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检索条件"任意字段=International Conference on Big Data Intelligence and Computing, DataCom 2022"

共 1353 条记录，以下是41-50 订阅

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Fast Integration for Multiple Graphs with Neumann Approximation

Fast Integration for Multiple Graphs with Neumann Approximat...

引用

IEEE international conference on big data and Smart computing (bigComp)

作者： Yun, Taehwan Kim, Myung Jun Shin, Hyunjung Ajou Univ Dept Artificial Intelligence Suwon South Korea INRIA Saclay Soda Palaiseau France Ajou Univ Dept Ind Engn Suwon South Korea

ISBN: (纸本)9781665475785

Graph-based models have gained much interest in the domain of machine learning as they offer the advantage of handling data that reside on complex structures. From various models that encounter graph-structured data, graph-based semi-supervised learning (SSL) have shown successful results in multiple applications. The key idea behind SSL is the spreading process of labels through the edges and the problem boils down to keeping the graph Laplacian intact. Meanwhile, with the rapid growth in availability of data, there exist multiple descriptions of graphs for the same set of data points. Each graph contains complementary information to one another, and it would be beneficial to integrate all the available information. In this paper, we propose an SSL-based fast graph integration method that employs approximation in the maximum likelihood estimation process of finding the combination. The proposed approximation utilizes the connection between the covariance and its Neumann series, which allows us to avoid explicit matrix inversion. Empirically, the proposed method achieved competitive performance with significant improvements in computational time when compared to other method.

关键词： graph-based semi-supervised learning graph integration maximum likelihood estimation Neumann series

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The Storage and Sharing of big data in Air Traffic Management System

The Storage and Sharing of Big Data in Air Traffic Managemen...

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international conference on big data intelligence and computing, datacom 2022

作者： Zhang, Yuan Wang, Qing Li, Jiahao Wu, Zhijun College of Safety Science and Engineering Civil Aviation University of China Tianjin China College of Electronic Information and Automation Civil Aviation University of China Tianjin China

ISBN: (纸本)9789819922321

With the continuous development of aviation business and informatization, the storage and sharing mechanism based on big data will play a more important role in the next generation Air Traffic Management (ATM) system. On the basis of analyzing the traditional aviation network, this paper proposes a new network structure for the storage and sharing of aviation big data to solve the transmission bottleneck, information security and other problems existing in the current network, and effectively ensure the safe and efficient operation of air traffic transportation. The structure is information-centric, with publish and subscribe as the main transmission paradigm, and includes multiple levels such as security, policy, data content and application. The main research contents of this paper are as follows. (i) Analyze the development trend of the new generation of ATM and the problems existing in the current aviation network, and in this context, propose the network structure for aviation big data. (ii) Design the policy layer of the new network structure, including content naming mode, routing mode and caching mode. (iii) Design the security layer of the new network structure, including data security, privacy protection and guarantee service, analyze the application of the new aviation network in specific aviation services. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： big data

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Prior-dependent analysis of posterior sampling reinforcement learning with function approximation 27

Prior-dependent analysis of posterior sampling reinforcement...

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27th international conference on Artificial intelligence and Statistics (AISTATS)

作者： Li, Yingru Luo, Zhi-Quan Chinese Univ Hong Kong Shenzhen Peoples R China Shenzhen Res Inst Big Data Shenzhen Peoples R China

This work advances randomized exploration in reinforcement learning (RL) with function approximation modeled by linear mixture MDPs. We establish the first prior-dependent Bayesian regret bound for RL with function approximation;and refine the Bayesian regret analysis for posterior sampling reinforcement learning (PSRL), presenting an upper bound of (O) over tilde (d root (HT)-T-3 log T), where d represents the dimensionality of the transition kernel, H the planning horizon, and T the total number of interactions. This signifies a methodological enhancement by optimizing the O( root log T) factor over the previous benchmark (Osband and Van Roy, 2014) specified to linear mixture MDPs. Our approach, leveraging a value-targeted model learning perspective, introduces a decoupling argument and a variance reduction technique, moving beyond traditional analyses reliant on confidence sets and concentration inequalities to formalize Bayesian regret bounds more effectively.

关键词： Reinforcement learning

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database Management and data Mining 5

Database Management and Data Mining

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5th international conference on Computer Information Science and Artificial intelligence, CISAI 2022

作者： Qiao, Zhiying College of Arts Media and Technology Chiang Mai University Chiang Mai50200 Thailand

ISBN: (纸本)9781510662520

Today, driven by new technologies such as cloud computing, big data, the Internet of Things and artificial intelligence, the era of artificial intelligence has arrived. "Digital transformation" has become the only way for enterprises to seek breakthroughs and innovations. As the carrier of information, the applications of data are fundamental. This paper discusses the database construction, data mining, analysis methods and the application of optimizing the database. Each of these applications will be discussed in details. © 2023 SPIE.

关键词： data mining

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S2D: Enhancing Zero-Shot Cross-Lingual Event Argument Extraction with Semantic Knowledge 13th

S2D: Enhancing Zero-Shot Cross-Lingual Event Argument Extrac...

引用

13th international conference on Natural Language Processing and Chinese computing

作者： Zhao, Zongkai Li, Xiuhua Wei, Kaiwen Chongqing Univ Sch Big Data & Software Engn Chongqing Peoples R China Chongqing Univ Coll Comp Sci Chongqing Peoples R China

ISBN: (纸本)9789819794300;9789819794317

Zero-shot Cross-lingual EAE has garnered significant interests from the community because it could minimize the need for extensive data annotation to identify the roles of the arguments within a specific event. Some prior works point out that syntactic structures could be regarded as the language-independent features and those methods have achieved promising performance. However, sometimes even the sentences in different languages express the same meaning, the syntactic parsing results are quite different. To alleviate this problem, we find the semantic information is rarely considered, which could be considered as another language-independent features and provides more consistent parsing results across languages. To this end, in this paper, we propose the Semantic-Syntactic Driven framework (S2D) for incorporating the semantic and syntactic information simultaneously. Specifically, we design a language-independent dual-prefix constructor module to handle the semantic and syntactic discrepancies between source and target languages. Besides, we introduce a semantic role labeling source generator module to expand the source language dataset with semantic features. The experimental results illustrate that S2D surpasses the state-of-the-art methods by 3.4% and 5.3% in terms of average F1-score on the ACE-2005 and ERE datasets, respectively.

关键词： Event Extraction Cross Lingual Semantic Knowledge

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An enhanced LSTM method for financial market forecasting 2

An enhanced LSTM method for financial market forecasting

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2nd international conference on big data, Computational intelligence, and Applications, BDCIA 2024

作者： Sun, Yu Mutalib, Sofianita Guangdong University of Science and Technology Guangdong Dongguan523083 China School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam40450 Malaysia

ISBN: (纸本)9781510689053

To effectively enhance the prediction accuracy of financial time series and improve the interpretability of the structural parameters of the prediction model, this paper proposes a long short-term memory (LSTM) stock price prediction model optimized in two stages based on Bayesian and Particle Swarm Optimization (BPSO-LSTM). This model represents an innovative advancement of the traditional LSTM framework, specifically designed to address complex nonlinear problems characterized by long-term dependencies, particularly in stock indices. Initially, the Bayesian algorithm is employed to optimize the core parameters of the LSTM model, establishing a foundation for subsequent Particle Swarm Optimization. Following this, an adaptive particle swarm algorithm is utilized to further refine the core parameters obtained through Bayesian Optimization, ensuring a high degree of compatibility between the characteristics of stock data and the network topology, thereby significantly improving the prediction accuracy of stock prices. Results from the prediction of the S&P 500 and Hang Seng Index indicate that the two-stage BPSO-LSTM model not only enhances prediction accuracy but also demonstrates strong universal applicability. © 2025 SPIE.

关键词： Particle swarm optimization (PSO)

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Efficient Vertical Mining of Frequent Quantitative Patterns

Efficient Vertical Mining of Frequent Quantitative Patterns

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2023 IEEE international conference on Dependable, Autonomic and Secure computing, 2023 international conference on Pervasive intelligence and computing, 2023 international conference on Cloud and big data computing, 2023 international conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Czubryt, Thomas J. Hryhoruk, Connor C.J. Leung, Carson K. Pazdor, Adam G.M. University of Manitoba Department of Computer Science WinnipegMB Canada

ISBN: (纸本)9798350304602

Frequent pattern mining has become popular in big data analytics and knowledge discovery as it discovers sets of items (e.g., merchandise items or events) that co-occur frequently. These frequent patterns are discovered by either horizontally by transaction-centric mining algorithms or vertically by item-centric mining algorithms. Regardless of the mining algorithms used, traditional frequent pattern mining algorithms focus on discovering Boolean frequent patterns, which reveal the presence or absence of specific items within the discovered patterns. However, in many real-life scenarios, the quantities of items within the patterns are crucial. For instance, the quantity of items can significantly impact the profitability of selling the items found in the discovered patterns. An existing quantitative algorithm called Q-VIPER (2022) mined frequent quantitative patterns by representing the big data as a collection of item-centric bitmaps. Each bitmap captures the presence or absence of a transaction containing the item, together with the quantity of that item in each transaction. It then mines quantitative frequent patterns vertically. It works well with small quantity. However, when dealing with large quantity, it generates a large number of sets of candidate quantitative frequent patterns (aka sets of item expressions, or itemexpsets for short). Given that large quantities are not unusual in numerous real-life applications, we design a scalable solution in this paper. The resulting scalable quantitative frequent pattern algorithm called SQ-VIPER significantly reduces the number of candidates to be generated, and thus speeds up the mining process. Evaluation results show that superiority of our SQ-VIPER over the existing Q-VIPER and MQA-M algorithms, which respectively mine quantitative frequent patterns vertically and horizontally. © 2023 IEEE.

关键词： big data

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Electrochemical Biosensor Design Through data-Driven Modeling Incorporating Meta-Analysis and big dataWorkflow 22nd

Electrochemical Biosensor Design Through Data-Driven Modelin...

引用

22nd international conference on Artificial intelligence and Soft computing (ICAISC)

作者： Vasyl, Martsenyuk Aleksandra, Klos-Witkowska Andrii, Semenets Univ Bielsko Biala Willowa 2 PL-43300 Bielsko Biala Poland Ivan Horbachevsky Ternopil Natl Med Univ 1 Maidan Voli UA-46001 Ternopol Ukraine

ISBN: (纸本)9783031425073;9783031425080

The objective of the work is to offer workflow enabling us to execute both empirical and analytical studies of enzyme kinetics. For this purpose, on the one hand, we are based on a series of experimental research involving the traditional methods and techniques used when studying biochemical reactions and designing electrochemical biosensors: conductance research, spectroscopy, and electromagnetic field study. On the other hand, when studying enzyme kinetics analytically we employ the Michaelis-Menten approach while modelling enzyme-substrate-inhibitor interactions and extend it to multi-substrate multi-inhibitor complexes. Enforcing traditional big data workflow is offered with the help of meta-analysis facilities of existing repositories of biochemical studies located on the BRENDA platform.

关键词： Electrochemical biosensor enzyme kinetics big data workflow parameter estimation

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Application of Artificial intelligence Algorithms in Precision Marketing with Flow data Analysis Models 5

Application of Artificial Intelligence Algorithms in Precisi...

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5th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2024

作者： Lu, Xueqin NanNing University Guangxi541699 China Shinawatra University Pathum Thani Samkhok 12160 Thailand

ISBN: (纸本)9798350395235

Precision marketing is critical in today's competitive e-commerce landscape. This study explores the integration of big data analytic into precision marketing for e-commerce. It introduces innovative data clustering techniques to improve the grouping of data points, thereby increasing the accuracy and speed of analysis. The research presents cutting-edge precision marketing algorithms that use data analytic to optimize tactics in real time. Specifically, the k-mode clustering pattern, a novel k-means algorithm, is proposed to address symbolic data clustering. It modifies the k-means algorithm with a 0-1 matching distance metric, making it suitable for symbolic data sets. The study highlights the critical role of big data analytic and advanced algorithms in effective precision marketing campaigns in digital commerce. In experiments across 10 independent datasets, precision testing results exceed 98.5%. © 2024 IEEE.

关键词： big data

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Addressing big data Analytics Issues and Challenges Using Cloud Infrastructure

Addressing Big Data Analytics Issues and Challenges Using Cl...

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international conference on Decision Aid Sciences and Applications (DASA)

作者： Moosa, Hanna Rana, Muhammad Ehsan Asia Pacific Univ Technol & Innovat Sch Comp Kuala Lumpur Malaysia

ISBN: (纸本)9781665495011

data related terms are the buzzwords in research and academia in recent studies. big data, cloud computing, data analytics and artificial intelligence are some of the terms commonly seen in the studies. big data and cloud computing integration opens many possibilities and opportunities to variety of organisations and businesses throughout the world. There are many cloud computing service providers offering different service and deployment model portfolios to choose from. Powerful analytical tools are being integrated with cloud computing such as Hadoop, Spark, and Storm to speed up the analytical process and maximise the benefit of cloud computing. This research reviews on the use of cloud computing in big data analytics and the challenges that comes with it. In this paper, different cloud computing models, available analytical tools and real-time data processing in cloud computing has been discussed with comparative analysis of the services providers. Finally, the challenges of the existing cloud computing solutions and future research scope is discussed.

关键词： big data Analytics Cloud computing Analytical Tools Real-time big data Analytics

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