IEEE ICDM 17th International Workshop on
Spatial and Spatiotemporal Data Mining (SSTDM)

November 28, 2022,
Orlando, Florida, USA.

What this workshop is about?

With advances in remote sensors, sensor networks, and the proliferation of location-sensing devices in daily life activities and common business practices, the generation of disparate, dynamic, and geographically distributed spatiotemporal data has exploded in recent years. In addition, significant progress in ground, air- and space-borne sensor technologies has led to unprecedented access to earth science data, including polar data, for scientists from different disciplines, interested in studying the complementary nature of different parameters. These developments are quickly leading toward a data-rich but information-poor environment. The rate at which geospatial data are being generated clearly exceeds our ability to organize and analyze them to extract patterns critical for understanding in a timely manner a dynamically changing world. Access to such data can help address important challenges such as climate change, sea-level rise, and their impact on communities through transformative spatiotemporal data science and machine learning. This workshop focuses on advances at the intersection of Geospatial AI, Machine Learning, and Spatiotemporal Computing in order to address these scientific and computational challenges and provide innovative and effective solutions.
More specifically, efficient, reliable, and explainable AI, Machine Learning, and Data Mining techniques are needed for extracting useful geoinformation from large heterogeneous, often multi-modal spatiotemporal datasets (e.g., remote sensing, GIS, trajectory, geo-social media). Traditional techniques are ineffective as they do not incorporate the idiosyncrasies of the spatial domain, which include (but are not limited to) spatial autocorrelation, spatial context, and spatial constraints. Extracting useful geoinformation and actionable knowledge from several terabytes of streaming multi-modal data per day also demands the use of modern computing in all its forms (clusters to the cloud). Thus, we invite all researchers and practitioners to participate in this event and share, contribute, and discuss the emerging challenges in Geo-spatial-temporal AI, Machine Learning, and Data Mining.

Proceedings

Steering Committee

• Shashi Shekar, University of Minnesota, USA
• Anthony Stefanidis, George Mason University, USA
• Ranga Raju Vatsavai, North Carolina State University, USA

General Chairs

• Shashi Shekar, University of Minnesota, USA
• Vandana Janeja, University of Maryland Baltimore County, USA

Program Chairs

• Ranga Raju Vatsavai, North Carolina State University, USA

Publicity Chairs

• Krishna Karthik Gadiraju, Juniper Networks, USA

Program Committee

• Mandar Chaudhary, Ebay Inc, USA
• Surya Durbha, CSRE, IIT, Bombay, India
• Zhe Jiang, University of Florida, USA
• Dev Oliver, Environmental Systems Research Institute, USA
• Krishna Karthik Gadiraju, Juniper Networks, USA
• Ranga Raju Vatsavai, North Carolina State University, USA
• Xun Zhou, University of Iowa, USA
• Selim Aksoy, Bilkent University, Turkey
• Annalisa Appice, University of Bari, Italy
• Maurizio Atzori, University of Cagliari, Italy
• Berkay Aydin, Georgia State University, USA
• Vania Bogorny, Universidade Federal deSanta Catarina, Brasil
• Shyam Boriah, University of Minnesota, USA
• Anil Cheriyadat, Oak Ridge National Laboratory, USA
• Arie Croitoru, George Mason University, USA
• Alfredo Cuzzocrea, Univ. Trieste, Italy
• Debasish Das, North Eastern University, USA
• Ke Deng, RMIT University, Australia
• Wei Ding, University of Houston-Clear Lake, USA
• Anthony Filippi, Texas A&M University, USA
• Dimitris Gunopulos, University of Athens, USA
• Auroop Ganguly, North Eastern University, USA
• Diansheng Guo, University of South Carolina, USA
• Forrest Hoffman, Oak Ridge National Laboratory, USA
• Vandana Janeja, University of Maryland Baltimore County, USA
• Goo Jun, The University of Texas Health Science Center at Houston, USA
• Shih-Chieh Kao, Oak Ridge National Laboratory, USA
• Ki-Joune Li, Pusan National University, Korea
• Aurelie Lozano, IBM-Research, USA
• Giuseppe Manco, ICAR-CNR, Italy
• Alessandra Raffaeta, Ca' Foscari University of Venice, Italy
• Falco Schmid, University of Bremen, Germany
• Cyrus Shahabi, University of Southern California, USA
• Xuan Shi, University of Arkansas, USA
• Alexandre Sorokine, Oak Ridge National Laboratory, USA
• Robert Stewart, Oak Ridge National Laboratory, USA
• Pang-Ning Tan, Michigan State, USA
• Michail Vlachos, IBM Research, USA
• Monica Wachowicz, University New Brunswick, Canada
• Jianting Zhang, IAIS, The City University of New York, USA
• Mallikarjun Shankar, Oak Ridge National Laboratory, USA
• Bharathkumar Ramachandra, Geopipe Inc, USA
• Zexi Chen, Tiktok, USA
• Ashwin Shashidharan, ESRI, USA
• Seokyong Hong, CJ Logistics, South Korea
• Arpan Man Sainju, Middle Tennessee State University, USA
• Azim Esmaiel Abadi, Georgia State University, USA
• Benjamin Dutton, North Carolina State University, USA
• Yanjie Fu, University of Central Florida, USA
• Yanhua Li, Worchester Polytechnic Institute, USA
• Sagy Cohen, University of Alabama, USA