Lecture Notes in Computer Science 12858

Founding Editors Gerhard Goos Karlsruhe Institute of Technology, Karlsruhe, Germany Juris Hartmanis Cornell University, Ithaca, NY, USA

Editorial Board Members Elisa Bertino Purdue University, West Lafayette, IN, USA Wen Gao Peking University, Beijing, China Bernhard Steffen TU Dortmund University, Dortmund, Germany Gerhard Woeginger RWTH Aachen, Aachen, Germany Moti Yung Columbia University, New York, NY, USA More information about this subseries at http://www.springer.com/series/7409 Leong Hou U • Marc Spaniol • Yasushi Sakurai • Junying Chen (Eds.)

Web and Big Data

5th International Joint Conference, APWeb-WAIM 2021 Guangzhou, China, August 23–25, 2021 Proceedings, Part I

123 Editors Leong Hou U Marc Spaniol University of Macau University of Caen Normandie Macau, China Caen, France Yasushi Sakurai Junying Chen Osaka University South China University of Technology Osaka, Japan Guangzhou, China

ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Computer Science ISBN 978-3-030-85895-7 ISBN 978-3-030-85896-4 (eBook) https://doi.org/10.1007/978-3-030-85896-4

LNCS Sublibrary: SL3 – Information Systems and Applications, incl. Internet/Web, and HCI

© Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Preface

This volume (LNCS 12858) and its companion volume (LNCS 12859) contain the proceedings of the fifth Asia-Pacific Web (APWeb) and Web-Age Information Man- agement (WAIM) Joint Conference on Web and Big Data, called APWeb-WAIM. With the increased focus on big data, the new joint conference is expected to attract more professionals from different industrial and academic communities, not only from the Asia-Pacific countries but also from other continents. The objective is to enable the sharing and exchange of ideas, experiences, and results in the areas of the World Wide Web and big data, thus covering web technologies, database systems, information management, software engineering, and big data. The fifth APWeb-WAIM conference was held in Guangzhou during August 23–25, 2021. As an Asia-Pacific flagship conference focusing on research, development, and applications in relation to Web information management, APWeb-WAIM builds on the successes of APWeb and WAIM: APWeb was previously held in Beijing (1998), Hong Kong (1999), Xi’an (2000), Changsha (2001), Xi’an (2003), Hangzhou (2004), Shanghai (2005), Harbin (2006), Huangshan (2007), Shenyang (2008), Suzhou (2009), Busan (2010), Beijing (2011), Kunming (2012), Sydney (2013), Changsha (2014), Guangzhou (2015), and Suzhou (2016); and WAIM was held in Shanghai (2000), Xi’an (2001), Beijing (2002), Chengdu (2003), Dalian (2004), Hangzhou (2005), Hong Kong (2006), Huangshan (2007), Zhangjiajie (2008), Suzhou (2009), Jiuzhaigou (2010), Wuhan (2011), Harbin (2012), Beidaihe (2013), Macau (2014), Qingdao (2015), and Nanchang (2016). The APWeb-WAIM conferences were held in Beijing (2017), Macau (2018), Chengdu (2019), and Tianjin (2020). With the fast development of web-related technologies, we expect that APWeb-WAIM will become an increas- ingly popular forum that brings together outstanding researchers and developers in the fields of the Web and big data from around the world. The high-quality program documented in these proceedings would not have been possible without the authors who chose APWeb-WAIM for disseminating their find- ings. A total of 184 submissions were received and, after the double-blind review process (each paper received at least three review reports), the conference accepted 44 regular papers (23.91%), 24 short research papers, and 6 demonstrations. The con- tributed papers address a wide range of topics, such as graph mining, data mining, data management, topic model and language model learning, text analysis, text classifica- tion, machine learning, knowledge graphs, emerging data processing techniques, information extraction and retrieval, recommender systems, and spatial and spatio-temporal databases. The technical program also included keynotes by M. Tamer Özsu (University of Waterloo, USA), Huan Liu (Arizona State University, Tempe, USA), X. Sean Wang (Fudan University, China), and Xiaokui Xiao (National University of Singapore, Singapore). We are grateful to these distinguished scientists for their invaluable contributions to the conference program. As a joint conference, teamwork is particularly important for the success of APWeb-WAIM. We are deeply vi Preface thankful to the Program Committee members and the external reviewers for lending their time and expertise to the conference. Special thanks go to the local Organizing Committee led by Yi Cai. Thanks also go to the workshop chairs (Yunjun Gao, An Liu, and Xiaohui Tao), demo chair (Yanghui Rao), industry chair (Jianming Lv), tutorial chair (Raymond Chi-Wing Wong), publication chair (Junying Chen), local arrange- ment chairs (Guohua Wang and Junying Chen), and publicity chairs (Xin Wang and Jianxin Li). Their efforts were essential to the success of the conference. Last but not least, we wish to express our gratitude to the Webmaster (Jianwei Lu), for all the hard work, and to our sponsors who generously supported the smooth running of the conference. We hope you enjoy the exciting program of APWeb-WAIM 2021 as documented in these proceedings.

July 2021 Yi Cai Tom Gedeon Qing Li Baltasar Fernández Manjón Leong Hou U Marc Spaniol Yasushi Sakurai Organization

Organizing Committee

General Chairs

Yi Cai South China University of Technology, China Tom Gedeon Australia National University, Australia Qing Li Hong Kong Polytechnic University, China Baltasar Fernández Manjón UCM, Spain

Program Committee Chairs

Leong Hou U University of Macau, China Marc Spaniol Université de Caen Normandie, France Yasushi Sakurai Osaka University, Japan

Workshop Chairs

Yunjun Gao Zhejiang University, China An Liu Soochow University, China Xiaohui Tao University of Southern Queensland, Australia

Demo Chair

Yanghui Rao Sun Yat-sen University, China

Tutorial Chair

Raymond Chi-Wing Wong Hong Kong University of Science and Technology, China

Industry Chair

Jianming Lv South China University of Technology, China

Publication Chair

Junying Chen South China University of Technology, China viii Organization

Publicity Chairs

Xin Wang Tianjin University, China Jianxin Li Deakin University, Australia

Local Arrangement Chairs

Guohua Wang South China University of Technology, China Junying Chen South China University of Technology, China

Webmaster

Jianwei Lu South China University of Technology, China

APWeb-WAIM Steering Committee Representative

Yanchun Zhang Victoria University, Australia

Senior Program Committee Members

Feida Zhu Singapore Management University, Singapore Lei Chen Hong Kong University of Science and Technology, China Mizuho Iwaihara Waseda University, Japan Peer Kroger Christian-Albrechst-University Kiel, Germany Reynold Cheng The University of Hong Kong, China Wolf-Tilo Balke TU Braunschweig, Germany Xiang Zhao National University of Defence Technology, China Yunjun Gao Zhejiang University, China Zhiguo Gong University of Macau, China

Program Committee Members

Alex Delis University of Athens, Greece An Liu Soochow University, China Aviv Segev KAIST, Korea Baoning Niu Taiyuan University of Technology, China Bin Cui Peking University, China Bo Tang Southern University of Science and Technology, China Bohan Li Nanjing University of Aeronautics and Astronautics, China Bolong Zheng Huazhong University of Science and Technology, China Carson K. Leung University of Manitoba, Canada Cheqing Jin East China Normal University, China Chih-Hua Tai National Taipei University, China Organization ix

Defu Lian University of Electronic Science and Technology of China, China Dhaval Patel IBM TJ Watson Research Center, USA Dimitris Sacharidis ULB, Belgium Giovanna Guerrini University of Genoa, Italy Guoqiong Liao Jiangxi University of Finance and Economics, China Haibo Hu Hong Kong Polytechnic University, China Hailong Sun Beihang University, China Haiwei Zhang Nankai University, China Han Su University of Southern California, USA Hao Wang Nanjing University of Information Science and Technology, China Hiroaki Ohshima University of Hyogo, Japan Hongzhi Wang Harbin Institute of Technology, China Hua Wang Victoria University, Australia Hui Li Xiamen University, China Hui Luo RMIT University, Australia Ilaria Bartolini University of Bologna, Italy Jian Yin Sun Yat-sen University, China Jianbin Huang Xidian University, China Jianbin Huang XDU, China Jianming Lv South China University of Technology, China Jianxin Li Beihang University, China Jianzhong Qi The University of Melbourne, Australia Jieming Shi The Hong Kong Polytechnic University, China Ju Fan Renmin University of China, China Jun Gao Peking University, China Junhu Wang Griffith University, Australia Junjie Yao East China Normal University, China Junying Chen South China University of Technology, China Kai Yang City University of Hong Kong, China Kai Zeng Microsoft, USA Kai Zheng University of Electronic Science and Technology of China, China Krishna Reddy P. International Institute of Information Technology, India Kyuseok Shim Seoul National University, Korea Lei Duan Sichuan University, China Leong Hou U University of Macau, China Liang Hong Wuhan University, China Lianghuai Yang Zhejiang University of Technology, China Lu Chen Zhejiang University, China Man Lung Yiu Hong Kong Polytechnic University, China Maria Luisa Damiani University of Milan, Italy Markus Endres University of Augsburg, Germany Meng Wang Southeast University, China Mirco Nanni ISTI-CNR, Italy x Organization

Panagiotis Karras Aarhus University, Denmark Peiquan Jin University of Science and Technology of China, China Peng Wang Fudan University, China Qingbao Huang Guangxi University, China Raymond Chi-Wing Wong Hong Kong University of Science and Technology, China Rong-Hua Li Beijing Institute of Technology, China Sanghyun Park Yonsei University, Korea Sangkeun Lee Oak Ridge National Laboratory, USA Sanjay Kumar Madria Missouri University of Science and Technology, USA Senzhang Wang Central South University, China Shaoxu Song Tsinghua University, China Sheng Wang New York University, USA Shengli Wu Jiangsu University, China Shuyue Hu NUS, Singapore Taketoshi Ushiama Kyushu University, Japan Tao Wang King’s College London, UK Tieyun Qian Wuhan University, China Ting Deng Beihang University, China Tingjian Ge University of Massachusetts, Lowell, USA Vincent Oria NJIT, USA Wee Siong Ng Institute for Infocomm Research, Singapore Wei Lu Renmin University of China, China Wei Song Wuhan University, China Wei Wang University of New South Wales, Australia Wen Zhang Wuhan University, China Xiang Lian Kent State University, USA Xiangmin Zhou RMIT University, Australia Xiaochun Yang Northeastern University, USA Xiaohui Tao The University of Southern Queensland, Australia Xiaokui Xiao National University of Singapore, Singapore Xiaowei Wu University of Macau, China Xike Xie University of Science and Technology of China, China Xin Cao University of New South Wales, Australia Xin Huang Hong Kong Baptist University, China Xin Wang Tianjin University, China Xingquan Zhu Florida Atlantic University, USA Xudong Mao Xiamen University, China Yafei Li Zhengzhou University, China Yajun Yang Tianjin University, China Yanghua Xiao Fudan University, China Yanghui Rao Sun Yat-sen University, China Yang-Sae Moon Kangwon National University, South Korea Yaokai Feng Kyushu University, Japan Yijie Wang National University of Defense Technology, China Yingxia Shao BUPT, China Organization xi

Yongpan Sheng Chongqing University, China Yongxin Tong Beihang University, China Yu Gu Northeastern University, USA Zakaria Maamar Zayed University, United Arab of Emirates Zhaonian Zou Harbin Institute of Technology, China Zhixu Li Soochow University, China Zouhaier Brahmia University of Sfax, Tunisia Keynotes Approaches to Distributed RDF Data Management and SPARQL Processing

M. Tamer Özsu

David R. Cheriton School of Computer Science, University of Waterloo, Canada [email protected]

Abstract. Resource Description Framework (RDF) has been proposed for modelling Web objects as part of developing the “semantic web”, but its usage has extended beyond this original objective. As the volume of RDF data has increased, the usual scalability issues have arisen and solutions have been developed for distributed/parallel processing of SPARQL queries over large RDF datasets. RDF has also gained attention as a way to accomplish data integration, leading to federated approaches. In this talk I will provide an overview of work in these two areas. Striving for Socially Responsible AI in Data Science

Huan Liu

School of Computing and Augmented Intelligence, Arizona State University, USA [email protected]

Abstract. AI has never been this pervasive and effective. AI algorithms are used in news feeds, friend/purchase recommendation, making hiring and firing decisions, and political campaigns. Data empowers AI algorithms and is then collected again for further training AI algorithms. We come to realize that AI algorithms have biases, and some biases might result in deleterious effects. Facing existential challenges, we explore how socially responsible AI can help in data science: what it is, why it is important, and how it can protect and inform us, and help prevent or mitigate the misuse of AI. We show how socially responsible AI works via use cases of privacy preservation, cyberbullying identification, and disinformation detection. Knowing the problems with AI and our own conflicting goals, we further discuss some quandaries and challenges in our pursuit of socially responsible AI. Democratizing the Full Data Analytics Software Stack

X. Sean Wang

School of Computer Science, Fudan University, China [email protected]

Abstract. Data analysis and machine learning is a complex task, involving a full stack of hardware and software systems, from the usual compute systems, cloud computing and supercomputing systems, to data collection systems, data storage and database systems, data mining and machine learning systems, and data visualization and interaction systems. A realistic and highly efficient data ana- lytics and AI application often requires a smooth collaboration among the dif- ferent systems, which becomes a big technical hurdle, especially to the non-computing professionals. The history of computing may be viewed as a technical democratizing processing, which in turn brings huge benefit to the society and its economy. The democratizing process for data analysis and machine learning has started to appear in various aspects, but it still needs research and development in multiple directions, including human-machine natural interaction, automated system selection and deployment, and automated workflow execution and optimization. It can be expected that this democratizing process will continue, and the research and development efforts by the computer scientists are much needed. Efficient Network Embeddings for Large Graphs

Xiaokui Xiao

School of Computing, National University of Singapore, Singapore [email protected]

Abstract. Given a graph G, network embedding maps each node in G into a compact, fixed-dimensional feature vector, which can be used in downstream machine learning tasks. Most of the existing methods for network embedding fail to scale to large graphs with millions of nodes, as they either incur signif- icant computation cost or generate low-quality embeddings on such graphs. In this talk, we will present two efficient network embedding algorithms for large graphs with and without node attributes, respectively. The basic idea is to first model the affinity between nodes (or between nodes and attributes) based on random walks, and then factorize the affinity matrix to derive the embeddings. The main challenges that we address include (i) the choice of the affinity measure and (ii) the reduction of space and time overheads entailed by the construction and factorization of the affinity matrix. Extensive experiments on large graphs demonstrate that our algorithms outperform the existing methods in terms of both embedding quality and efficiency. Contents – Part I

Graph Mining

Co-authorship Prediction Based on Temporal Graph Attention ...... 3 Dongdong Jin, Peng Cheng, Xuemin Lin, and Lei Chen

Degree-Specific Topology Learning for Graph Convolutional Network . . . . . 20 Jiahou Cheng, Mengqing Luo, Xin Li, and Hui Yan

Simplifying Graph Convolutional Networks as Matrix Factorization ...... 35 Qiang Liu, Haoli Zhang, and Zhaocheng Liu

GRASP: Graph Alignment Through Spectral Signatures...... 44 Judith Hermanns, Anton Tsitsulin, Marina Munkhoeva, Alex Bronstein, Davide Mottin, and Panagiotis Karras

FANE: A Fusion-Based Attributed Network Embedding Framework ...... 53 Guanghua Li, Qiyan Li, Jingqiao Liu, Yuanyuan Zhu, and Ming Zhong

Data Mining

What Have We Learned from OpenReview?...... 63 Gang Wang, Qi Peng, Yanfeng Zhang, and Mingyang Zhang

Unsafe Driving Behavior Prediction for Electric Vehicles...... 80 Jiaxiang Huang, Hao Lin, and Junjie Yao

Resource Trading with Hierarchical Game for Computing-Power Network Market ...... 94 Qingzhong Bao, Xiaoxu Ren, Chunfeng Liu, Xin Wang, Xiaofei Wang, and Chao Qiu

Analyze and Evaluate Database-Backed Web Applications with WTool . . . . . 110 Zhou Zhou and XuJia Yao

Semi-supervised Variational Multi-view Anomaly Detection...... 125 Shaoshen Wang, Ling Chen, Farookh Hussain, and Chengqi Zhang

A Graph Attention Network Model for GMV Forecast on Online Shopping Festival ...... 134 Qianyu Yu, Shuo Yang, Zhiqiang Zhang, Ya-Lin Zhang, Binbin Hu, Ziqi Liu, Kai Huang, Xingyu Zhong, Jun Zhou, and Yanming Fang xx Contents – Part I

Suicide Ideation Detection on Social Media During COVID-19 via Adversarial and Multi-task Learning ...... 140 Jun Li, Zhihan Yan, Zehang Lin, Xingyun Liu, Hong Va Leong, Nancy Xiaonan Yu, and Qing Li

Data Management

An Efficient Bucket Logging for Persistent Memory ...... 149 Xiyan Xu and Jiwu Shu

Data Poisoning Attacks on Crowdsourcing Learning ...... 164 Pengpeng Chen, Hailong Sun, and Zhijun Chen

Dynamic Environment Simulation for Database Performance Evaluation . . . . 180 Chunxi Zhang, Rong Zhang, Qian Su, and Aoying Zhou

LinKV: An RDMA-Enabled KVS for High Performance and Strict Consistency Under Skew ...... 190 Xing Wei, Huiqi Hu, Xuan Zhou, and Aoying Zhou

Cheetah: An Adaptive User-Space Cache for Non-volatile Main Memory File Systems...... 199 Tian Yan, Linpeng Huang, and Shengan Zheng

Topic Model and Language Model Learning

Chinese Word Embedding Learning with Limited Data ...... 211 Shurui Chen, Yufu Chen, Yuyin Lu, Yanghui Rao, Haoran Xie, and Qing Li

Sparse Biterm Topic Model for Short Texts ...... 227 Bingshan Zhu, Yi Cai, and Huakui Zhang

EMBERT: A Pre-trained Language Model for Chinese Medical Text Mining ...... 242 Zerui Cai, Taolin Zhang, Chengyu Wang, and Xiaofeng He

Self-supervised Learning for Semantic Sentence Matching with Dense Transformer Inference Network...... 258 Fengying Yu, Jianzong Wang, Dewei Tao, Ning Cheng, and Jing Xiao

An Explainable Evaluation of Unsupervised Transfer Learning for Parallel Sentences Mining ...... 273 Shaolin Zhu, Chenggang Mi, and Xiayang Shi Contents – Part I xxi

Text Analysis

Leveraging Syntactic Dependency and Lexical Similarity for Neural Relation Extraction ...... 285 Yashen Wang

A Novel Capsule Aggregation Framework for Natural Language Inference. . . 300 Chao Sun, Jianzong Wang, Fengying Yu, Ning Cheng, and Jing Xiao

Learning Modality-Invariant Features by Cross-Modality Adversarial Network for Visual Question Answering ...... 316 Ze Fu, Changmeng Zheng, Yi Cai, Qing Li, and Tao Wang

Difficulty-Controllable Visual Question Generation ...... 332 Feng Chen, Jiayuan Xie, Yi Cai, Tao Wang, and Qing Li

Incorporating Typological Features into Language Selection for Multilingual Neural Machine Translation ...... 348 Chenggang Mi, Shaolin Zhu, Yi Fan, and Lei Xie

Removing Input Confounder for Translation Quality Estimation via a Causal Motivated Method ...... 358 Xuewen Shi, Heyan Huang, Ping Jian, and Yi-Kun Tang

Text Classification

Learning Refined Features for Open-World Text Classification ...... 367 Zeting Li, Yi Cai, Xingwei Tan, Guoqiang Han, Haopeng Ren, Xin Wu, and Wen Li

Emotion Classification of Text Based on BERT and Broad Learning System ...... 382 Sancheng Peng, Rong Zeng, Hongzhan Liu, Guanghao Chen, Ruihuan Wu, Aimin Yang, and Shui Yu

Improving Document-Level Sentiment Classification with User-Product Gated Network ...... 397 Bing Tian, Yong Zhang, and Chunxiao Xing

Integrating RoBERTa Fine-Tuning and User Writing Styles for Authorship Attribution of Short Texts ...... 413 Xiangyu Wang and Mizuho Iwaihara

Dependency Graph Convolution and POS Tagging Transferring for Aspect-Based Sentiment Classification ...... 422 Zexin Li, Linjun Chen, Tiancheng Huang, and Jiagang Song xxii Contents – Part I

Machine Learning 1

DTWSSE: Data Augmentation with a Siamese Encoder for Time Series . . . . 435 Xinyu Yang, Xinlan Zhang, Zhenguo Zhang, Yahui Zhao, and Rongyi Cui

PT-LSTM: Extending LSTM for Efficient Processing Time Attributes in Time Series Prediction ...... 450 Yongqiang Yu, Xinyi Xia, Bo Lang, and Hongyu Liu

Loss Attenuation for Time Series Prediction Respecting Categories ofValues...... 465 Jialing Zhang, Zheng Liu, Yanwen Qu, and Yun Li

PFL-MoE: Personalized Federated Learning Based on Mixture of Experts . . . 480 Binbin Guo, Yuan Mei, Danyang Xiao, and Weigang Wu

A New Density Clustering Method Using Mutual Nearest Neighbor ...... 487 Yufang Zhang, Yongfang Zha, Lintao Li, and Zhongyang Xiong

Author Index ...... 495 Contents – Part II

Machine Learning 2

Unsupervised Deep Hashing via Adaptive Clustering...... 3 Shuying Yu, Xian-Ling Mao, Wei Wei, and Heyan Huang

FedMDR: Federated Model Distillation with Robust Aggregation ...... 18 Yuxi Mi, Yutong Mu, Shuigeng Zhou, and Jihong Guan

Data Augmentation for Graph Convolutional Network on Semi-supervised Classification ...... 33 Zhengzheng Tang, Ziyue Qiao, Xuehai Hong, Yang Wang, Fayaz Ali Dharejo, Yuanchun Zhou, and Yi Du

Generating Long and Coherent Text with Multi-Level Generative Adversarial Networks ...... 49 Tianyi Tang, Junyi Li, Wayne Xin Zhao, and Ji-Rong Wen

A Reasonable Data Pricing Mechanism for Personal Data Transactions with Privacy Concern ...... 64 Zheng Zhang, Wei Song, and Yuan Shen

Knowledge Graph

A Probabilistic Inference Based Approach for Querying Associative Entities in Knowledge Graph ...... 75 JianYu Li, Kun Yue, Jie Li, and Liang Duan

BOUNCE: An Efficient Selective Enumeration Approach for Nested Named Entity Recognition ...... 90 Liujun Wang and Yanyan Shen

PAIRPQ: An Efficient Path Index for Regular Path Queries on Knowledge Graphs ...... 106 Baozhu Liu, Xin Wang, Pengkai Liu, Sizhuo Li, and Xiaofei Wang

A Hybrid Semantic Matching Model for Neural Collective Entity Linking . . . 121 Baoxin Lei, Wen Li, Leung-Pun Wong, Lap-Kei Lee, Fu Lee Wang, and Tianyong Hao

Multi-space Knowledge Enhanced Question Answering over Knowledge Graph ...... 135 Ye Ji, Bohan Li, Yi Liu, Yuxin Zhang, and Ken Cai xxiv Contents – Part II

Emerging Data Processing Techniques

A Distribution-Aware Training Scheme for Learned Indexes...... 143 Youyun Wang, Chuzhe Tang, and Xujia Yao

AIR Cache: A Variable-Size Block Cache Based on Fine-Grained Management Method ...... 158 Yuxiong Li, Yujuan Tan, Congcong Xu, Duo Liu, Xianzhang Chen, Chengliang Wang, Mingliang Zhou, and Leong Hou U

Learning an Index Advisor with Deep Reinforcement Learning...... 178 Sichao Lai, Xiaoying Wu, Senyang Wang, Yuwei Peng, and Zhiyong Peng

SardineDB: A Distributed Database on the Edge of the Network...... 186 Min Dong, Haozhao Zhong, Boyu Sun, Sheng Bi, and Yi Cai

DLSM: Distance Label Based Subgraph Matching on GPU ...... 194 Shijie Jiang, Yang Wang, Guang Lu, and Chuanwen Li

Information Extraction and Retrieval

Distributed Top-k Pattern Mining ...... 203 Xin Wang, Mingyue Xiang, Huayi Zhan, Zhuo Lan, Yuang He, Yanxiao He, and Yuji Sha

SQKT: A Student Attention-Based and Question-Aware Model for Knowledge Tracing ...... 221 Qize Xie, Liping Wang, Peidong Song, and Xuemin Lin

Comparison Question Generation Based on Potential Compared Attributes Extraction ...... 237 Jiayuan Xie, Wenhao Fang, Yi Cai, and Zehang Lin

Multimodal Encoders for Food-Oriented Cross-Modal Retrieval ...... 253 Ying Chen, Dong Zhou, Lin Li, and Jun-mei Han

Data Cleaning for Indoor Crowdsourced RSSI Sequences ...... 267 Jing Sun, Bin Wang, Xiaoxu Song, and Xiaochun Yang

Recommender System

A Behavior-Aware Graph Convolution Network Model for Video Recommendation ...... 279 Wei Zhuo, Kunchi Liu, Taofeng Xue, Beihong Jin, Beibei Li, Xinzhou Dong, He Chen, Wenhai Pan, Xuejian Zhang, and Shuo Zhou Contents – Part II xxv

GRHAM: Towards Group Recommendation Using Hierarchical Attention Mechanism...... 295 Nanzhou Lin, Juntao Zhang, Xiandi Yang, Wei Song, and Zhiyong Peng

Multi-interest Network Based on Double Attention for Click-Through Rate Prediction ...... 310 Xiaoling Xia, Wenjian Fang, and Xiujin Shi

Self-residual Embedding for Click-Through Rate Prediction ...... 323 Jingqin Sun, Yunfei Yin, Faliang Huang, Mingliang Zhou, and Leong Hou U

GCNNIRec: Graph Convolutional Networks with Neighbor Complex Interactions for Recommendation ...... 338 Teng Mei, Tianhao Sun, Renqin Chen, Mingliang Zhou, and Leong Hou U

Spatial and Spatio-Temporal Databases

Velocity-Dependent Nearest Neighbor Query ...... 351 Xue Miao, Xi Guo, Xiaochun Yang, Lijia Yang, Zhaoshun Wang, and Aziguli Wulamu

Finding Geo-Social Cohorts in Location-Based Social Networks ...... 368 Muhammad Aamir Saleem, Toon Calders, Torben Bach Pedersen, and Panagiotis Karras

Modeling Dynamic Spatial Influence for Air Quality Prediction with Atmospheric Prior ...... 384 Dan Lu, Le Wu, Rui Chen, Qilong Han, Yichen Wang, and Yong Ge

Learning Cooperative Max-Pressure Control by Leveraging Downstream Intersections Information for Traffic Signal Control...... 399 Yuquan Peng, Lin Li, Qing Xie, and Xiaohui Tao

Privacy-Preserving Healthcare Analytics of Trajectory Data ...... 414 Carson K. Leung, Anifat M. Olawoyin, and Qi Wen

Demo

PARROT: An Adaptive Online Shopping Guidance System ...... 423 Da Ren, Yi Cai, Zhicheng Zhong, Zhiwei Wu, Zeting Li, Weizhao Li, and Qing Li gStore-C: A Transactional RDF Store with Light-Weight Optimistic Lock . . . 429 Zhe Zhang and Lei Zou xxvi Contents – Part II

Deep-gAnswer: A Knowledge Based Question Answering System ...... 434 Yinnian Lin, Minhao Zhang, Ruoyu Zhang, and Lei Zou

ALMSS: Automatic Learned Index Model Selection System...... 440 Rui Zhu, Hongzhi Wang, Yafeng Tang, and Bo Xu

GPKRS: A GPU-Enhanced Product Knowledge Retrieval System ...... 446 Yuming Lin, Hao Song, Chuangxin Fang, and You Li

Standard-Oriented Standard Knowledge Graph Construction and Applications System ...... 452 Haopeng Ren, Yi Cai, Mingying Zhang, Wenjian Hao, and Xin Wu

Author Index ...... 459