In many application areas of machine learning and data mining, researchers face challenges entailed by temporal and spatial data.  This is the case of sales data analysis for targeted advertising, customer relationship management, fraud detection, intensive care monitoring, information filtering, user modelling, and learning from geographic or geo-referenced data.  As many of the related issues have already received due attention in the literature, time seems to be ripe for a one-day workshop to bring together a group of scientists specializing on this field.  The intention is to attempt to summarize the current status of the relevant work, and perhaps also identify strands for future research. 

The workshop will primarily focus on the following aspects: 

• Problems of representing time and space
• Generalization in temporal and spatial domains
• Induction and use of massive background knowledge
• Data mining and knowledge discovery in databases
• Scalability, complexity, and efficiency

The tasks of interest include but are not limited to: 

• Classification and prediction
• Data segmentation and clustering
• Forming higher-level categories (such as episodes)
• Detecting causal relationships
• Handling concept drift

SCHEDULE

ORGANIZING COMMITTEE