Map
Kempinski Hotel Dalian, Liaoning China
地址：辽宁省大连市凯宾斯基饭店
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.
All the regular papers will be considered for the Best Paper Award.
All deadlines are at 11:59 PM Pacific Standard Time.
First Round:
Paper submission: March 4, 2024 March 25,2024
Author notification: March 29, 2024 April 12,2024
Camera Ready: April 10, 2024 May 21,2024
Second Round:
Paper submission: April 30, 2024 May 16,2024
Author notification: May 18, 2024 June 6,2024
Camera Ready: May 30, 2024 June 30,2024
Conference dates: August 9-11, 2024
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.
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 10th International Conference on Big Data Computing and Communications (BigCom2024), which is to be held on August 9 – 11, 2024 in Dalian, 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 We welcome original, unpublished research papers that emphasize theoretical foundations, architecture design, modeling, algorithmic methodologies, and data-driven applications and management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.
All deadlines are at 11:59 PM Pacific Standard Time.
First Round:
Paper submission: March 4, 2024 March 25,2024
Author notification: March 29, 2024 April 12,2024
Camera Ready: April 10, 2024 May 21,2024
Second Round:
Paper submission: April 30, 2024 May 16,2024
Author notification: May 18, 2024 June 6,2024
Camera Ready: May 30, 2024 June 30,2024
Conference dates: August 9-11, 2024
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:
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.General Co-Chairs:
Zhongxuan Luo, Dalian University of Technology, China
Omer F. Rana, Cardiff University, United Kingdom
TPC Co-Chairs:
Ramin Yahyapour, University of Göttingen, Germany
Lei Wang, Dalian University of Technology, China
TBA
Sponsorships TBA
General information about submitting papers to BigCom2024, 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.
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.
Camera-ready Formatting:
| Early Bird (Before Jul. 20, 2024, 23:59:59) | Late (After Jul. 20, 2024, 23:59:59) | |
| Full Registration | USD 770 / RMB 5500 | USD 840 / RMB 6000 |
| Non-Student Registration | USD 770 / RMB 5500 | USD 840 / RMB 6000 |
| Student Registration | USD 385 / RMB 2750 | USD 420 / RMB 3000 |
| Non-author limited registration | USD 140 / RMB 1000 | USD 140 / RMB 1000 |
|
Prof. Jie WuTemple University，USA |
|
|
TITLE: Edge-Cloud Networks for Efficient AI/ML Implementations ABSTRACT: Edge-cloud networks connect a wide range of systems and devices, ranging from large data centers to small IoT devices. The potential advantages of adopting edge and cloud networking include the quick adaptation of new technologies, including a faster rollout and adoption of software and feature updates, as well as better management of various resources, including network and edge devices. This talk provides an overview of some challenges in efficient support for running AI/ML algorithms in edge-cloud networks. Our focus is on low latency, connectivity, and local data processing while still achieving efficiency. We will look at two specific examples of AI/ML implementations: one is the optimal offloading of AI/ML code from IoT/edge devices to the cloud, and the other is exploring network topology and connectivity for efficient decentralized federated learning. Biography: Jie Wu is Laura H. Carnell Professor at Temple University and the Director of the Center for Networked Computing (CNC). He served as Chair of the Department of Computer and Information Sciences from the summer of 2009 to the summer of 2016 and Associate Vice Provost for International Affairs from the fall of 2015 to the summer of 2017. Prior to joining Temple University, he was a program director at the National Science Foundation and was a distinguished professor at Florida Atlantic University, where he received his Ph.D. in 1989. His current research interests include mobile computing and wireless networks, routing protocols, network trust and security, distributed algorithms, applied machine learning, and cloud computing. Dr. Wu regularly published in scholarly journals, conference proceedings, and books. He serves on several editorial boards, including IEEE Transactions on Service Computing and Journal of Computer Science and Technology. Dr. Wu is/was general chair/co-chair for IEEE DCOSS’09, IEEE ICDCS’13, ICPP’16, IEEE CNS’16, WiOpt’21, ICDCN’22, IEEE IPDPS'23, ACM MobiHoc'23, and IEEE CCGrid 2024 as well as program chair/cochair for IEEE MASS’04, IEEE INFOCOM’11, CCF CNCC’13, and ICCCN’20. He was an IEEE Computer Society Distinguished Visitor, ACM Distinguished Speaker, and chair for the IEEE Technical Committee on Distributed Processing (TCDP). Dr. Wu is a Fellow of the AAAS and a Fellow of the IEEE. He is the recipient of the 2011 China Computer Federation (CCF) Overseas Outstanding Achievement Award. He is a Member of the Academia Europaea (MAE). Dr. Wu is currently on leave working at China Telecom as a scientist in cloud computing. |
||
|
Prof. Yi PanShenzhen Institue of Advanced Technology, Chinese Academy of Sciences, China |
|
|
TITLE: AIGC Empowers Biomedical Applications ABSTRACT: Starting from the current state of generative artificial intelligence (AIGC) and large language models (LLM), I will first discuss the basic principles and shortcomings of the latest AIGC products, such as ChatGPT and Sora, along with their future improvements and development trends. I will mainly elaborate on the important roles and value of AIGC in the biopharmaceutical field. Recently, ChatGPT outperformed 17 doctors by accurately diagnosing a rare disease in a 4-year-old boy. This demonstrates that, when applied appropriately, AI can indeed become an assistant in diagnosing and treating diseases. However, a study published in JAMA by Brigham and Women’s Hospital, affiliated with Harvard University, showed that ChatGPT's cancer treatment recommendations were only completely accurate in 62% of cases, indicating that its results should be applied cautiously. One solution to this issue is the use of content detection tools, such as AIGC-X and ZeroGPT. The vast information behind ChatGPT is an advantage, but in specialized fields, it also brings the downside of excessive interference information. To address this, our team has developed a large language model knowledge vector library system for autism that reduces training time and achieves similar objectives using only a small amount of training data. This lecture will also introduce the use of AIGC in designing new drug molecules. By inputting numerous small drug molecules related to the treatment of a particular disease into the AIGC system, new drug molecules can be generated. Coupled with our powerful AI drug screening capabilities, we have the potential to design new drugs suitable for specific targets. Biography: Dr. Yi Pan is currently a Chair Professor and the Dean of College of Computer Science and Control Engineering at Shenzhen Institue of Advanced Technology, Chinese Academy of Sciences, China and a Regents’ Professor Emeritus at Georgia State University, USA. He served as Chair of Computer Science Department at Georgia State University from 2005 to 2020. He has also served as an Interim Associate Dean and Chair of Biology Department during 2013-2017. Dr. Pan joined Georgia State University in 2000, was promoted to full professor in 2004, named a Distinguished University Professor in 2013 and designated a Regents' Professor (the highest recognition given to a faculty member by the University System of Georgia) in 2015. Dr. Yi Pan is Fellow of American Institute for Medical and Biological Engineering, Foreign Member of Russian Academy of Engineering, Foreign member of Ukrainian Academy of Engineering Science, Member of European Academy of Sciences and Arts, Fellow of the Royal Society for Public Health, Fellow of the Institute of Engineering and Technology, and Fellow of the Japan Society for the Promotion of Science. Dr. Pan received his B.Eng. and M.Eng. degrees in computer engineering from Tsinghua University, China, in 1982 and 1984, respectively, and his Ph.D. degree in computer science from the University of Pittsburgh, USA, in 1991. <>br Dr. Pan has published more than 450 papers including over 250 journal papers with more than 100 papers published in IEEE/ACM Transactions/Journals. In addition, he has edited/authored 43 books. His work has been cited more than 26200 times based on Google Scholar and his current h-index is 98. Dr. Pan is currently serving as Editor-in-Chief of Big Data Mining and Analytics (a top 3% journal), Associate Editor-in-Chief of Journal of Computer Science and Technology (JCST), and Chinese Journal of Electronics (CJE). Dr. Pan has served as an editor-in-chief or editorial board member for 20 journals including 7 IEEE Transactions. |
||
|
Prof. Kun YangNanjing University (Suzhou Campus), China |
|
|
TITLE: Data-driven Self-evolving Communication Networks ABSTRACT: Starting from the current state of generative artificial intelligence (AIGC) and large language models (LLM), I will first discuss the basic principles and shortcomings of the latest AIGC products, such as ChatGPT and Sora, along with their future improvements and development trends. I will mainly elaborate on the important roles and value of AIGC in the biopharmaceutical field. Recently, ChatGPT outperformed 17 doctors by accurately diagnosing a rare disease in a 4-year-old boy. This demonstrates that, when applied appropriately, AI can indeed become an assistant in diagnosing and treating diseases. However, a study published in JAMA by Brigham and Women’s Hospital, affiliated with Harvard University, showed that ChatGPT's cancer treatment recommendations were only completely accurate in 62% of cases, indicating that its results should be applied cautiously. One solution to this issue is the use of content detection tools, such as AIGC-X and ZeroGPT. The vast information behind ChatGPT is an advantage, but in specialized fields, it also brings the downside of excessive interference information. To address this, our team has developed a large language model knowledge vector library system for autism that reduces training time and achieves similar objectives using only a small amount of training data. This lecture will also introduce the use of AIGC in designing new drug molecules. By inputting numerous small drug molecules related to the treatment of a particular disease into the AIGC system, new drug molecules can be generated. Coupled with our powerful AI drug screening capabilities, we have the potential to design new drugs suitable for specific targets. Biography: Kun Yang received his PhD from the Department of Electronic & Electrical Engineering of University College London (UCL), UK. He is currently a Chair Professor in the School of Intelligent Software and Engineering, Nanjing University (Suzhou Campus), China and also affiliated with University of Essex, UK. His main research interests include wireless networks and communications, and edge computing. In particular he is interested in energy aspects of future communication systems and AI for wireless. He has managed research projects funded by UK EPSRC, EU FP7/H2020, and industries. He has published 500+ papers and filed 50 patents. He serves on the editorial boards of a number of IEEE journals (e.g., IEEE WCM, TNSE, TVT, TNB). He is a Deputy Editor-in-Chief of IET Smart Cities Journal. He has been a Judge of GSMA GLOMO Award at World Mobile Congress – Barcelona since 2019. He was a Distinguished Lecturer of IEEE ComSoc (2020-2021). He is a Member of Academia Europaea (MAE), a Fellow of IEEE, a Fellow of IET and a Distinguished Member of ACM. |