloud computing, multi-cloud, webservice, and microservice which are used to exploit potential benefits of big data analytics. Furthermore, some typical big data frameworks are surveyed and a big data framework for E-Government is also proposed. Open research questions and challenges are highlighted (for researchers and developers) following our review. Our goal in presenting the novel concepts presented in this article is to promote creative ideas in the research endeavor to performefficaciously next-generation E-Government in the context of Industry 4.0.

关键词： big data E-Government big data analytics big data framework

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A Systemic big data framework for the Charging Pile Business 22

A Systemic Big Data Framework for the Charging Pile Business

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Proceedings of the 7th International Conference on Information Systems Engineering

作者： Licheng Xu Shuai Xie Department of Software Engineering Universiti Malaya Malaysia Faculty of Computer Science and Information Technology Universiti Malaya Malaysia

ISBN: (纸本)9781450397889

关键词： Charging pile data big data framework Decision-making reference Charging business improvement

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A big data framework for network security of small and medium enterprises for future computing

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JOURNAL OF SUPERCOMPUTING 2019年第6期75卷 3334-3367页

作者： Kim, Ha-Kyun So, Won-Hyun Je, Seung-Mo Pukyong Natl Univ Daeyeon Dept MIS Div Business Busan South Korea Acad Korean Studies Grad Sch Korean Studies Seongnam South Korea Korea Univ Dept Comp Sci Educ Seoul South Korea

Recently, there have been some concerns for the network control systems which have been built for the major closed national communication infrastructures in the Republic of Korea. In particular, the control systems used by the small- and medium-sized enterprises (SMEs) often did not consider much on the sophisticated internal or external threats such as hacking or identity theft when they were first developed. For this reason, SMEs have been constantly exposed to internal/external security threats posed by both internal and external attackers, while they are using their own internal business networks constantly stay linked to the external networks operated by their cooperating companies or institutions for the convenience of conducting business. Hence, the security of SMEs' business networks is still vulnerable, but it is not easy to obtain the information from security officials about what are the actual problems they are facing. In this study, a method of strengthening the SMEs' security capability is proposed, especially focusing on a framework to be used when applying big data. For the simulations, necessary data was provided by the SMEs who have experienced security threats in the past. An empirical analysis was conducted with such data, and a framework was designed accordingly. The framework was simulated in a test bed for adjustment. The demographic characteristics of the data providers were studied to ensure a more accurate analysis of the data. The frequency and reliability analysis methods were used together with the machine learning technique to measure the reliability of the data, whereas a factor analysis was applied the validity. Partial least squares (PLS) was used for the empirical analysis as well. An application to which these methodologies were applied was implemented with Java Android and will be provided to the security managers of SMEs upon completion. It is expected that the framework proposed in this study will be a useful solution for SMEs f

关键词： big data framework big data framework SMEs PLS Python Java Android UML User interface Android application

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Towards a big data framework for analyzing social media content

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INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT 2019年 44卷 1-12页

作者： Luis Jimenez-Marquez, Jose Gonzalez-Carrasco, Israel Luis Lopez-Cuadrado, Jose Ruiz-Mezcua, Belen Univ Carlos III Madrid Dept Comp Sci & Engn Av Univ 30 Leganes 28911 Spain

Modern companies generate value by digitalizing their services and products. Knowing what customers are saying about the firm through reviews in social media content constitutes a key factor to succeed in the big data era. However, social media data analysis is a complex discipline due to the subjectivity in text review and the additional features in raw data. Some frameworks proposed in the existing literature involve many steps that thereby increase their complexity. A two-stage framework to tackle this problem is proposed: the first stage is focused on data preparation and finding an optimal machine learning model for this data;the second stage relies on established layers of big data architectures focused on getting an outcome of data by taking most of the machine learning model of stage one. Thus, a first stage is proposed to analyze big and small datasets in a non-big data environment, whereas the second stage analyzes big datasets by applying the first stage machine learning model of. Then, a study case is presented for the first stage of the framework to analyze reviews of hotel-related businesses. Several machine learning algorithms were trained for two, three and five classes, with the best results being found for binary classification.

关键词： big data framework Machine learning model Social media analytics Hospitality Yelp

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Application of Open-Source big-data framework in Marine Information Processing

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JOURNAL OF COASTAL RESEARCH 2019年第Sup1期98卷 187-190页

作者： Gao, Xiaoxing Wang, Hanxin Li, Xiaoxia Hebei GEO Univ Shijiazhuang 050031 Hebei Peoples R China

Open-source big-data systems can use a variety of processing technologies. For workloads that only require batch processing, Hadoop, which is less time sensitive and less expensive than other solutions, would be a good choice. For workloads that only require stream processing, Storm can support a wider range of languages and achieve very low latency processing, but the default configuration can produce duplicate results and cannot guarantee order. Samza's tight integration with YARN and Kafka provides greater flexibility, easier-to-use multiteam usage, and simpler replication and state management. The most suitable solution depends mainly on the state of the data to be processed, the time required for processing, and the desired result. Specifically, using a full-featured solution or a solution that focuses primarily on a project requires a careful trade-off. As it matures and is widely accepted, similar issues need to be considered when evaluating any emerging and innovative solutions.

关键词： Open source big data framework ocean information processing

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