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检索条件"任意字段=3rd APWeb and WAIM Joint Conference on Web and Big Data, APWeb-WAIM 2019"

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2nd International Workshop on Knowledge Graph Management and Analysis, KGMA 2019, and 1st International Workshop on data Science for Emerging Applications, DSEA 2019, held jointly with the 3rd apweb and waim joint conference on web and big data, apweb-waim 2019

2nd International Workshop on Knowledge Graph Management and...

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2nd International Workshop on Knowledge Graph Management and Analysis, KGMA 2019, and 1st International Workshop on data Science for Emerging Applications, DSEA 2019, held jointly with the 3rd apweb and waim joint conference on web and big data, apweb-waim 2019

ISBN: (纸本)9783030339814

The proceedings contain 9 papers. The special focus in this conference is on Knowledge Graph Management and Analysis. The topics include: Classification-based emoji recommendation for user social networks;leveraging context information for joint entity and relation linking;community detection in knowledge graph network with matrix factorization learning;a survey of relation extraction of knowledge graphs;pevr: Pose estimation for vehicle re-identification;the research of chinese ethnical face recognition based on deep learning;model of charging stations construction and electric vehicles development prediction.

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3rd apweb and waim joint conference on web and big data, apweb-waim 2019

3rd APWeb and WAIM Joint Conference on Web and Big Data, APW...

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3rd apweb and waim joint conference on web and big data, apweb-waim 2019

ISBN: (纸本)9783030260743

The proceedings contain 65 papers. The special focus in this conference is on web and big data. The topics include: A novel ensemble approach for click-through rate prediction based on factorization machines and gradient boosting decision trees;latent path connected space model for recommendation;freshJoin: An efficient and adaptive algorithm for set containment join;apara: Workload-aware data partition and replication for parallel databases;Which category is better: Benchmarking the rdBMSs and GDBMSs;A meta-path-based recurrent model for next POI prediction with spatial and temporal contexts;discovering attractive segments in the user generated video streams;spatial temporal trajectory similarity join;exploring nonnegative and low-rank correlation for noise-resistant spectral clustering;supervised hashing with recurrent scaling;TRPN: Matrix factorization meets recurrent neural network for temporal rating prediction;an efficient top-k spatial join query processing algorithm on big spatial data;multi-view based spatial-keyword query processing for real estate;powerMonitor: Aspect mining and sentiment analysis on online reviews;transformer and multi-scale convolution for target-oriented sentiment analysis;a new feature selection algorithm based on category difference for text categorization;opinion-aware knowledge embedding for stance detection;history-driven entity categorization;MBMN: Multivariate bernoulli mixture network for news emotion analysis;improved review sentiment analysis with a syntax-aware encoder;who is the abnormal user: anomaly detection framework based on the graph convolutional networks;featureBand: A feature selection method by combining early stopping and genetic local search;FMQO: A federated rdF system supporting multi-query optimization;dataservicehatch: generating and composing continuous data services;data driven charging station placement.

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3rd apweb and waim joint conference on web and big data, apweb-waim 2019

3rd APWeb and WAIM Joint Conference on Web and Big Data, APW...

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3rd apweb and waim joint conference on web and big data, apweb-waim 2019

ISBN: (纸本)9783030260712

The proceedings contain 65 papers. The special focus in this conference is on web and big data. The topics include: Pseudo topic analysis for boosting pseudo relevance feedback;A novel approach for air quality inference and prediction based on DBU-LSTM;leveraging domain context for question answering over knowledge graph;predictive role discovery of research teams using ordinal factorization machines;leveraging lexical semantic information for learning concept-based multiple embedding representations for knowledge graph completion;deep learning for online display advertising user clicks and interests prediction;efficient distributed knowledge representation learning for large knowledge graphs;distributed landmark selection for lower bound estimation of distances in large graphs;WRL: A combined model for short-term load forecasting;AERIAL: An efficient randomized incentive-based influence maximization algorithm;coherence and salience-based multi-document relationship mining;cider: Highly efficient processing of densely overlapped communities in big graphs;DeepAM: Deep semantic address representation for address matching;Medical treatment migration prediction in healthcare via attention-based bidirectional GRU;reducing wrong labels for distant supervision relation extraction with selective capsule network;iterative hypergraph computation based on hyperedge-connected graphs;coupled semi-supervised clustering: Exploring attribute correlations in heterogeneous information networks;how to reach: Discovering multi-resolution paths on large scale networks;PrivBUD-Wise: Differentially private frequent itemsets mining in high-dimensional databases;analysis and management to hash-based graph and rank;a Learning Approach for Topic-Aware Influence Maximization;two-encoder pointer-generator network for summarizing segments of long articles;preface;a framework for image dark data assessment;in pursuit of social capital: Upgrading social circle through edge rewiring.

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Asia Pacific web and web-Age Information Management joint International conference on web and big data, apweb-waim 2020 in conjunction with 3rd International Workshop on Knowledge Graph Management and Applications, KGMA 2020, 2nd International Workshop on Semi-structured big data Management and Applications, SemiBDMA 2020 and 1st International Workshop on Deep Learning in Large-scale Unstructured data Analytics, DeepLUDA 2020

Asia Pacific Web and Web-Age Information Management Joint In...

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Asia Pacific web and web-Age Information Management joint International conference on web and big data, apweb-waim 2020 in conjunction with 3rd International Workshop on Knowledge Graph Management and Applications, KGMA 2020, 2nd International Workshop on Semi-structured big data Management and Applications, SemiBDMA 2020 and 1st International Workshop on Deep Learning in Large-scale Unstructured data Analytics, DeepLUDA 2020

ISBN: (纸本)9789811604782

The proceedings contain 13 papers. The special focus in this conference is on web and big data. The topics include: Knowledge-Driven Multi-dimensional Dialogue Rewriting Model;towards Knowledge Graphs Federations: Issues and Technologies;a Learning Interests Oriented Model for Cold Start Recommendation;a Composite Chain Structure Blockchain Storage Method Based on Blockchain Technology;curriculum-Oriented Multi-goal Agent for Adaptive Learning;preface;neighborhood Query Processing and Surrounding Objects Retrieval in Spatial databases: Applications and Algorithms;distributed Storage and Query for Domain Knowledge Graphs;label Propagation Algorithm Based on Topological Potential;LBNet: A Model for Judicial Reading Comprehension;deep Semantic Hashing for Large-Scale Image Retrieval.

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5th Asia-Pacific web and web-Age Information Management International joint conference on web and big data, apweb-waim 2021, 4th International Workshop on Knowledge Graph Management and Applications, KGMA 2021, 3rd International Workshop on Semi-structured big data Management and Application, SemiBDMA 2021, and 2nd International Workshop on Deep Learning in Large-scale Unstructured data Analytics, DeepLUDA 2021

5th Asia-Pacific Web and Web-Age Information Management Inte...

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5th Asia-Pacific web and web-Age Information Management International joint conference on web and big data, apweb-waim 2021, 4th International Workshop on Knowledge Graph Management and Applications, KGMA 2021, 3rd International Workshop on Semi-structured big data Management and Application, SemiBDMA 2021, and 2nd International Workshop on Deep Learning in Large-scale Unstructured data Analytics, DeepLUDA 2021

ISBN: (纸本)9789811681424

The proceedings contain 11 papers. The special focus in this conference is on web and big data. The topics include: Survival Effect of Internet Macroscopic Topology Evolution;the Method for Image Noise Detection Based on the Amount of Knowledge Associated with Intuitionistic Fuzzy Sets;link Prediction of Heterogeneous Information Networks Based on Frequent Subgraph Evolution;memory Attentive Cognitive Diagnosis for Student Performance Prediction;A Study on the Privacy Threat Analysis of PHI-Code;preface;should I Stay or Should I Go: Predicting Changes in Cluster Membership;logistics Policy Evaluation Model Based on Text Mining;MRRVOS: Modular Refinement Referring Video Object Segmentation.

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6th Asia-Pacific web and web-Age Information Management International joint conference on web and big data, apweb-waim 2022: 5th International Workshop on Knowledge Graph Management and Applications, KGMA 2022, 4th International Workshop on Semi-structured big data Management and Applications, SemiBDMA 2022, and 3rd International Workshop on Deep Learning in Large-scale Unstructured data Analytics, DeepLUDA 2022

6th Asia-Pacific Web and Web-Age Information Management Inte...

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6th Asia-Pacific web and web-Age Information Management International joint conference on web and big data, apweb-waim 2022: 5th International Workshop on Knowledge Graph Management and Applications, KGMA 2022, 4th International Workshop on Semi-structured big data Management and Applications, SemiBDMA 2022, and 3rd International Workshop on Deep Learning in Large-scale Unstructured data Analytics, DeepLUDA 2022

ISBN: (纸本)9789819913534

The proceedings contain 24 papers. The special focus in this conference is on Asia-Pacific web and web-Age Information Management International joint conference on web and big data. The topics include: Analysis of Public Opinion Evolution in Public Health Emergencies Based on Multi-fusion Model;Failure Simulation Study of Double-Layer Exhaust Manifold for Turbocharged Engine Under LCF & HCF;lung Electrical Impedance Tomography Based on Improved Generative Adversarial Network;snake Robot Motion Planning Based on Improved Depth Deterministic Policy Gradient;Global Path Planning for Multi-objective UAV-Assisted Sensor data Collection: A DRL Approach;Infrared Image Object Detection of Vehicle and Person Based on Improved YOLOv5;slow Port Scanning Attack Detection Algorithm Based on Dynamic Time Window Mechanism;AU-GAN: Attention U-Net Based on a Built-In Attention for Multi-domain Image-to-Image Translation;multi-robot Online Complete Coverage Based on Collaboration;effectively Filtering Images for Better Multi-modal Knowledge Graph;A Long Short-Term Urban Air Quality Prediction Model Based on Spatiotemporal Merged GLU and GCN;colour Coherence Entropy for Ceramic Fragment Analysis;a Fast Method of Legal Decision Recommendation System;an Encryption Key Sharing Method for Contact Tracing Application;a Non-contact Interaction Method for 3D Display Devices;accelerating Distributed Knowledge Graph System Based on Non-Volatile Memory;towards a Unified Storage Scheme for Dual data Models of Knowledge Graphs;cross-platform Product Matching Based on Knowledge Graph;A GCN-Based Sequential Recommendation Model for Heterogeneous Information Networks;SP-LAN: A Stock Prediction Model Based on LSTM-Attention Network;research on Composite Index Construction Method Based on Master-Slave Blockchain Structure;multi-source Heterogeneous Blockchain data Quality Assessment Model.

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A Knowledge-Enabled Customized data Modeling Platform Towards Intelligent Police Applications 6th

A Knowledge-Enabled Customized Data Modeling Platform Toward...

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6th Asia Pacific web (apweb) and web-Age Information Management (waim) joint International conference on web and big data (apweb-waim)

作者： Wang, Tiexin Jiang, Hong Zhang, Huihui Yan, Xinhua Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol 29 Jiangjun Rd Nanjing 211106 Peoples R China Weifang Univ Weifang 261061 Peoples R China Nanjing DENET Syst Technol Co Ltd Nanjing Peoples R China

ISBN: (纸本)9783031251573;9783031251580

With the rapid development of information and communication technologies, massive amounts of data continue to be generated and flood all aspects of society. As one of the key departments of the government, the public security bureau masters all kinds of heterogonous data. Deep analysis of these data will help to detect and prevent public security cases and maintain social stability. Therefore, it is an urgent demand for grassroots police officers to better manage and use these data. To address this demand, in this paper, we present the work of designing and implementing a customized data modeling platform. With the modeling platform, which owns a visual interface, police officers can have a better overview and understanding of collected data and use the drag-and-drop method to build data analysis models. As a core component of this modeling platform, after analyzing 211 tables of practical police data, we built a public security domain knowledge model. Cooperating with the Sucheng branch of Suqian Public Security Bureau, we conducted a set of experiments with police officers on real police data. Experiment results show that the modeling platform has better user-friendliness and outperforms the traditional SQL-based querying method considering the integrity of querying results.

关键词： Public security bureau Knowledge model Customized modeling platform Query expansion

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A data Services Composition Approach for Continuous Query on data Streams 2nd

A Data Services Composition Approach for Continuous Query on...

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2nd International joint conference on Asia-Pacific web (apweb) / web-Age Information Management (waim)

作者： Wang, Guiling Zuo, Xiaojiang Hesenius, Marc Xu, Yao Han, Yanbo Gruhn, Volker North China Univ Technol Beijing Key Lab Integrat & Anal Large Scale Strea 5 Jinyuanzhuang Rd Beijing 100144 Peoples R China Ocean Informat Technol Co CETC Ocean Corp China Elect Technol Grp Corp 11 Shuangyuan RdBadachu Hitech Pk Beijing 100041 Peoples R China Univ Duisburg Essen Paluno Ruhr Inst Software Technol Schutzenbahn 70 D-45127 Essen Germany

ISBN: (纸本)9783319968933;9783319968926

We witness a rapid increase in the number of data streams due to Cloud Computing, big data and IoT development. We would like to access and share data streams using a data service approach. In this paper, we propose a flexible continuous data service model and a continuous data service composition algorithm for answering queries across data streams. Service operation instance is modeled as a view defined on data streams composed of two parts: a data part and a time synchronization part. The composition algorithm extends the traditional Bucket algorithm to find the contained rewriting of user query on views satisfying the containment relationship of both data part and time synchronization part. We also present use case and experimental studies indicating that the approach is effective and efficient.

关键词： data streams Query rewriting data services Service composition Continuous query

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Federated Knowledge Graph Embedding Unlearning via Diffusion Model 1

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8th International joint conference on web and big data and web-Age Information Management (apweb-waim)

作者： Liu, Bingchen Fang, Yuanyuan Wang, Xu Li, Xin Shandong Univ Sch Software 1500 Shunhua Rd Jinan Shandong Peoples R China Univ Finance & Econ Sch Comp Sci & Technol 7366 Second Ring East Rd Jinan Shandong Peoples R China

ISBN: (数字)9789819772353

ISBN: (纸本)9789819772346;9789819772353

Knowledge graph (KG) embedding representation provides a foundation for knowledge reasoning and applications by mapping entities and relations into vector space. Federated learning (FL) promotes the development and application of artificial intelligence technologies by enabling model sharing and collaboration while safeguarding data privacy. Federated KG embedding enables the utilization of knowledge from diverse client sources while safeguarding the privacy of local data. Due to demands such as privacy protection and the need to adapt to dynamic data changes, investigations into machine unlearning (MU) have been sparked. It is challenging to maintain the performance of KG embedding models while forgetting the influence of specific forgotten data on the model. Existing methods attempt to make simple improvements at the level of the loss function for model training, but lack a targeted design for MU task characteristics. In this paper, we propose FedDM, a novel framework tailored for MU of federated knowledge graph embedding. Leveraging diffusion models, we generate noisy data to sensibly mitigate the influence of specific knowledge on FL models while preserving the overall performance of the remaining data. We conduct experimental evaluations on benchmark datasets to assess the efficacy of the proposed model. Extensive experiments demonstrate that FedDM yields promising results in knowledge forgetting.

关键词： Federated learning Knowledge graph Machine unlearning Diffusion model

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Fuzzy Rough Incremental Attribute Reduction Applying Dependency Measures 1st

Fuzzy Rough Incremental Attribute Reduction Applying Depende...

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1st joint International conference on apweb-waim

作者： Liu, Yangming Zhao, Suyun Chen, Hong Li, Cuiping Lu, Yanmin Renmin Univ China Sch Informat Beijing 100872 Peoples R China Renmin Univ China MOE Key Lab Data Engn & Knowledge Engn Beijing Peoples R China Bldg 6 1388 Zhangdong Rd Pudong New Area Shanghai 201203 Peoples R China

ISBN: (纸本)9783319635798;9783319635781

Since data increases with time and space, many incremental rough based reduction techniques have been proposed. In these techniques, some focus on knowledge representation on the increasing data, some focus on inducing rules from the increasing data. Whereas there is less work on incremental feature selection (i.e., attribute reduction) from the increasing data, especially the increasing real valued data. And fuzzy rough sets is then applied in this incremental method because fuzzy rough set can effectively reduce attributes from the real valued data. By analyzing the basic concepts, such as lower approximation and positive region, of fuzzy rough sets on incremental datasets, the incremental mechanisms of these concepts are then proposed. An incremental algorithm is then designed. Finally, some numerical experiments demonstrate that the incremental algorithm is effective and efficient compared to non-incremental attribute reduction algorithms, especially on the datasets with large number of attributes.

关键词： Feature selection Incremental learning Fuzzy rough set Dependency function

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