In response to the epidemic prevention, the organizing committee has decided that BIGCOM 2021 will be held as a virtual (online) conference.
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: February 28, 2021 March 7, 2021
Author notification: March 25, 2021 March 31, 2021
Camera Ready: April 10, 2021 (Firm)
Second Round:
Paper submission: May 7, 2021
Author notification: June 4, 2021
Camera Ready: June 18, 2021
Conference dates: August 13-15, 2021
Each paper will have a 12-minute presentation followed by a 3-minute Q&A. You need to pre-record the video and send it to your session chair on or before Aug. 12, 2021 (CST). The Video instructions can be found here. The contact information of the session chairs can be found in the above program file.
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 7th International Conference on Big Data Computing and Communications (BigCom2021), which is to be held on Aug. 13-15, 2021 in Deqing, China aims to address these challenges. 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:All deadlines are at 11:59 PM Pacific Standard Time.
First Round:
Paper submission: February 28, 2021 March 7, 2021
Author notification: March 25, 2021 March 31, 2021
Camera Ready: April 10, 2021 (Firm)
Second Round:
Paper submission: May 7, 2021
Author notification: June 4, 2021
Camera Ready: June 18, 2021
Conference dates: August 13-15, 2021
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:
Dan Wang, The Hong Kong Polytechnic University, Hong Kong
Wenyuan Xu, Zhejiang University, China
Yu Wang, Temple University, USA
TPC Co-Chairs:
Chenren Xu, Peking University, China
Dusan M. Stipanovic, University of Illinois at Urbana-Champaign, USA
Haisheng Tan, University of Science and Technology of China, China
TBA
Sponsorships TBA
Held online.
General information about submitting papers to BigCom2021, 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.
Please be sure your paper is formatted properly for submission. In particular, please carefully follow all of the following formatting requirements:
Given that our conference will be held online, we have adjusted our registration fee. Previous overpayment will be refunded.
Early Bird (Before Jul.15, 2021) | Late (After Jul.15, 2021) | Online | |
Full Registration | USD 650/RMB 4200 | USD 700/RMB 4500 | RMB 2200 for paper registration |
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Non-Student Registration | USD 650/RMB 4200 | USD 700/RMB 4500 | Full Refund |
Student Registration | USD 325/RMB 2100 | USD 350/RMB 2250 | Full Refund |
Prof. Wenguang ChenDepartment of Computer Science, Tsinghua University Email: cwg@tsinghua.edu.cn |
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Chukonu: A Fully-Featured Big Data Processing System with a C++ native computing coreAbstract: In this talk, we present a new native big data framework Chukonu with the feature-reusing approach. Chukonu supports full features of Spark core and has competitive performance to pure-native frameworks. We first extend the run-time only DAG of Spark to a hybrid DAG representation to enable the integration of native compute engine to Spark: the DAG of Chukonu is divided into compile-time parts and run-time parts, in which compile-time parts are natively optimized, and the run-time parts are delegated to the Spark core. Then we propose a few optimization techniques, such as operator fusion, vectorization, and data compaction to reduce the JNA calls overhead of Spark integration significantly. The evaluation shows that Chukonu has a speedup up to 71.58× (geometric mean 6.09×) over Apache Spark on six common big data workloads in an in-house cluster.Biography: Wenguang Chen is a professor in Department of Computer Science and Technology, Tsinghua University. His research interest is in parallel and distributed systems and programming systems. He received the Bachelor’s and Ph.D. degrees in computer science from Tsinghua University in 1995 and 2000 respectively. Before joining Tsinghua in 2003, he was the CTO of Opportunity International Inc. He was appointed as the associate head of Department of Computer Science and Technology from 2007 to 2014. He is a distinguished member and distinguished speaker of CCF( China Computer Foundation). He is an ACM member, Member at Large of ACM China Council. |
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Prof. John C.S. LuiDepartment of Computer Science & Engineering, The Chinese University of Hong KongEmail: cslui@cse.cuhk.edu.hk |
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Title: Sampling Large Networks: Algorithms and ApplicationsAbstract: Often times, large networks can be represented as graphs. For example, the Internet topology can be represented as an undirected graph while large logical networks (e.g., Facebook, Twitter,..etc) can be represented as either directed or undirected graphs. For these graphs, characterizing node pair relationships is important for applications such as friend recommendation and interest targeting in online social networks (OSNs). Due to the large scale nature of such networks, it is infeasible to enumerate all user pairs and so sampling is used. In this talk, we show that it is a great challenge even for OSN service providers to characterize user pair relationships when they possess the complete graph topology. The reason is that when sampling techniques (i.e., uniform vertex sampling and random walk) are naively applied, they can introduce large biases, in particular, for estimating similarity distribution of user pairs with constraints such as existence of mutual neighbors. Estimating statistics of user pairs is even more challenging in the absence of the complete topology information, since an unbiased sampling technique such as uniform vertex sampling is usually not allowed, and exploring the OSN graph topology is expensive. To address these challenges, we present asymptotically unbiased sampling methods to characterize user pair properties. We also show potential applications and discuss future research work.Biography: John C.S. Lui is currently the Choh-Ming Li Professor of the Computer Science & Engineering Department at The Chinese University of Hong Kong. His current research interests are in online learning and machine learning on network systems, quantum networks, network economics, network/system security, large scale distributed systems and performance evaluation theory. John received various departmental teaching awards and the CUHK Vice-Chancellor's Exemplary Teaching Award, as well as the CUHK Faculty of Engineering Research Excellence Award (2011-2012). He is a co-recipient of the IFIP WG 7.3 Performance 2005, IEEE/IFIP NOMS 2006 and SIMPLEX'14 Best Paper Awards, ACM RecSys’17 best paper award and best paper runner-up in ACM Mobihoc’18 and ASONAM’17. He is an elected member of the IFIP WG 7.3, Fellow of ACM, Fellow of IEEE, Senior Research Fellow of the Croucher Foundation, Fellow of the Hong Kong Academy Engineering and Sciences and HK RGC Senior Research Fellow. His personal interests include films and general reading. |
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Prof. Xiaoming FuFaculty of Mathematics and Computer Science, Georg-August-University of Goettingen Email: fu@cs.uni-goettingen.de |
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Title: Estimating Socioeconomic Status with Big DataAbstract: Socioeconomic status (SES) represents a person's social and economic rank in a society, especially education, occupation and income. Traditionally, estimating SES for a large population is performed by national statistical institutes through a large number of household interviews, which is highly expensive and time-consuming. In this talk I will describe our recent efforts on estimating a person's socioeconomic status with multiple sources of data.Biography: Xiaoming Fu received the Ph.D. degree in computer science from Tsinghua University, China, in 2000. Since 2007 he is a full professor and the Head of the Computer Networks Group, University of Göttingen. He has also held visiting positions at ETSI, University of Cambridge, Columbia University, UCLA, Sorbonne University, Tsinghua University, Nanjing University and Polytechnic University of Hong Kong. He is a Distinguished Lecturer of IEEE, a member of ACM, a fellow of IET, and a member of Academia Europaea. |