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SSTDM 2021 : 16th International Workshop on Spatial and Spatiotemporal Data Mining (SSTDM-21)

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Link: https://research.csc.ncsu.edu/stac/conferences/ICDM-SSTDM21/
 
When Dec 7, 2021 - Dec 7, 2021
Where Auckland, New Zealand
Submission Deadline Aug 30, 2021
Notification Due Sep 24, 2021
Final Version Due Oct 1, 2021
 

Call For Papers

16th International Workshop on Spatial and Spatiotemporal Data Mining (SSTDM-21)
In Cooperation with IEEE ICDM 2021, December 2021, Auckland, New Zealand
http://research.csc.ncsu.edu/stac/conferences/ICDM-SSTDM21/

Call For Papers

Important Deadlines

Paper Submission

August 30, 2021

Acceptance Notice

September 24, 2021

Camera Ready

October 01, 2021

Workshop Date

December 07, 2021


Note: SSTDM follows ICDM conference guidelines regarding COVID-19. Please keep monitoring the ICDM-21 main page for future notifications regarding the same.


Synopsis: 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 an unprecedented access to earth science data for scientists from different disciplines, interested in studying the complementary nature of different parameters. These developments are quickly leading towards 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. Computer science and geoinformatics are collaborating in order to address these scientific and computational challenges and provide innovative and effective solutions.



More specifically, efficient and reliable data mining techniques are needed for extracting useful geoinformation from large heterogeneous, often multi-modal spatiotemporal datasets. Traditional data mining 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 from several terabytes of streaming multi-modal data per day also demands the use of modern computing in all its forms. Thus, we invite all researchers and practitioners to participate in this event and share, contribute, and discuss the emerging challenges in spatial and spatiotemporal data mining.



Topics: The major topics of interest to the workshop include but are not limited to:



Theoretical foundations of spatial and spatiotemporal data mining
Spatial and spatiotemporal analogues of interesting patterns: frequent itemsets, clusters, outliers, and the algorithms to mine them
Deep learning methods for spatial and temporal data
Methods that explicitly model spatial and temporal context
Spatial and spatiotemporal autocorrelation and heterogeneity, its quantification and
efficient incorporation into the data mining algorithms
Image (multispectral, hyperspectral, aerial, radar) information mining, change detection
Role of uncertainty in spatial and spatiotemporal data mining
Integrated approaches to multi-source and multimodal data mining
Resource-aware techniques to mine streaming spatiotemporal data
Spatial and spatiotemporal data mining at multiple granularities (space and time)
Data structures and indexing methods for spatiotemporal data mining
Spatial and Spatiotemporal online analytical processing, data warehousing
Geospatial Intelligence
Climate Change, Natural Hazards, Critical Infrastructures
High-performance SSTDM
Spatio-temporal data mining at the edge
Novel applications that demonstrate success stories of spatial and spatiotemporal data mining
Spatio-temporal data mining for epidemiology and health
Spatio-temporal data mining for social good
Spatio-temporal benchmark datasets


Proceedings: Accepted papers will be included in a ICDM Workshop Proceedings volume, to be published by IEEE Computer Society Press, which will also be included in the IEEE Digital Library.



Paper Submission: This is an open call-for-papers. We invite both full papers (max 8 pages) describing mature work and short papers (max 6 pages) describing work-in-progress or case studies. Only original and high-quality papers conforming to the ICDM 2021 standard guidelines will be considered for this workshop. Detailed submission instructions are available at the SSTDM-21 (http://research.csc.ncsu.edu/stac/conferences/ICDM-SSTDM21/) website.

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