About the Workshop
Modern applications span high-performance computing (HPC), cloud, edge, and Internet-of-Things (IoT) platforms. Across all of them, data volumes have grown to strain storage, I/O, communication, and energy budgets at every layer of the system. Data reduction has become indispensable in this regime: it can shrink data by one or even two orders of magnitude, with a large downstream payoff in reduced memory and storage pressure, fewer I/O bottlenecks, shorter communication time, and better energy efficiency in parallel and distributed environments.
The pressure is acute across the computing landscape. Exascale systems such as Frontier, Aurora, and El Capitan produce simulation outputs orders of magnitude larger than available storage, while light sources, telescopes, and particle detectors stream raw measurements faster than disks and networks can ingest them. AI/ML training corpora and foundation-model checkpoints now reach tens to hundreds of terabytes per run, and energy-constrained IoT and edge deployments must compress sensor streams in real time within strict power, memory, and bandwidth budgets. IWBDR-6 brings the data-reduction community together to share results, identify new directions, and build collaborations around three research pillars: reduction ratio at scale, the accuracy-performance trade-off, and information preservation.
Workshop Format
IWBDR-6 will be organized as a hybrid workshop, supporting both in-person attendance in Phoenix, Arizona and remote participation through a managed virtual meeting platform. It continues a well-established series: the 1st through 5th editions were held in conjunction with IEEE BigData 2020, 2021, 2022, 2023, and 2025, respectively.
Following IWBDR convention, the program features one distinguished keynote speaker drawn from national laboratories, leading research universities, and industry, alongside peer-reviewed paper presentations. Each submission receives at least three single-blind reviews from the Program Committee, and the workshop accepts at least six regular papers to fill a full session.
Important Dates
- Full paper submission deadline: October 30, 2026 (Friday)
- Author notification: November 13, 2026 (Friday)
- Camera-ready deadline: November 23, 2026
- IWBDR-6 workshop: December 14–17, 2026 (the specific date is to be announced)
Dates are tentative and will be finalized in alignment with the IEEE BigData 2026 workshop track.
Call for Papers
We invite original submissions on data reduction for big data across high-performance computing, cloud, edge, and IoT platforms, spanning algorithms, mathematics, system software, hardware co-design, and application-driven methods. IWBDR-6 targets IEEE BigData attendees facing data-volume bottlenecks across HPC simulations, AI/ML pipelines, cloud and edge analytics, and experimental facilities, where reduction-specific techniques such as error-bounded lossy compression, progressive reduction, and neural-hybrid encoders are rarely covered in depth.
Topics of Interest
The research topics covered by IWBDR-6 include, but are not limited to:
Data reduction techniques for big data issues in high-performance computing (HPC), cloud computing, Internet-of-Things (IoT), edge computing, machine learning and deep learning, and other big data areas:
- Lossy and lossless compression methods
- Approximate computation methods
- Compressive/compressed sensing methods
- Reduction methods for unstructured data
- Tensor decomposition methods
- Data deduplication methods
- Domain/motif-specific methods, such as (un)structured meshes, particles, tensors
- Accuracy-guarantee data reduction methods
- Optimal design of data reduction methods
- Progressive data reduction methods
- Methods to provide reduced models/representations
Additional topics of interest:
- Mathematical methods with provable error bounds on data and quantities of interest
- Metrics and infrastructures to evaluate reduction methods and assess quality/fidelity of reduced data
- Uncertainty quantification for reduction methods/models/representations
- Benchmark applications and datasets for big data reduction
- Data analysis and visualization techniques leveraging reduced data
- Characterizing the impact of data reduction techniques on applications
- Hardware-software co-design of data reduction
- Trade-offs between accuracy & performance on emerging computing hardware and platforms
- Resource-constrained and/or time-constrained data reduction methods
- Software, tools, and programming models for managing reduced data
- Runtime systems and supports for data reduction
- Development of composable data reduction pipelines/workflows
- Automation of data reduction in scientific workflows
- Data reduction challenges and solutions in observational and experimental environments
Submission
Full workshop papers are submitted through the IWBDR-6 paper submission system on EasyChair; the dedicated submission link will be posted here once finalized. Each paper receives at least three single-blind reviews from the Program Committee. At least one author of every accepted paper must register and present (in person or virtually).
Important Dates
- Full paper submission deadline: October 30, 2026 (Friday)
- Author notification: November 13, 2026 (Friday)
- Camera-ready deadline: November 23, 2026
- IWBDR-6 workshop: December 14–17, 2026
Dates are tentative and will be finalized in alignment with the IEEE BigData 2026 workshop track.
Program Chairs
- Jiannan Tian, Oakland University, United States
- Daoce Wang (Co-Chair), University of Nebraska Omaha, United States
Program Committee
The list is sorted alphabetically by last name.
- Allison Baker, National Center for Atmospheric Research, United States
- Martin Burtscher, Texas State University, United States
- Jon Calhoun, Clemson University, United States
- Franck Cappello, Argonne National Laboratory, United States
- Jieyang Chen, University of Oregon, United States
- Soumya Dutta, Los Alamos National Laboratory, United States
- Pascal Grosset, Los Alamos National Laboratory, United States
- Hanqi Guo, Ohio State University, United States
- Pu Jiao, The University of Texas Rio Grande Valley, United States
- Shaomeng Li, National Center for Atmospheric Research, United States
- Jesus Pulido, Los Alamos National Laboratory, United States
- Dingwen Tao, University of Chinese Academy of Sciences (UCAS), China
- Xiaodong Yu, Stevens Institute of Technology, United States
- Zhaorui Zhang, The Hong Kong Polytechnic University, Hong Kong, China
- Kai Zhao, Florida State University, United States
Steering Committee
- Sheng Di, Argonne National Laboratory, United States
- Xin Liang, Oregon State University, United States
Invited Keynote
It is an IWBDR convention to invite a distinguished keynote speaker at every edition, drawn from national laboratories, leading research universities, and industry. For IWBDR-6 we will invite one distinguished keynote speaker; the invitee is to be determined and will be confirmed ahead of the workshop. Check back closer to Dec. 14–17, 2026.
Past editions have featured keynote speakers including Dr. Bill Spotz (DOE/ASCR), Dr. Franck Cappello (Argonne National Laboratory), Dr. Feng Zhang (Renmin University), Dr. Benben Liu (LSCM), and Dr. Zhuoran Ji (Shandong University).
Past Events
IWBDR has been held annually in conjunction with the IEEE International Conference on Big Data. Each edition's website is linked below.
- IWBDR-1 (IEEE BigData
2020)
When: December 10, 2020 (Thursday) • Where: Virtual - IWBDR-2 (IEEE BigData
2021)
When: December 17, 2021 • Where: Virtual - IWBDR-3 (IEEE BigData
2022)
When: December 18, 2022 • Where: Osaka, Japan - IWBDR-4 (IEEE BigData
2023)
When: December 15–18, 2023 • Where: Sorrento, Italy - IWBDR-5 (IEEE
BigData 2025)
When: December 8, 2025 • Where: Macau SAR, China
Welcome to Phoenix
IWBDR-6 is co-located with IEEE BigData 2026 in Phoenix, Arizona: the fifth-largest city in the United States, set in the Salt River Valley (the "Valley of the Sun") amid the saguaro-studded Sonoran Desert. Beyond the conference, the region rewards a few extra days with desert landscapes, year-round sunshine, accessible hiking, and a growing arts and dining scene.
The downtown core, where most conference activity centers, is served by Valley Metro Rail, and Phoenix Sky Harbor International Airport sits just a few miles east of downtown.
Official tourism & city info
- Visit Phoenix ↗: the city's official destination marketing organization, with events, neighborhood guides, and hotel and transit info.
- City of Phoenix ↗: the official municipal site.
- Visit Arizona: Phoenix ↗: the state tourism board's Phoenix overview.
- Valley Metro ↗: light rail and buses across the metro area.
If you have an extra day
Desert & outdoors
- Desert Botanical Garden ↗: a celebrated collection of Sonoran Desert flora in Papago Park.
- Camelback Mountain ↗: the iconic, strenuous summit hike with panoramic valley views.
- South Mountain Park & Preserve ↗: one of the largest municipal parks in the country, with miles of desert trails.
Museums & culture
- Heard Museum ↗: a renowned collection of American Indian art and history.
- Phoenix Art Museum ↗: the largest art museum in the Southwest.
- Musical Instrument Museum (MIM) ↗: instruments and music from around the world.
Day trips & neighborhoods
Phoenix is associated with Sonoran hot dogs, Mexican and Southwestern cuisine, and a fast-growing craft food scene. For a longer excursion, Sedona ↗ (red-rock country, roughly two hours north) and the Grand Canyon ↗ (about three and a half hours) are classic Arizona destinations, while Old Town Scottsdale ↗ next door is an easy evening out.
Getting there
- Phoenix Sky Harbor International Airport (PHX) ↗: the primary gateway, just east of downtown and connected to Valley Metro Rail via the free PHX Sky Train.
- Valley Metro Rail ↗: light rail linking the airport, downtown, Tempe, and Mesa.
- Amtrak (Maricopa, AZ) ↗: the nearest Amtrak station, served by the Sunset Limited with Thruway bus connections to downtown Phoenix.
The links above point to official tourism, municipal, and operator sites. IWBDR-6 is not affiliated with these organizations; they are provided as a convenience for attendees.