The 12^th International Conference on
Big Data Computing and Communications

Big Data lies at the center of modern science and technology, with major advances in analyzing & learning from Big Data concurrently reshaping human knowledge, society, and economy. The overwhelming amounts of data generated in many applications (fundamental sciences, cyber-physical systems, smart cities, sensor networks, and many more) alongside the urge for fast and effective handling and decision-making, in real-time, pose a number of significant challenges on the underlying system design and methods.

The 12th International Conference on Big Data Computing and Communications (BigCom2026), which is to be held on AUG. 14-16, 2026 in Beijing, China. The conference aims to attract researchers and practitioners with interest in the theme of Big Data, in its broadest sense: analytics, management, security and privacy, communications, and high-performance computing. We welcome original, unpublished research papers that emphasize theoretical foundations, modeling, algorithmic methodologies, and data-driven applications in science and engineering. We also welcome visionary papers on new and emerging topics.

The related topics include but are not limited to:

Track 1. Architecture and Systems for Big Data

Track 2. Algorithms for Big Data

Track 3. AI for Big Data

Track 4. Security and Privacy for Big Data

Track 5. Communication and Networking for Big Data

All the regular papers will be considered for the Best Paper Award.

Important Dates

All deadlines are at 11:59 PM Pacific Standard Time.

First Round Deadline:
Paper submission: March 17, 2026
Acceptance: April 19, 2026
Camera Ready: April 30, 2026

Second Round Deadline:
Paper submission: June 5, 2026
Acceptance: June 30, 2026
Camera Ready: July 7, 2026

Conference dates: August 14–16, 2026

京ICP备2026004762号京公网安备11010202000001号

Main Technical Program

Saturday, August 15, 9:00–12:00

Keynote: BIGCOM 2026 Keynote

Title 1: The Role of Unlabeled Data in Regularization and Optimal Statistical Learning

Speaker 1: Shang-Hua Teng, Professor of Computer Science and Mathematics, USC, USA

Title 2: Edge Intelligence for Next-Generation AI Services

Speaker 2: Geyong Min, Professor of High Performance Computing and Networking in the Department of Computer Science, University of Exeter, U.K.

Title 3: From Artificial Intelligence to Processor Chips

Speaker 3: Yunji Chen, Deputy Director, Institute of Industrial Artificial Intelligence & Institute of Computing Technology, CAS, China

Saturday, August 15, 13:00–18:00 & Sunday, August 16 9:00-12:20

Conference Program

Each paper will have a 12-minute presentation followed by a 3-minute Q&A.

According to the No Show policy of IEEE, each paper should be presented at the conference. We reserve the right to preclude authors who do not present their paper at the conference from having their papers published in IEEE Xplore.

BigCom 2026 - Main Technical Program

Saturday, August 15, 13:00–13:48

Session 1: Big Data Communications and Mobile Network Analytics

1. On Measurements and Theoretical Analysis of Spatio-Frequency Property for OFDM Backscattering Systems

Jianming Xia (The 38th Research Institute of China Electronics Technology Group Corp), Xin He (School of Computer and Information Anhui Normal University), Haichuang Ying (School of Computer Science and Technology Anhui University) and Xing Guo (School of Computer Science and Technology Anhui University)

2. Boosting Transmission Performance over Wi-Fi via Receiver-Side Driver Status Probing

Shenghui Wei (Beijing University of Posts and Telecommunications), Mu Wang (Beijing University of Posts and Telecommunications), Zhiqiang Hu (China Telecom), Yiying Lin (Beijing University of Posts and Telecommunications), Kan Zheng (Ningbo University), Enhuan Dong (Tsinghua University), Changqiao Xu (Beijing University of Posts and Telecommunications) and Su Yao (Tsinghua University)

3. Less is More: Prior-Free Link Selection for UAV Localization

XuanRui Zhang (Beihang University) and ZhiYi Liu (University of Science and Technology of China)

4. CLAR: Cross-layer Adaptive Routing via Fuzzy Reinforcement Learning in UASNs

Xinxiang Wang (Shandong University of Science and Technology), Hang Tao (Shandong University of Science and Technology), Jingyu Song (Shandong University of Science and Technology), Wei Shi (Qingdao University of Science and Technology) and Hanjiang Luo (Shandong University of Science and Technology)

Saturday, August 15, 13:50–14:38

Session 2: Network Traffic Measurement and Streaming Big Data Analytics

1. Erlang: A Three-party Collaborative Network Flow Measurement Framework

Bowen Sun (National University of Defense Technology), Lailong Luo (National University of Defense Technology), Zijian Deng (National University of Defense Technology), Chenjie Wang (National University of Defense Technology) and Shangsen Li (National University of Defense Technology)

2. MK-Index: A Metadata-Enhanced Index for Multi-Attribute Network Traffic Retrieval

Zhaoyin Hu (National University of Defense Technology), Tao Zhao (National University of Defense Technology), Fei Wang (National University of Defense Technology) and Shuhui Chen (National University of Defense Technology)

3. Resource-Aware Drift Monitoring in Data Streams: A Survey of Detection, Approximation, and Evaluation

Jiayi Liu (School of Computer Science and Engineering, Sun Yat-sen University), Xiaoxi Zhang (School of Computer Science and Engineering, Sun Yat-sen University) and Xu Chen (School of Computer Science and Engineering, Sun Yat-sen University)

4. Real-Time AI over Streaming Data: A Cross-Layer Survey of Systems and Design Principles

Zeteng Yan (Sun Yat-sen University), Xiaoxi Zhang (Sun Yat-sen University) and Xu Chen (Sun Yat-sen University)

Saturday, August 15, 14:40–15:28

Session 3: Edge Big Data Computing and AI Systems

1. Reactive Mitigation of Distribution Drift in Profile-Guided Optimization for Dynamic Neural Networks

Lingji Ouyang (USTC), Guopeng Li (USTC), Chengquan Feng (USTC) and Haisheng Tan (USTC)

2. H-SOAP: A Heterogeneous Offloading Framework for SVD-based KV Cache Compression and Management

Ziqi Fan (University of Science and Technology of China) and Bei Hua (University of Science and Technology of China)

3. Versatile: A Time-Energy Cooperative Aware Hierarchical Offloading and Scheduling Algorithm for Multiple DAGs in Edge Heterogeneous Scenarios

Pan Lu (Beihang University), Kuangyu Zheng (Beihang University), Jiao Wu (Northwestern Polytechnical University, Xi'an Aeronautics Computing Technique Research Institute, Aviation Industry Corporation of China, Ltd.), Ang Li (Beihang University), An Huang (Beihang University), YuLiang Lei (Xi'an Aeronautics Computing Technique Research Institute, Aviation Industry Corporation of China, Ltd.), Baisong Ren (Xiong'an National Innovation Center), Yan Li (Xiong'an National Innovation Center) and Yifan Wang (Xiong'an National Innovation Center)

4. Quality-Adaptive Online Scheduling for Streaming Analytics at the Edge

Jianming Xia (The 38th Research Institute of China Electronics Technology Group Corporation), Hanzhi Sun (University of Science and Technology of China), Chi Zhang (Hefei University of Technology) and Haisheng Tan (University of Science and Technology of China)

Saturday, August 15, 15:30–16:30

Session 4: Security, Privacy, and Verifiable Computing

1. Lattice-Based Conditional Privacy-Preserving Authentication with Robust Tracing for UAV Systems

Ziyi Chen (Southeast University), Liquan Chen (Southeast University), Bo Yang (Southeast University), Fanghui Hu (Southeast University) and Xinzao Jiang (Southeast University)

2. Lightweight Verifiable SM9-Based Privacy-Preserving Data Aggregation for Smart Grids with Batch Signature Verification

Jiale Li (Southeast University), Yixuan Shen (Southeast University), Yuning Luo (Southeast University) and Jianchang Lai (Southeast University)

3. LSH-Merkle: Accelerating Verifiable Data Structures via Locality-Sensitive Hashing

Linfan Gong (University of Sydney) and Gang Wang (Northeastern University)

4. A Blockchain-Enhanced CP-ABE Scheme with Hidden Policy Supporting Fine-Grained Trace and Revocation in Multi-Domain Internet of Vehicles

Pengbo Wang (School of Cyberspace Science and Technology, Beijing Institute of Technology), Chang Xu (School of Cyberspace Science and Technology, Beijing Institute of Technology), Zijian Zhang (School of Cyberspace Science and Technology, Beijing Institute of Technology) and Zichao Bu (Transaction Banking Department E-Channel Division, CHINA CITIC BANK)

5. Medical Image Encryption Algorithm Based on Random-Layer Spiral Transform and Dual Rotation Diffusion

Quanyv Wang (Anhui Jianzhu University) and Yalong Yang (Anhui Jianzhu University)

Saturday, August 15, 16:32–17:20

Session 5: Cybersecurity Analytics for Big Data

1. GEAR: Graph-Enhanced Anomaly-Driven Attack Chain Reconstruction from Network Flows

Fanyou Zhao (Tsinghua University), Zhiliang Wang (Tsinghua University), Songyun Wu (Tsinghua University), Zheyu Jiang (Tsinghua University), Junhua Ren (SinorRail (Beijing) Network Technology Research Institute Co. Ltd) and Xiaoqian Lu (SinorRail (Beijing) Network Technology Research Institute Co. Ltd)

2. CATS: Calibration-Aware Encrypted Traffic Classification with Selective Prediction

Lu Yang (The Unit of 91977 of PLA), Zuojia Chen (The Unit of 91977 of PLA) and Yunke Zou (The Unit of 91977 of PLA)

3. An Unsupervised Clustering Side-Channel Analysis Framework Based on Dual-Domain Fusion

Haoyang Li (International School, Beijing University of Posts and Telecommunications, Beijing, China), Shaofei Sun (School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing, China), Han Wang (National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing, China), Xing Fang (Beijing Institute of Computer Technology and Application, Beijing, China) and Jiongjia Zhu (International School, Beijing University of Posts and Telecommunications, Beijing, China)

4. Unsupervised Isolation-based Adaptive Hardware Trojan Detection with Silhouette Feature Guidance

Jiongjia Zhu (International School, Beijing University of Posts and Telecommunications), Shaofei Sun (School of Cyberspace Security, Beijing University of Posts and Telecommunications), Xing Fang (Beijing Institute of Computer Technology and Application), Han Wang (National Computer Network Emergency Response Technical Team/Coordination Center of China) and Haoyang Li (International School, Beijing University of Posts and Telecommunications)

Saturday, August 15, 17:24–18:00

Session 6: Optimization and Intelligent Decision-Making

1. LLM-based Adaptive Scenario Genetic Algorithm for Intelligent Workshop Scheduling

Mingyang Fang (USTC), Shan Chen (Anhui GujingGongjiu Co., Ltd) and Haisheng Tan (USTC)

2. Physics-Aware Heterogeneous Graph Reinforcement Learning for Decoupling Capacitor Optimization

Zimo Wang (University of Science and Technology of China)

3. Online 3D Bin Packing with Adaptive Candidate Position Generation

Yi Bu (University of Science and Technology of China, Hefei, China), Huiyou Zhan (University of Science and Technology of China, Hefei, China) and Haisheng Tan (University of Science and Technology of China, Hefei, China)

Sunday, August 16, 8:00–9:00

Session 7: Federated Big Data Learning, Privacy, and Robustness

1. HAS-D: A Hybrid Adaptive Subspace Defense Against Stealthy Poisoning in Federated Learning

Yifeng Tang (Guangxi Engineering Research Center of Industrial Internet Security and Blockchain, Guilin University of Electronic Technology), Zhaoyu Su (Guangxi Engineering Research Center of Industrial Internet Security and Blockchain, Guilin University of Electronic Technology), Chunhai Li (Guangxi Engineering Research Center of Industrial Internet Security and Blockchain, Guilin University of Electronic Technology), Mingfeng Chen (Guangxi Engineering Research Center of Industrial Internet Security and Blockchain, Guilin University of Electronic Technology), Lanrui Li (Guangxi Engineering Research Center of Industrial Internet Security and Blockchain, Guilin University of Electronic Technology), Kang Yang (Guangxi Engineering Research Center of Industrial Internet Security and Blockchain, Guilin University of Electronic Technology) and Wenyi Zhu (Guangxi Engineering Research Center of Industrial Internet Security and Blockchain, Guilin University of Electronic Technology)

2. Unlearning Backdoors in Federated Learning via Robust Gradient Rectification and Attention Regularization

Yunhui Shen (Guangxi Engineering Research Center of Industrial Internet Security and Blockchain, Guilin University of Electronic Technology), Zhaoyu Su (Guangxi Engineering Research Center of Industrial Internet Security and Blockchain, Guilin University of Electronic Technology), Chunhai Li (Guangxi Engineering Research Center of Industrial Internet Security and Blockchain, Guilin University of Electronic Technology), Mingfeng Chen (Guangxi Engineering Research Center of Industrial Internet Security and Blockchain, Guilin University of Electronic Technology) and Ru Jia (Guangxi Engineering Research Center of Industrial Internet Security and Blockchain, Guilin University of Electronic Technology)

3. DAILoRA-Fed: Privacy-Preserving Incremental Analysis of Cross-Domain Network Traffic Big Data

Tianyu Han (Northeastern University), Jinpeng Han (Northeastern University), Dong Li (Dalian Naval Academy) and Rongfei Zeng (Northeastern University)

4. Communication-Efficient and Secure Vertical Federated Learning for Meteorological Data

Shenyi Qi (School of Computer Science and Technology), Qing Li (Tianjin Jinnan Meteorological Service), Wenchun He (National Meteorological Information Centre), ZhiZhong Cai (Haikou University of Economics) and Xiyue Wang (Key Laboratory of Meteorological Software)

5. Vertical Federated Rainfall Prediction with Differential Privacy and Secure Aggregation for Multi-Meteorological Stations

Shenyi Qi (School of Computer Science and Technology), Qing Li (Tianjin Jinnan Meteorological Service), Wenchun He (National Meteorological Information Centre), ZhiZhong Cai (Haikou University of Economics) and Xiyue Wang (Key Laboratory of Meteorological Software)

Sunday, August 16, 9:02–10:02

Session 8: Large Models for Big Data Intelligence and Reliable Reasoning

1. SafeVET: Leakage-Resistant Text-to-SQL via Runtime Auditing in Verifiable Execution Tracing

Dongyu Wang (University of Science and Technology of China), Guoxin Liu (Anhui University), Xide Zou (Anhui University) and Lan Zhang (University of Science and Technology of China)

2. SR-LoftQ: Stable-Rank-Aware LoRA Initialization for Quantized Large Language Models

Zilong Wang (School of Computer Science and Technology, University of Science and Technology of China (USTC)), Yucang Yang (Tsinghua University, China Unicom Group), Hai Ding (China Unicom Group), Yang Tang (China Unicom Group) and Haisheng Tan (School of Artificial Intelligence and Data Science, University of Science and Technology of China (USTC))

3. Cross-Agent Confidence-Weighted Process Reward Construction for LLM Multi-Round Collaboration

Tianxiang Jiang (University of Science and Technology of China), Mingwei Fu (University of Science and Technology of China), Qi Dong (University of Science and Technology of China) and Mengxiao Zhu (University of Science and Technology of China)

4. Generation Enhancement and External-Feedback-Driven Iterative Correction for Natural Language Optimization Modeling

Fang Junhao (University of Science and Technology of China), Pan Xiaotian (University of Science and Technology of China), Wu Feng (University of Science and Technology of China) and Li Xiang-Yang (University of Science and Technology of China)

5. Enhancing Fitness Intelligence through Domain-Specific LLM Post-Training

Xingtao Zhao (Beihang University), Tian Yang (Renmin University of China) and Han Jiang (Beihang University)

Sunday, August 16, 10:04–10:52

Session 9: Big Data Representation Learning and Knowledge Intelligence

1. PESL: Improving Embedding Learning for CTR Prediction with User-Item Pairwise Similarity Loss

Yifang Zhang (University of Science and Technology of China) and Lan Zhang (University of Science and Technology of China)

2. Triggered Replay-Based Recovery for Incremental Knowledge Graph Embedding

Rong Lin (University of Science and Technology of China), Ziwen Wang (University of Science and Technology of China), Pengfei Zhou (University of Science and Technology of China) and Wangqiu Zhou (Hefei University of Technology)

3. Outcome-Aware Cross-Modal Contrastive Learning for Reaction Performance Prediction

Zhen Huang (University of Science and Technology of China)

4. KECQA: A Knowledge Enhanced Chemical Question Answering Framework

Bin Guo (University of Science and Technology of China) and Yuhang Zhang (University of Science and Technology of China)

Sunday, August 16, 10:54–11:42

Session 10: Multimodal Learning for Human-Centered Applications

1. Template-Free Brain MRI Alignment Using Outlier-Robust Wasserstein Distance under Rigid Transformation

Wei Li (University of Science and Technology of China), Xu Wang (University of Science and Technology of China), Fuyou Miao (University of Science and Technology of China) and Yan Xiong (University of Science and Technology of China)

2. ACES: A Collaborative Behavior Sequence Modeling and Evaluation Method

Han Zhao (University of Science and Technology of China), Wei Huang (National Education Examinations Authority), Mo Zhang (Educational Testing Service), Li Feng (University of Science and Technology of China) and Mengxiao Zhu (University of Science and Technology of China)

3. Bi-Wrist: Multi-Modal Gesture Recognition and User Authentication on COTS Smartwatches

Feng Guo (Beijing Institute of Technology), Fan Li (Beijing Institute of Technology) and Zhengsong Li (Beijing Institute of Technology)

4. LeakGuard: Mitigating Membership Inference Attacks via Confidence-Regularized Training

Zekun Xu (The University of Sydney), Yuanjing Wang (Nanjing University of Posts and Telecommunications), Yuhan Xie (Nanjing University of Posts and Telecommunications) and Haiping Huang (Nanjing University of Posts and Telecommunications)

Sunday, August 16, 11:44–12:20

Session 11: Urban Big Data Analytics for Intelligent Transportation and Logistics

1. Traffic Flow Prediction via Gated Hybrid-hop Graph Convolution and Prototype-based Spatial Attention

Xiaoqian Tang (University of Science and Technology of China, School of Computer Science and Technology), Lexiang Mei (University of Science and Technology of China, School of Artificial Intelligence and Data Science), Li Sun (Anhui Expressway Network Operation Co., Ltd.) and Haisheng Tan (University of Science and Technology of China, School of Artificial Intelligence and Data Science)

2. Spatiotemporal Delivery Risk Prediction under Temporal Drift and Cold-Start

Ke Huang (Northwestern Polytechnical University), Yan Liu (Northwestern Polytechnical University), Bin Guo (Northwestern Polytechnical University), Yasan Ding (Northwestern Polytechnical University), Jing Zhang (Xi'an University of Science and Technology) and Zhiwen Yu (Harbin Engineering University)

3. Highway Capture System: Design and Practice

Yilong Shi (Traffic Management Research Institute of the Ministry of Public Security (TMRI), Wuxi 214151, China), Yuedong Ruan (Zhejiang Dahua Technology Co., Ltd, Hangzhou 310051, China), Donghai You (Traffic Management Research Institute of the Ministry of Public Security (TMRI), Wuxi 214151, China), Ge Zhang (Traffic Management Research Institute of the Ministry of Public Security (TMRI), Wuxi 214151, China) and Qing Li (Chengdu Municipal Public Security Bureau Traffic Management Bureau, Chengdu 610017, China)

CALL FOR PAPERS

Big Data lies at the center of modern science and technology, with major advances in analyzing & learning from Big Data concurrently reshaping human knowledge, society, and economy. The overwhelming amounts of data generated in many applications (fundamental sciences, cyber-physical systems, smart cities, sensor networks, and many more) alongside the urge for fast and effective handling and decision-making, in real-time, pose a number of significant challenges on the underlying system design and methods.

The 12th International Conference on Big Data Computing and Communications (BigCom2026), which is to be held on August 14 – 16, 2026 in Bejing, China. The conference aims to attract researchers and practitioners with interest in the theme of Big Data, in its broadest sense: analytics, management, security and privacy, communications and networking, and high-performance computing. We welcome original, unpublished research papers that emphasize theoretical foundations, architecture design, modeling, algorithmic methodologies, and data-driven applications and management in science and engineering. We also welcome visionary papers on new and emerging topics, such as Metaverse, Digital Twin, Generative AI, and etc.

We welcome high quality papers that describe original and unpublished research advancing the state of the art in big data and communications. Topics for submissions include but not limited to the following:

Track 1. Architecture and Systems for Big Data

Track chair

Track 2. Algorithms for Big Data

Track chair

Track 3. AI for Big Data

Track chair

Track 4. Security and Privacy for Big Data

Track chair

Track 5. Communication and Networking for Big Data

Track chair

Important Dates

All deadlines are at 11:59 PM Pacific Standard Time.

First Round Deadline:
Paper submission: March 17, 2026
Acceptance: April 19, 2026
Camera Ready: April 30, 2026

Second Round Deadline:
Paper submission: June 5, 2026
Acceptance: June 30, 2026
Camera Ready: July 7, 2026

Conference dates: August 14–16, 2026

Paper Submission

Review policy: Authors may choose either to include or to exclude their identify in the submission. The program committee members are instructed not to disadvantage a submission either way.

Papers that do not adhere to the following guidelines will be rejected without review:

Submitted in PDF format

The page limits are 8 pages (including reference).

US letter-sized (8.5 inches x 11 inches) and two-Column format

Font size no smaller than 10 points

The conference proceedings will be published by Conference Publishing Services (CPS) and submitted for indexing by EI. Selected papers will be recommended to publish at SCI-indexed journals.

For details please check Submit.

Organizing Committee

General Chairs:
Liehuang Zhu, Beijing Institute of Technology, China
Christos Anagnostopoulos, University of Glasgow, United Kingdom

TPC Co-Chairs:
Song Yang, Beijing Institute of Technology, China
Thar Baker, University of Khorfakkan, U.A.E.
Qi Song, University of Science and Technology of China, China

Track 1 TPC

Fangda Guo, guofangda@ict.ac.cn, Institute of Computing Technology, Chinese Academy of Sciences
Aman Abidi, aabidi@swin.edu.au, Swinburne University of Technology
Xiaofan Li, xiaofanli@nju.edu.cn, Nanjing University
Hao Wang, haowang@dlut.edu.cn, Dalian University of Technology
Liangtian Wan, wanliangtian@dlut.edu.cn, Dalian University of Technology
Yuncan Zhang, zhangyc29@mail.sysu.edu.cn, Sun Yat-sen University‌
Lizhen Zhou, zlz@nuist.edu.cn, Nanjing University of Information Science and Technology‌
Shucun Fu, shucunfu@nuist.edu.cn, Nanjing University of Information Science and Technology‌

Track 2 TPC

Tong Zhang, zhangt@nuaa.edu.cn, Nanjing University of Aeronautics and Astronautics
Yi Tian, tiany_0613@163.com, Northeastern University
Cunqian Yu, yucunqian@163.com, Shenyang Aerospace University
Qiang Su, qiangsu@xmu.edu.cn, Xiamen University
Chengxi Gao, chengxi.gao@siat.ac.cn, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
Dongbo Li, ldb@hit.edu.cn, Harbin Institute of Technology
Donglong Chen, donglongchen@uic.edu.cn, Beijing Normal-Hong Kong Baptist University

Track 3 TPC

wenjingx@gxu.edu.cn
ran.ran.wang@ieee.org
binglei_yue@std.uestc.edu.cn
chijiang@std.uestc.edu.cn
hongboyin@std.uestc.edu.cn
luoxiaorui@std.uestc.edu.cn
shaofeishi@std.uestc.edu.cn
xihanliu@std.uestc.edu.cn

Track 4 TPC

Yuezhi Che, Wuhan University, cheyuezhi@whu.edu.cn
Jiaju Bai, Beihang University, baijiaju@buaa.edu.cn
Song Bian, Beihang University, sbian@buaa.edu.cn
Xiaoya Xu, Beijing Computer Technology and Application Research Institute, cherie_xu@buaa.edu.cn
He Lu, Nanyang Technological University, luhe0002@e.ntu.edu.sg
Jun Tang, 2024040410@njupt.edu.cn, Nanjing University of Posts and Telecommunications
Yuanjing Wang, 2024040411@njupt.edu.cn, Nanjing University of Posts and Telecommunications
Wenming Wang, wy523@aqnu.edu.cn, Anqing Normal University
Shuyu Chang shuyu_chang@njupt.edu.cn, Nanjing University of Posts and Telecommunications
Lingyan Xue xly412102@163.com, Nanjing University of Finance & Economics
Hancheng Gao, gaohc98@163.com, Nanjing University of Posts and Telecommunications
Chao Sha, shac@njupt.edu.cn, Nanjing University of Posts and Telecommunications
Shijie Zhang, zmxj0824@gmail.com, Nanjing University of Posts and Telecommunications
Olaoluwa Adeleke, adeleke.j.olaoluwa@gmail.com, The Polytechnic Ibadan
Wei-Chiang Hong, samuelsonhong@gmail.com, Harbin Engineering University
Lip Yee Por, porlip@um.edu.my, University of Malaya
Arash Heidari, arash_heidari@ieee.org, Islamic Azad University
sanjay kumar, skumar.it@nitrr.ac.in, NIT Raipur
Wei Feng, fengw1981@126.com, Panzhihua University, Panzhihua
Yi-Fan Tseng, yftseng@cs.nccu.edu.tw, National Chengchi University
Jinbo Xiong, jbxiong@fjnu.edu.cn, Fujian Normal University
Yuezhi Che, cheyuezhi@whu.edu.cn, Wuhan University
Jiaju Bai, baijiaju@buaa.edu.cn, Beihang University
Song Bian, sbian@buaa.edu.cn, Beihang University
Xiaoya Xu, cherie_xu@buaa.edu.cn, Beijing Institute of Computer Technology and Applications
He Lu, luhe0002@e.ntu.edu.sg, Nanyang Technological University
Chuanxin, Zhao, zcxonline@126.com, Anhui Normal University

Track 5 TPC

Dongbiao He, hdb@seu.edu.cn, Southeast University
Pengfei Xue, xuepengfei@nudt.edu.cn, National University of Defense Technology
Songyun Wu, shoinsong@163.com, Zhongguancun Laboratory
Jia Zhang, zhangj@mail.zgclab.edu.cn, Zhongguancun Laboratory
Yali Yuan, yaliyuan@seu.edu.cn, Southeast University
Dongqi Han, handongqi@bupt.edu.cn, Beijing University of Posts and Telecommunications
Renjie Xie, rexie@guet.edu.cn, Guilin University of Electronic Technology
Mu Wang, muwang@bupt.edu.cn, Beijing University of Posts and Telecommunications
Junqi Chen, cookiechen@mails.guet.edu.cn, Guilin University of Electronic Technology
Tengfei Cao, caotf@qhu.edu.cn, Qinghai University
Hao Hao, 2865442105@qq.com, Shandong Computer Science Center (National Supercomputer Center in Jinan)
Jiankun Sun, jiankun.s@outlook.com, Zhongguancun Laboratory

General Chairs

Liehuang Zhu, Beijing Institute of Technology, China
Christos Anagnostopoulos, University of Glasgow, United Kingdom

TPC Co-Chairs

Song Yang, Beijing Institute of Technology, China
Thar Baker, University of Khorfakkan, U.A.E.
Qi Song, University of Science and Technology of China, China

TPC Vice Chair

Xiaoxi Zhang, Sun Yat-sen University, China

Local Arrangement Chairs

Youqi Li, Beijing Institute of Technology, China
Peng Gong, Beijing Institute of Technology, China

Publicity Chairs

Ruiting Zhou, Southeast University, China
Su Yao, Tsinghua University, China

Publication Chairs

Fan Li, Beijing Institute of Technology, China
Haohua Du, Beihang University, China

Industry Liaison Chairs

Xinwei Yao, Zhejiang University of Technology, China
Yu Wu, Dongguan University of Technology, China

Rising Star Track Chair

Haisheng Tan, University of Science and Technology of China, China

Finance and Registration Chairs

Xuerong Huang, University of Science and Technology of China, China
Juan Yao, University of Science and Technology of China, China

Web Chairs

Tianxing Xiao, Beijing Institute of Technology, China

Track Chairs

Zichuan Xu, Dalian University of Technology, China
Rui Zhou, Swinburne University of Technology, Australia
Rongfei Zeng, Northeast University, China
Yupeng Li, Hong Kong Baptist University, China
Yin Zhang, University of Electronic Science and Technology of China, China
Limei Peng, Kyungpook National University, South Korea
Haiping Huang, Nanjing University of Posts and Telecommunications, China
Haohua Du, Beihang University, China
Enhuan Dong, Tsinghua University, China
Tingting Yuan, IMC Univ. of Applied Sciences Krems, Austria

TPC Members

TPC members: please refer to the TPC Members section.

Program Committee

1. Architecture and Systems for Big Data

Zichuan Xu, Dalian University of Technology, China
Rui Zhou, Swinburne University of Technology, Australia

2. Algorithms for Big Data

Rongfei Zeng, Northeast University, China
Yupeng Li, Hong Kong Baptist University, China

3. AI for Big Data

Yin Zhang, University of Electronic Science and Technology of China, China
Limei Peng, Kyungpook National University, South Korea

4. Security and Privacy for Big Data

Haiping Huang, Nanjing University of Posts and Telecommunications, China
Haohua Du, Beihang University, China

5. Communication and Networking for Big Data

Enhuan Dong, Tsinghua University, China
Tingting Yuan, IMC Univ. of Applied Sciences Krems, Austria

TBA

Sponsorships TBA

BigCom 2026 will be held in Beijing, China from August 14 to 16, 2026. For more information about the venue, check below.

I. Information

北京世纪金源大饭店 / EMPARK GRAND HOTEL BEIJING
Hotel Website: click here
Tel: +86-10-88598888
Address: No. 69, Banjing Road, Haidian District, Beijing

only for reference
>> Standard single room: RMB 750 / night (about USD 111 / night). (One bed, Internet and one breakfast included)
>> Standard double room: RMB 750 / night (about USD 111 / night). (Two beds, Internet and one breakfast included)

II. Room Booking

To make a reservation, please contact the local arrangement liaison:
Pengfei Su 苏鹏飞
Tel: +86-199-2152-0568
Please mention that you are attending BIGCOM 2026 to receive the conference rate.

III. Directions

* Beijing Capital International Airport (PEK): 36 km, 50 minutes
* Beijing Daxing International Airport (PKX): 60 km, 65 minutes
* Beijing East Railway Station: 22 km, 45 minutes
* Beijing West Railway Station: 12 km, 20 minutes
* Beijing South Railway Station: 21 km, 35 minutes
* Beijing North Railway Station: 7 km, 18 minutes

IV. Tips

For non-Chinese attendees, a "Bring me to the hotel" card is available below. You can show it to taxi drivers and they will take you to the hotel.

请带我去：

北京市海淀区板井路69号，世纪金源大饭店。谢谢！

The above Chinese text means:
Please bring me to EMPARK GRAND HOTEL BEIJING, thank you!
Hotel address: No. 69, Banjing Road, Haidian District, Beijing.

Paper Submission Instructions

General information about submitting papers to BigCom2026, including submission deadline dates, is available in the Call for Papers. This page details the actual submission process, including the requirements for formatting your paper. Submitted papers must be unpublished and must not be currently under review for any other publication. Our proceedings will be published by Conference Publishing Services (CPS) and submitted for indexing by EI. The selected papers will be recommended and published by SCIE journals.

Before submitting your paper, please check the description of the conference scope in the Call for Papers. BigCom covers all issues in big data computing and communications on their theories and applications. If you are unsure whether your work falls within the scope of the conference, please contact the corresponding track chairs.

For the camera-ready version, please submit your final version via "Edit Submission" after the final version submission becomes available.

Formatting Your Paper for Submission

Submitted papers must be written in the English language, with a maximum length limit of 8 printed pages, including figures, tables, appendices, and references. Papers that do not comply with the length limit will not be reviewed. Use the standard IEEE Transactions templates for Microsoft Word or LaTeX formats found at: https://www.ieee.org/conferences_events/conferences/publishing/templates.html. If the paper is typeset in LaTeX, please use an unmodified version of the LaTeX template IEEEtran.cls version 1.8, and use the preamble:
\documentclass[10pt, conference, letterpaper]{IEEEtran}

Do not use additional LaTeX commands or packages to override and change the default typesetting choices in the template, including line spacing, font sizes, margins, space between the columns, and font types. This implies that the manuscript must use 10-point Times font, two-column formatting, as well as all default margins and line spacing requirements as dictated by the original version of IEEEtran.cls version 1.8.

If you are using Microsoft Word to format your paper, you should use an unmodified version of the Microsoft Word IEEE Transactions template (US letter size). Regardless of the source of your paper formatting, you must submit your paper in the Adobe PDF format.

The paper must print clearly and legibly, including all the figures, on standard black-and-white printers. Reviewers are not required to read your paper in color. The submitted manuscript should be self-contained within 8 pages. Inclusion of additional material (e.g., a technical report containing the detailed math proof through an anonymous Dropbox or OneDrive link) is not allowed.

Please be sure your paper is formatted properly for submission. In particular, please carefully follow all of the following formatting requirements:

Your submission must be in PDF. We will not accept the papers in any other format.

Your submission must use at least 10pt fonts and be correctly formatted for printing on Letter-sized (8.5" by 11") paper.

The page limits are 8 pages (including reference).

The paper must print clearly on standard black-and-white printers. Reviewers are not required to view your paper in color.

Submitting Your Paper

Submission link is here(www.bigcom2026.com/bigcom2026a).
If you have any questions about the submission process or paper format, please contact at bigcom2026@gmail.com.

Registration

To all participants: Kindly register for the conference by 10 July 2026.

Registration Policy

Each accepted paper must be registered with the Author rate.

All registrations (Author / Non-Author) allow full access to BigCom 2026, including Keynotes, Technical Sessions, Panels, Proceedings, Breaks, Welcome Reception & Coffee Break.

Registration Fee

USD value is indicative, based on the exchange rate as of 2026-06-15 (1 USD ≈ 6.74 CNY). Payment is collected in CNY; the actual USD equivalent may vary at the time of conversion.
Early Bird Deadline: July 10, 2026

Early Bird — Author

¥ 6,000 CNY

≈ USD $890

Regular — Author

¥ 6,500 CNY

≈ USD $959

Early Bird — Non-Author

¥ 3,000 CNY

≈ USD $443

Regular — Non-Author

¥ 3,000 CNY

≈ USD $443

Step 1 — Register & Pay on ScienceMate

Both registration and payment are processed through ScienceMate (科学邦), our official academic conference service platform.

Go to ScienceMate →

Step 2 — Register on BigCom 2026 Paper Submission Management system (Author)

All authors must create an account on the BigCom 2026 paper submission management system.

Sign up on Submission System →

Cancellation & Refund

All refund/cancellation requests must be received in writing to bigcom2026@gmail.com by 30 June, 2026, 11:59 PM Pacific Standard Time.
There will be a 20% cancellation fee for Non-Author registrations. Refunds will be processed through ScienceMate after the request is approved by the organizers.
Author registrations are non-refundable.

For any questions, contact bigcom2026@gmail.com.

Keynote

Begin at 9:00 a.m. on August 15.

Title 1: The Role of Unlabeled Data in Regularization and Optimal Statistical Learning
Speaker 1: Shang-Hua Teng, Professor of Computer Science and Mathematics, USC, USA
Abstract: Unlabeled data plays a central role in modern machine learning, yet its contribution to learnability and regularization remains poorly understood. At the same time, empirical risk minimization (ERM) is known to fail in settings where uniform convergence does not characterize learnability. More broadly, while machine learning relies heavily on algorithmic techniques such as regularization, no single principle has emerged as the characterization of optimal learning in these settings. In this talk, I will describe a precise characterization of the role of regularization in perhaps the simplest setting where ERM fails: Multiclass learning with arbitrary label sets. Using one-inclusion graphs (OIGs), we develop optimal learning algorithms that naturally unify several classical principles, including Occam’s Razor through structural risk minimization (SRM), maximum-entropy methods, and Bayesian inference. Our characterization reveals a surprising role for unlabeled data: it enables forms of local regularization that are sufficient for optimal learning. These results provide new insights into both the power of unlabeled data and the foundations of regularization in machine learning. Joint work with Julian Asilis, Siddartha Devic, Shaddin Dughmi, and Vatsal Sharan.
Biography: Shang-Hua Teng is a USC University Professor of Computer Science and Mathematics. He is a fellow of SIAM, ACM, and Alfred P. Sloan Foundation, and has twice won the Gödel Prize, first in 2008, for developing smoothed analysis, and then in 2015, for designing the breakthrough scalable Laplacian solver. Citing him as, “one of the most original theoretical computer scientists in the world”, the Simons Foundation named him a 2014 Investigator to pursue long-term curiosity-driven fundamental research. He also received the 2009 Fulkerson Prize, 2023 Science & Technology Award for Overseas Chinese from the China Computer Federation, 2021 and 2025 ACM STOC Test of Time Awards (for smoothed analysis and max-flow computation), 2022 ACM SIGecom Test of Time Award (for settling the complexity of computing a Nash equilibrium), 2026 Shanghai Jiaotong Ruiyuan Prize, and multiple conference recognizations (ISAAC 2009 Best Paper, STOC 2011 Best Paper, KDD 2026 Best Paper Runner-Up). In addition, he co-developed the first optimal well-shaped Delaunay mesh generation algorithms for arbitrary three-dimensional domains, settled the Rousseeuw-Hubert regression-depth conjecture in robust statistics, and resolved two long-standing complexity-theoretical questions regarding the Sprague-Grundy theorem in combinatorial game theory. For his industry work with Xerox, NASA, Intel, IBM, Akamai, and Microsoft, he received fifteen patents in areas including compiler optimization, Internet technology, and social networks, and was recently named a fellow of the National Academy of Inventors.

Title 2: Edge Intelligence for Next-Generation AI Services
Speaker 2: Geyong Min, Professor of High Performance Computing and Networking in the Department of Computer Science, University of Exeter, U.K.
Abstract: The next generation of AI applications demands intelligent computing infrastructures capable of delivering low latency, scalability, and adaptability close to where data are generated. Intelligent edge computing has therefore emerged as a key enabler of edge intelligence, supporting real-time AI services across diverse and dynamic environments. This talk presents recent advances in building intelligent edge infrastructures through resource deployment and service optimization. We first introduce a cooperative edge server deployment architecture that reduces infrastructure overhead while improving resource utilization. To address increasingly dynamic deployment scenarios, we further explore a hybrid server deployment paradigm that enhances both spatial adaptability and computational efficiency. In addition, we present a task-aware service placement mechanism that optimizes service provisioning according to application demands and available edge resources. Together, these solutions establish a scalable and efficient foundation for supporting next-generation AI services and intelligent applications.
Biography: Professor Geyong Min is a Chair in High Performance Computing and Networking in the Department of Computer Science at the University of Exeter, UK. His research interests include Computer Networks, Cloud and Edge Computing, Mobile and Ubiquitous Computing, Systems Modelling and Performance Engineering. His recent research has been supported by Horizon Europe, UKRI, EPSRC, Royal Society, Royal Academy of Engineering, and industrial partners. He has published more than 200 research papers in leading international journals including IEEE/ACM Transactions on Networking, IEEE Journal on Selected Areas in Communications, IEEE Transactions on Computers, IEEE Transactions on Parallel and Distributed Systems, and IEEE Transactions on Wireless Communications, and at reputable international conferences, such as SIGCOMM-IMC, INFOCOM, and ICDCS. He is an Associated Editor of several international journals, e.g., IEEE Transactions on Parallel and Distributed Systems, IEEE Transactions on Computers, and IEEE Transactions on Cloud Computing. He served as the General Chair or Program Chair of a number of international conferences in the area of Information and Communications Technologies.

Title 3: From Artificial Intelligence to Processor Chips
Speaker 3: Yunji Chen, Deputy Director, Institute of Industrial Artificial Intelligence & Institute of Computing Technology, CAS, China
Abstract: -
Biography: Prof. Yunji Chen is Deputy Director of the Institute of Industrial Artificial Intelligence and the Institute of Computing Technology at CAS, Director of the National Key Laboratory of Processor Chips, and Vice Chairman of the All-China Youth Federation. He has been engaged in interdisciplinary research at the intersection of processor chips and artificial intelligence for many years. He developed Cambricon-1, the world's first deep learning processor chip, and was recognized by Science magazine as a "pioneer" and "leader" in the field of deep learning processors. He also served as one of the chief architects of the Loongson-3 CPU. As the principal investigator, he was awarded the Second Prize of the National Natural Science Award of China — the first and only national natural science award in the history of China's processor chip field. He is a recipient of the National Science Fund for Distinguished Young Scholars (with extended funding), the National May 1st Labor Medal, the China Youth May 4th Medal, and the Ho Leung Ho Lee Prize for Scientific and Technological Innovation. He was named one of MIT Technology Review's 35 Innovators Under 35.