Eleventh Workshop on Data Mining in Earth System Science (DMESS 2023)

Co-conveners: Forrest M. Hoffman, Jitendra Kumar, Kaixu Bai, and Venkata Shashank “Shashi” Konduri

In conjunction with the IEEE International Conference on Data Mining (ICDM 2023)

Shanghai, China

Spanning many orders of magnitude in time and space scales, Earth science data, from point measurements to process-based Earth system model output, are increasingly large and complex, and often represent very long time series, making these data difficult to analyze, visualize, interpret, and understand. An “explosion” of heterogeneous, multi-disciplinary data–including observations and models of interacting natural, engineered, and human systems–have rendered traditional means of integration and analysis ineffective, necessitating the application of new analytical methods and the development of highly scalable software tools for synthesis, assimilation, comparison, and visualization. For complex, nonlinear feedbacks among chaotic processes, new methods and approaches for data mining and computational statistics are required for classification and change detection, model evaluation and benchmarking, uncertainty quantification, and incorporation of constraints from physics, chemistry, and biology into analysis. This workshop explores various data mining approaches and algorithms for understanding nonlinear dynamics of weather and climate systems and their interactions with biogeochemical cycles, impacts of natural system responses and climate extremes on engineered systems and interdependent infrastructure networks, and mitigation and adaptation strategies for natural hazards and infrastructure and ecosystem resilience. Encouraged are original research papers describing applications of statistical and data mining methods that support analysis and discovery in climate predictability, attributions, weather extremes, water resources management, risk analysis and hazards assessment, ecosystem sustainability, infrastructure resilience, and geo-engineering.

Rigorous review papers that either have the potential to expose data mining researchers to commonly used data-driven methods in the Earth sciences or discuss the applicability and caveats of such methods from a machine learning or statistical perspective, are also desired. Methods may include, but are not limited to cluster analysis, empirical orthogonal functions (EOFs), extreme value and rare events analysis, genetic algorithms, neural networks and deep learning methods, physics-constrained data analytics, automated data assimilation, and other machine learning techniques. Novel approaches that bring new ideas from nonlinear dynamics and information theory, network science and graphical methods, and the state-of-the-art in computational statistics and econometrics, into data mining and machine learning, are particularly encouraged.

Previous Workshops

Program

11th Workshop on Data Mining in Earth System Science (DMESS 2023)
at the IEEE International Conference on Data Mining (ICDM 2023)

Coming soon!

Committee

Workshop Co-conveners

  • Forrest M. Hoffman is a Distinguished Computational Earth System Scientist at Oak Ridge National Laboratory (ORNL) in Oak Ridge, Tennessee, USA. As a resident researcher in ORNL’s Climate Change Science Institute (CCSI) and a member of ORNL’s Computational Sciences & Engineering Division (CSED), Forrest develops and applies Earth system models (ESMs) to investigate the global carbon cycle and feedbacks between biogeochemical cycles and the climate system. He applies data mining methods using high performance computing to problems in landscape ecology, remote sensing, and large-scale climate data analytics. He founded the workshop series on Data Mining in Earth System Science (DMESS) in 2009 and has served as lead convener for all prior workshops. Forrest is also a Joint Faculty Professor in the University of Tennessee’s Department of Civil & Environmental Engineering in nearby Knoxville, Tennessee, USA.

  • Jitendra Kumar is a computational hydrologist at Oak Ridge National Laboratory and a Joint Assistant Professor at the University of Tennessee, Knoxville, Tennessee, USA. He conducts research at the intersection of high performance computing, environmental and Earth sciences, and systems analysis and data mining. His research entails data mining, large-scale global optimization, computational hydrology and hydrogeology, landscape ecology, remote sensing, and development of parallel algorithms for large-scale supercomputers.

  • Kaixu Bai is an Associate Professor in the School of Geographic Sciences at East China Normal University in Shanghai, China. He applies big data analytics for missing value imputation and data fusion to advance environmental monitoring and management. As an atmospheric scientist, he uses remote sensing data to study aerosols, air quality, climate, and meteorology, and he performs multisensor data fusion and machine learning to enhance data for modeling and analysis.

  • Venakta Shashank “Shashi” Konduri is a remote sensing scientist working for the National Ecological Observatory Network (NEON), which is operated by Battelle. He is interested in studying the structure, composition and environmental drivers of vegetation distribution over large spatiotemporal scales using remote sensing data and machine learning methods. In collaboration with scientists at reputed government agencies, such as NASA, Oak Ridge National Laboratory (ORNL) and the US Forest Service, he has developed and deployed spatiotemporal data mining methods for improved understanding of plant structure and composition and predictive modeling of vegetation productivity.

Program Committee

  • Kaixu Bai (East China Normal University, Shanghai, CHINA)
  • Udit Bhatia (IIT Gandhinagar, Gujarat, INDIA)
  • Bjørn-Gustaf J. Brooks (Living Carbon, San Francisco, California, USA)
  • Gustau Camps-Valls (Image Processing Laboratory, University of Valencia, Valencia, SPAIN)
  • Nathan Collier (Computational Earth Sciences Group, Computational Sciences & Engineering Division and Oak Ridge Climate Change Science Institute (CCSI), Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA)
  • Auroop R. Ganguly (Department of Civil and Environmental Engineering, Northeastern University, Boston, Massachusetts, USA)
  • William W. Hargrove (Eastern Forest Environmental Threat Assessment Center, USDA Forest Service, Asheville, North Carolina, USA)
  • Forrest M. Hoffman (Computational Earth Sciences Group, Computational Sciences & Engineering Division and Oak Ridge Climate Change Science Institute (CCSI), Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA)
  • Jian Huang (Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, Tennessee USA)
  • Soo Kyung Kim (PARC, a Xerox Company, Palo Alto, California, USA)
  • Venakta Shashank “Shashi” Konduri (National Ecological Observatory Network, Boulder, Colorado, USA)
  • Jitendra Kumar (Terrestrial Systems Modeling Group, Environmental Sciences Division and Oak Ridge Climate Change Science Institute (CCSI), Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA)
  • Vipin Kumar (Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota, USA)
  • Kuldeep Kurte (Computational Sciences and Engineering Division, Oak Ridge National Laboratory, USA)
  • Zachary L. Langford (Cyber & Applied Data Analytics Division, Oak Ridge National Laboratory, USA)
  • Miguel D. Mahecha (Remote Sensing Center for Earth System Research, University of Leipzig, Leipzig, GERMANY)
  • Jiafu Mao (Terrestrial Systems Modeling Group, Environmental Sciences Division and Oak Ridge Climate Change Science Institute (CCSI), Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA)
  • Murali Gopalakrishnan Meena (Oak Ridge Leadership Computing Facility,Oak Ridge National Laboratory, USA)
  • Richard T. Mills (Laboratory for Advanced Numerical Simulations, Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, Illinois, USA)
  • Steven P. Norman (Eastern Forest Environmental Threat Assessment Center, USDA Forest Service, Asheville, North Carolina, USA)
  • Bharat Sharma (Ecosystem Processes Group, Environmental Sciences Division and Oak Ridge Climate Change Science Institute (CCSI), Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA)
  • Sarat Sreepathi (Computer Science & Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA)
  • Vamsi Sripathi (Intel Corporation, Hillsboro, Oregon, USA)
  • Karsten Steinhaeuser (Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota, USA)
  • TJ Vandal (NASA Ames Research Center, Moffett Field, California, USA)
  • Min Xu (Computational Earth Sciences Group, Computational Sciences & Engineering Division and Oak Ridge Climate Change Science Institute (CCSI), Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA)
  • Cheng-En Yang (Department of Civil and Environmental Engineering, University of Tennessee, Knoxville, Tennessee, USA)
Submission

Authors are invited to submit manuscripts of 8 pages (10 pages maximum for additional fee) reporting unpublished, mature, and original research and recent developments/theoretical considerations in applications of data mining to Earth sciences by July 1, 2023. Paper submissions should be in the IEEE 2-column format, including the bibliography and any possible appendices.

Accepted papers will be published in the conference proceedings by the IEEE Computer Society Press. Submission implies the willingness of at least one of the authors to register and present the paper.

Manuscripts must be submitted electronically in the online submission system.


All submissions will be triple-blind reviewed by the Program Committee on the basis of technical quality, relevance to scope of the workshop, originality, significance, and clarity. The following sections give further information for authors.

Triple blind submission guidelines

Since 2011, ICDM has imposed a triple blind submission and review policy for all submissions. Authors must hence not use identifying information in the text of the paper and bibliographies must be referenced to preserve anonymity. Any papers available on the Web (including Arxiv) no longer qualify for ICDM submissions, as their author information is already public.

What is triple blind reviewing?

The traditional blind paper submission hides the referee names from the authors, and the double-blind paper submission also hides the author names from the referees. The triple-blind reviewing further hides the referee names among referees during paper discussions before their acceptance decisions. The names of authors and referees remain known only to the PC Co-chairs, and the author names are disclosed only after the ranking and acceptance of submissions are finalized. It is imperative that all authors of ICDM submissions conceal their identity and affiliation information in their paper submissions. It does not suffice to simply remove the author names and affiliations from the first page, but also in the content of each paper submission.

How to prepare your submissions

The authors shall omit their names from the submission. For formatting templates with author and institution information, simply replace all these information in the template by “Anonymous”.

In the submission, the authors’ should refer to their own prior work like the prior work of any other author, and include all relevant citations. This can be done either by referring to their prior work in the third person or referencing papers generically. For example, if your name is Smith and you have worked on clustering, instead of saying “We extend our earlier work on distance-based clustering (Smith 2005),” you might say “We extend Smith’s (Smith 2005) earlier work on distance-based clustering.” The authors shall exclude citations to their own work which is not fundamental to understanding the paper, including prior versions (e.g., technical reports, unpublished internal documents) of the submitted paper. Hence, do not write: “In our previous work [3]” as it reveals that citation 3 is written by the current authors. The authors shall remove mention of funding sources, personal acknowledgments, and other such auxiliary information that could be related to their identities. These can be reinstituted in the camera-ready copy once the paper is accepted for publication. The authors shall make statements on well-known or unique systems that identify an author, as vague in respect to identifying the authors as possible. The submitted files shall be named with care to ensure that author anonymity is not compromised by the file name. For example, do not name your submission “Smith.pdf”, instead give it a name that is descriptive of the title of your paper, such as “ANewApproachtoClustering.pdf” (or a shorter version of the same).

Algorithms and resources used in a paper should be described as completely as possible to allow reproducibility. This includes experimental methodology, empirical evaluations, and results. Authors are strongly encouraged to make their code and data publicly available whenever possible. In addition, authors are strongly encouraged to also report, whenever possible, results for their methods on publicly available datasets.

All manuscripts are submitted as full papers and are reviewed based on their scientific merit. There is no separate abstract submission step. There are no separate industrial, application, short paper or poster tracks during submission.

Dates

All deadlines are at 11:59PM Beijing Time.

  • Paper submission: September 1, 2023 Extended to September 15, 2023
  • Author notification: October 1, 2023
  • Camera-ready deadline and copyright forms: October 15, 2023
  • Registration deadline: October 15, 2023
  • DMESS 2023 Workshop: December 1, 2023
  • ICDM 2023 Conference: December 1–4, 2023
Contact

Email: dmess2023 at climatemodeling dot org