The 2nd International Workshop on Spatio-Temporal Data Mining from the Web

Dubai, United Arab Emirates, April 13-17, 2026

Held in conjunction with the 2026 ACM Web Conference

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About

WebST 2026 is a comprehensive half-day workshop (with at least 8 accepted papers, 3 keynote talks, and over 70 attendees) at WWW 2026, catering to professionals, researchers, and practitioners who are interested in sensing, mining, and understanding big and heterogeneous spatio-temporal data generated from the web (e.g., social media posts, geotagged images, mobility traces) to tackle real-world challenges, such as climate change, disaster response, urban planning, and location-based social networks. The workshop will not only offer a platform for knowledge exchange but also acknowledge outstanding contributions through a distinguished Best Paper Award. Furthermore, we envision the publication of accepted papers in a special issue, organized in reputable journals such as ACM Transactions on Big Data and ACM Transactions on Intelligent Systems and Technology. This workshop would be sponsored by JD Technology, with other committee members from MIT, CMU, UCB, etc. Note that this will be the fifth time that our core members have organized a similar workshop. The previous 14 workshops were hosted with SIGKDD, IJCAI, and WWW, each of which attracted over 70 participants and 30 submissions on average.





Committee

General Chair

Qingsong Wen, Squirrel Ai Learning, Bellevue, USA

Shirui Pan, Griffith University, Australia

Flora Salim, University of New South Wales, Australia

Program Chair

Yuxuan Liang, The Hong Kong University of Science and Technology (Guangzhou), China

Hao Xue, University of New South Wales, Australia

Ming Jin, Griffith University, Australia

Fei Wang, Institute of Computing Technology, Chinese Academy of Sciences, China

Publicity Chair

Qingxiang Liu, The Hong Kong University of Science and Technology (Guangzhou), China

Call for papers

Aims and Scope

Our objective is to provide a platform for researchers, practitioners, and stakeholders from diverse fields, such as data mining (DM) and machine learning (ML), to explore the unique challenges and opportunities posed by web-sourced ST data. As the digital landscape continues to evolve, vast amounts of ST data are generated daily from diverse online sources, including social media, mobile and WoT, IoT sensors, and open data platforms. This workshop aims to address the growing need for innovative methods and practical tools by serving as the platform to present cutting-edge research for mining spatio-temporal data, discuss the challenges and ethical considerations, and explore future real-world applications.

In terms of the scope, the workshop will focus on these key areas:

  • Adapt ML/DM techniques to web-sourced ST data: Unlike image and text data, web-sourced ST data exhibits unique characteristics, including spatial distance, spatial hierarchy, temporal smoothness, periodicity, and trend. How to adapt existing ML/DM algorithms to process ST properties remains a challenge.

  • Cross-domain data fusion in the web: Data sourced from the web originates from diverse domains, such as social media, transportation systems, and mobile networks, and presents in varied modalities, including ST, textual, and visual forms. This focus area will address challenges in harmonizing heterogeneous ST data formats/sources to support decision-making.

  • Interactive visual data analytics for web-sourced ST data: While traditional data visualization emphasizes presenting static information, interactive visual data analytics from web sources introduces a dynamic approach, enabling real-time engagement with diverse data streams that vary across both time and space. This method not only integrates visualization with advanced DM/ML algorithms but also leverages cloud computing platforms to handle the scale and complexity of web-sourced ST data. By merging human and machine intelligence, interactive analytics empowers domain experts, such as urban planners, to work jointly with data scientists, combining specialized knowledge with ST insights from the web to address real-world challenges.

  • Web-based applications: Integrating ST data mining into web-based applications offers substantial improvements in decision-making and operational efficiency across various sectors. By analyzing spatial and temporal patterns, these applications can deliver critical insights that support strategic planning, informed decision-making, and optimal resource allocation. For instance, in urban planning, web-based platforms that track traffic and population movement data enable more responsive infrastructure development and efficient traffic management.

Topics of Interest

Our workshop will provide a venue for discussions on these topics:

  • Adaptation of cutting-edge machine learning algorithms for modeling ST data from web sources.

  • Techniques for ST data generation, forecasting, causal inference, anomaly detection, and control in web contexts.

  • Advanced learning frameworks (e.g., self-supervised learning, transfer learning, adversarial learning) for web-sourced ST data.

  • Cross-domain and multi-modal data fusion, integrating spatiotemporal, visual, and textual information sourced from the web.

  • Real-world applications of ST data mining in smart cities, transportation, and public health, leveraging web-derived insights.

  • Developing foundational models or utilizing LLMs for processing and analyzing ST data extracted from online platforms.

These topics are highly relevant to The Web Conference as they address the intersection of web technologies and spatio-temporal data analytics. By focusing on ML/DM adaptations for web-sourced data, real-time analysis techniques, and cross-domain data integration, the workshop emphasizes practical applications in smart cities and public health, showcasing how web-derived insights can drive informed decision-making and innovation in various sectors.

Important Dates

Important Dates
Workshop paper submission: TBD
Workshop paper notification: TBD
Workshop paper camera - ready: TBD
Workshops: TBD
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