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检索条件"任意字段=Conference on Data Mining and Knowledge Discovery - Theory, Tools, and Technology II"

共 838 条记录，以下是1-10 订阅

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The Fourth International Workshop on Smart data for Blockchain and Distributed Ledger (SDBD'24) 24

The Fourth International Workshop on Smart Data for Blockcha...

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30th ACM SIGKDD conference on knowledge discovery and data mining

作者： Zhu, Feida Pei, Jian Zeller, Michael Zhang, Bingxue Singapore Management Univ Singapore Singapore Singapore Duke Univ Durham NC USA Temasek San Diego CA USA Univ Shanghai Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9798400704901

With the advent of Bitcoin, a cryptographically-enabled peer-topeer digital payment system, blockchain together with a whole package of distributed ledger technologies, which serve as the underlying foundation of all the crypto-currencies, have been gaining attention from both academia and industry in the last fifteen years. The recent years have witnessed tremendous momentum in the development of blockchain and distributed ledger technologies, largely due to the impressive rise in the market capital of these digital tokens. More and more industries, from banking and insurance, to supply chain and e-commerce, are quickly realizing the great potential in blockchain technology in efficiency boost, process automation and secure data sharing across otherwise isolated data silos. Furthermore, as the recognition of the data value began to sink in, data assets has become an essential part of the development of enterprises and countries. Blockchain technology is regarded as the foundation of digital economy and provides an effective approach for data ownership, pricing and transactions, which are the core issues of data asset management. However, the potential implications of Blockchain technologies go far beyond their application as the technological backbone for cryptocurrencies. Web3.0, using blockchain as underlying technology, allow for various novel application scenarios, which are built upon distributed consensus and thus are hard to block or censor while providing public verifiability of peer-to-peer transactions without a trusted central party. Web3.0 are expected to become the main front for a plethora of highly expressive applications. To more thoroughly explore the potential of blockchain and web3.0 and promote their progress, SDBD'24 will provide a forum for the most recent blockchain and web3.0 research, innovations, and applications, bridging the gap between theory and practice in the design.

关键词： distributed ledger technology data governance AI in blockchain data asset data pricing privacy data provenance data auditing

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Bridging and Compressing Feature and Semantic Spaces for Robust Graph Neural Networks: An Information theory Perspective 24

Bridging and Compressing Feature and Semantic Spaces for Rob...

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30th ACM SIGKDD conference on knowledge discovery and data mining

作者： Zhong, Luying Lin, Renjie Li, Jiayin Wang, Shiping Chen, Zheyi Fuzhou Univ Fuzhou Peoples R China Fujian Normal Univ Fuzhou Peoples R China

ISBN: (纸本)9798400704901

The emerging Graph Convolutional Networks (GCNs) have attracted widespread attention in graph learning, due to their good ability of aggregating the information between higher-order neighbors. However, real-world graph data contains high noise and redundancy, making it hard for GCNs to accurately depict the complete relationships between nodes, which seriously degrades the quality of graph representations. Moreover, existing studies commonly ignore the distribution difference between feature and semantic spaces in graphs, causing inferior model generalization. To address these challenges, we propose DIB-RGCN, a novel robust GCN framework, to explore the optimal graph representation with the guidance of the well-designed dual information bottleneck principle. First, we analyze the reasons for distribution differences and theoretically prove that minimal sufficient representations in specific spaces cannot promise optimal performance for downstream tasks. Next, we design new dual channels to regularize feature and semantic spaces, eliminating the sharing of task-irrelevant information between spaces. Different from existing denoising algorithms that adopt a random dropping manner, we innovatively replace potential noisy features and edges with local neighboring representations. This design lowers edge-specific coefficient assignment, alleviating the interference of original representations while retaining graph structures. Further, we maximize the sharing of task-relevant information between feature and semantic spaces to alleviate the difference between them. Using real-world datasets, extensive experiments demonstrate the robustness of the proposed DIB-RGCN, which outperforms state-of-the-art methods on classification tasks.

关键词： Semi-supervised Learning Information theory Graph Neural Networks Robustness

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Compact Decomposition of Irregular Tensors for data Compression: From Sparse to Dense to High-Order Tensors 24

Compact Decomposition of Irregular Tensors for Data Compress...

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30th ACM SIGKDD conference on knowledge discovery and data mining

作者： Kwon, Taehyung Ko, Jihoon Jung, Jinhong Jang, Jun-Gi Shin, Kijung Korea Adv Inst Sci & Technol Seoul South Korea Soongsil Univ Seoul South Korea UIUC Champaign IL USA

ISBN: (纸本)9798400704901

An irregular tensor is a collection of matrices with different numbers of rows. Real-world data from diverse domains, including medical and stock data, are effectively represented as irregular tensors due to the inherent variations in data length. For their analysis, various tensor decomposition methods (e.g., PARAFAC2) have been devised. While they are expected to be effective in compressing large-scale irregular tensors, akin to regular tensor decomposition methods, our analysis reveals that their compression performance is limited due to the larger number of first mode factor matrices. In this work, we propose accurate and compact decomposition methods for lossy compression of irregular tensors. First, we propose Light-IT, which unifies all first mode factor matrices into a single matrix, dramatically reducing the size of compressed outputs. Second, motivated by the success of Tucker decomposition in regular tensor compression, we extend Light-IT to Light-IT++ to enhance its expressive power and thus reduce compression error. Finally, we generalize both methods to handle irregular tensors of any order and leverage the sparsity of tensors for acceleration. Extensive experiments on 6 real-world datasets demonstrate that our methods are (a) Compact: their compressed output is up to 37x smaller than that of the most concise baseline, (b) Accurate: our methods are up to 5x more accurate, with smaller compressed output, than the most accurate baseline, and (c) Versatile: our methods are effective for sparse, dense, and higher-order tensors.

关键词： Irregular Tensor Tensor Decomposition data Compression

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Semantic Reasoning and knowledge discovery in Biomedical Informatics Using Domain-Specific Ontologies 2

Semantic Reasoning and Knowledge Discovery in Biomedical Inf...

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2nd IEEE International conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Aishwarya, Bk Sharma, Smita Revathi, V. Singh, Navdeep Kumar, Ashwani Alwan, Adil A. Institute of Aeronautical Engineering Dundigal Hyderabad India Amity University Greater Noida Amity School of Engineering and Technology Uttar Pradesh India New Horizon College of Engineering Department of Applied Sciences Bangalore India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Uttar PradeshKnowledge Park II Greater Noida201306 India National University of Science and Technology College of Technical Engineering Dhi Qar Iraq

ISBN: (纸本)9798350384277

The introduction of domain-specific ontologies is revolutionizing the rapidly developing area of Biomedical Informatics by improving semantic reasoning and knowledge discovery. Using these ontologies, a complete framework for the extraction, organization, and analysis of complicated biological data is presented in this research. The framework provides deeper insights into biomedical research and healthcare practices by interpreting and connecting enormous arrays of heterogeneous data via the use of web technologies and sophisticated data mining methods. Accurate interpretation of biological concepts and their connections is made possible by semantic reasoning, which is supported by the structured representation of domain-specific ontologies. This leads to improved query performance, more precise data retrieval, and the development of complex inferential processes. The study also looks at how ontology-based data mining may be used to find new links and patterns, which might help with predictive modelling and the creation of hypotheses in biomedical research. The implementation obstacles are also discussed, such as scalability issues, data quality assurance, and ontology alignment and integration. The approach's major contributions to drug development, personalized medicine, and the larger field of biomedical research are highlighted in the paper's conclusion, which will eventually lead to better healthcare results and well-informed decisions. © 2024 IEEE.

关键词： Biomedical Informatics data mining Domain-Specific Ontologies knowledge discovery Semantic Reasoning

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Towards Adaptive Neighborhood for Advancing Temporal Interaction Graph Modeling 24

Towards Adaptive Neighborhood for Advancing Temporal Interac...

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30th ACM SIGKDD conference on knowledge discovery and data mining

作者： Zhang, Siwei Chen, Xi Xiong, Yun Wu, Xixi Zhang, Yao Fu, Yongrui Zhao, Yinglong Zhang, Jiawei Fudan Univ Sch Comp Sci Shanghai Key Lab Data Sci Shanghai Peoples R China Ant Grp Shanghai Peoples R China Univ Calif Davis IFM Lab Dept Comp Sci Davis CA 95616 USA

ISBN: (纸本)9798400704901

Temporal Graph Networks (TGNs) have demonstrated their remarkable performance in modeling temporal interaction graphs. These works can generate temporal node representations by encoding the surrounding neighborhoods for the target node. However, an inherent limitation of existing TGNs is their reliance on fixed, handcrafted rules for neighborhood encoding, overlooking the necessity for an adaptive and learnable neighborhood that can accommodate both personalization and temporal evolution across different timestamps. In this paper, we aim to enhance existing TGNs by introducing an adaptive neighborhood encoding mechanism. We present SEAN (Selective Encoding for Adaptive Neighborhood), a flexible plug-and-play model that can be seamlessly integrated with existing TGNs, effectively boosting their performance. To achieve this, we decompose the adaptive neighborhood encoding process into two phases: (i) representative neighbor selection, and (ii) temporal-aware neighborhood information aggregation. Specifically, we propose the Representative Neighbor Selector component, which automatically pinpoints the most important neighbors for the target node. It offers a tailored understanding of each node's unique surrounding context, facilitating personalization. Subsequently, we propose a Temporal-aware Aggregator, which synthesizes neighborhood aggregation by selectively determining the utilization of aggregation routes and decaying the outdated information, allowing our model to adaptively leverage both the contextually significant and current information during aggregation. We conduct extensive experiments by integrating SEAN into three representative TGNs, evaluating their performance on four public datasets and one financial benchmark dataset introduced in this paper. The results demonstrate that SEAN consistently leads to performance improvements across all models, achieving SOTA performance and exceptional robustness.

关键词： Temporal Graph Networks Representation Learning data mining

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Automated data mining of the Reference Stars From Astronomical CCD Frames 8

Automated Data Mining of the Reference Stars From Astronomic...

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8th International conference on Computational Linguistics and Intelligent Systems. Volume ii: Modeling, Optimization, and Controlling in Information and technology Systems Workshop, MOCITSW-CoLInS 2024

作者： Khlamov, Sergii Savanevych, Vadym Trunova, Tetiana Deineko, Zhanna Vovk, Oleksandr Gerasimenko, Roman Kharkiv National University of Radio Electronics Nauki avenue 14 Kharkiv61166 Ukraine

In astronomical images obtained using telescopes and cameras, there are from 1 to 100 thousand or more stars depending on the resolution and exposure time. These objects are fixed against the background of the frame and have constant positions in the celestial sphere. To determine which part of the sky corresponds more accurately to a given frame, it is necessary to associate the frame with known astronomical astrometric and photometric catalogs. These catalogs contain millions of position values of various stars as static objects. Having such information in the form of big data, as well as a huge amount of classified and clustered data in the form of databases, computational methods for fast extraction of the necessary data from them need to be developed. For this purpose, classical methods of "knowledge discovery in databases" (KDD) and data mining exist. However, for their proper application, it is necessary to classify the input data set for subsequent analysis and rejection. The implementation of these methods is closely related to the developed mathematical computational methods for automatic selection of reference stars in astronomical images. The result is implemented in the Lemur software of the CoLiTec (Collection Light technology) project for the astronomical data processing using the data mining methods. © 2024 Copyright for this paper by its authors.

关键词： data mining

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Using Multimodal data to Improve Precision of Inpatient Event Timelines 28th

Using Multimodal Data to Improve Precision of Inpatient Even...

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28th Pacific-Asia conference on knowledge discovery and data mining (PAKDD)

作者： Frattallone-Llado, Gabriel Kim, Juyong Cheng, Cheng Salazar, Diego Edakalavan, Smitha Weiss, Jeremy C. Univ Puerto Rico San Juan PR 00926 USA Carnegie Mellon Univ Pittsburgh PA 15206 USA Natl Lib Med Bethesda MD 20894 USA Univ Pittsburgh Pittsburgh PA 15260 USA

ISBN: (纸本)9789819722402;9789819722389

Textual data often describe events in time but frequently contain little information about their specific timing, whereas complementary structured data streams may have precise timestamps but may omit important contextual information. We investigate the problem in healthcare, where we produce clinician annotations of discharge summaries, with access to either unimodal (text) or multimodal (text and tabular) data, (i) to determine event interval timings and (ii) to train multimodal language models to locate those events in time. We find our annotation procedures, dashboard tools, and annotations result in high-quality timestamps. Specifically, the multimodal approach produces more precise timestamping, with uncertainties of the lower bound, upper bounds, and duration reduced by 42% (95% CI 34-51%), 36% (95% CI 28-44%), and 13% (95% CI 10-17%), respectively. In the classification version of our task, we find that, trained on our annotations, our multimodal BERT model outperforms unimodal BERT model and Llama-2 encoder-decoder models with improvements in F1 scores for upper (10% and 61%, respectively) and lower bounds (8% and 56%, respectively). The code for the annotation tool and the BERT model is available (link).

关键词： Temporal Information Timeline Construction Multimodal data Absolute Timeline Prediction

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TCMRD-KG: Design and Development of a Rheumatism knowledge Graph Based on Ancient Chinese Literature 2

TCMRD-KG: Design and Development of a Rheumatism Knowledge G...

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2024 International conference on Medical Artificial Intelligence

作者： Li, Haotian Xia, Congmin Hou, Youjuan Hu, Sile Quan, Jiang Liu, Yanjun Beijing Univ Chinese Med Sch Tradit Chinese Med Beijing Peoples R China China Acad Chinese Med Sci Guanganmen Hosp Beijing Peoples R China China Acad Chinese Med Sci Inst Informat Tradit Chinese Med Beijing Peoples R China China Acad Chinese Med Sci Inst Basic Theory Chinese Med Beijing Peoples R China

ISBN: (纸本)9798350377620;9798350377613

The use of Traditional Chinese medicineTCM in rheumatic diseases dates back to thousands of years ago. Compared with standardized treatment, TCM has the advantages of low cost, low side effects, and flexible medication. Ancient books of traditional Chinese medicine play an important role in clinical and scientific research. This study lakes the content related to rheumatism in ancient books of traditional Chinese medicine as the research object, integrates the ontology theory and technology in the knowledge graph, realizes the reconstruction of traditional Chinese medicine information knowledge, and provides basic data structure for data mining and knowledge discovery. This study is the first rheumatism-specific knowledge graph constructed based on ancient books of traditional Chinese medicine;it has tried the construction method of knowledge graph of ancient books of traditional Chinese medicine by combining automatic labeling of mainstream large language models with manual review;and according to the knowledge characteristics of ancient books of traditional Chinese medicine, the existing word segmentation technology is difficult to accurately reproduce the accurate meaning of the original text of ancient hooks, a new type of entity extraction method is given.

关键词： Traditional Chinese Medicine Rheumatic Diseases knowledge Graph

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Inverse Problem of Censored Markov Chain: Estimating Markov Chain Parameters from Censored Transition data 27th

Inverse Problem of Censored Markov Chain: Estimating Markov ...

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27th Pacific-Asia conference on knowledge discovery and data mining (PAKDD)

作者： Kohjima, Masahiro Kurashima, Takeshi Toda, Hiroyuki NTT Corp NTT Human Informat Labs Yokohama Kanagawa Japan

ISBN: (纸本)9783031333767;9783031333774

Due to the difficulty of collecting comprehensive data sets (factors include limited GPS coverage and the existence of competitors), most transition data collected from, for example, people and automobiles, have been censored, and so record only the visits of known observable states (locations). In this paper, we tackle the problem of estimating Markov chain parameters from censored transition data. Our parameter estimation method utilizes the theory of the censored Markov chain, the Markov chain that has unobservable states. Our problem formulation can be seen as the inverse of existing studies that construct censored Markov chains from (original) Markov chains and unobservable states. We confirm the effectiveness of the proposal by experiments on synthetic and real data sets.

关键词： probabilistic models markov chain censored transition data inverse problem

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Digital Twins for Traffic Congestion in Smart Cities: A Novel Solution Using data mining Techniques 15

Digital Twins for Traffic Congestion in Smart Cities: A Nove...

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15th International conference on knowledge Management and Information Systems, KMIS 2023 as part of the 15th International Joint conference on knowledge discovery, knowledge Engineering and knowledge Management, IC3K 2023

作者： Anniciello, Arianna Fioretto, Simona Masciari, Elio Napolitano, Enea Vincenzo Department of Electrical and Information Technology Engineering University of Naples Federico II Italy

ISBN: (纸本)9789897586712

This article serves as a position paper that explores the complex issue of traffic management in smart cities and the challenges it presents. The problem of urban traffic is particularly relevant in our modern world, where more and more people are moving to urban environments, leading to congestion, pollution and reduced quality of life. To address this challenge, we propose an innovative methodology based on Digital Twins. The paper proposes an extended approach that integrates Digital Twins with other existing techniques such as Trajectory mining, Process mining, and Decision Making. These techniques, which combine motion data, process analysis, and data-driven Decision Making, can enrich the Digital Twin model, provide a deeper understanding of traffic flows, and deliver more targeted and effective traffic management solutions. This proposal represents a significant step forward in the search for innovative and sustainable solutions for urban traffic management, and lays the foundation for further research and development in this critical area. Copyright © 2023 by SCITEPRESS - Science and technology Publications, Lda.

关键词： Smart city

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