1. Spatio-temporal Rule Mining: Issues and Techniques
Győző Gidófalvi
Geomatic ApS
Center for Geoinformatik
and
Torben Bach Pedersen
Aalborg University
DaWaK 2005

2. Outline Why mine spatio-temporal data?
Frequent pattern mining background
Frequent itemset mining / association rules
Sequential patterns
Taxonomy of spatio-temporal data
Some examples: STM, HUR, INFATI, DMI
How to mine spatio-temporal rules?
Pivoting -> spatio-temporal baskets
Some examples: INFATI, HUR, DMI
Mining long, common patterns in trajectories of moving objects
Conclusions and future directions
DaWaK 2005

3. Why Mine Spatio-temporal Data?
Spatio-temporal data is being collected at enormous speeds (Tbyte/hour)
Remote sensors on satellites
Telescope scanning the skies
Location data received from mobile devices
Data needs to be analysed for various purposes
Cataloging, classification, segmentation
Scientific hypothesis formulation
Study complex systems with autonomous mobile entities
Aid the management, storage, and retrieval of spatio-temporal data
Hidden information in data can be used to provide customized Location-Based Services (LBS) and Location-Based Advertising (LBA)
DaWaK 2005

4. Frequent Pattern Mining
First proposed by Agrawal and Sirkant for the analysis of customer purchase behaviour
Frequent itemsets: Discover items are bought together by customers frequently?
{bread, peanut butter, jelly}
Association rules: Discover a possible causal relationship between the items in such a frequent itemset?
{bread, peanut butter} -> {jelly} (within trans.)
Sequential patterns: Discover sequences of items or itemsets that are frequent in sequences of transactions?
{Star Wars Episode I} -> {Episode II} -> {Episode III} (between transactions)
Episodes: Discover periodic patterns in a long sequence?
Patterns in other structures: trees, graphs,…
How do we extend these to the spatio-temporal domain?
EX: {Strøget,noon,businessman} -> {cafe}
DaWaK 2005

5. Frequent Pattern Mining Cont…
Approaches to frequent itemset mining and association rules:
Apriori: bottom-up, generate-and-test frequent itemsets
BFS traversal of search space
Pruning using support monotonicity of itemsets
Projection-based (FP-growth): generate frequent itemset prefixes and extend the prefix by mining the prefix- projected database
DFS traversal of search space
Many other variants employing sophisticates in-memory data structures and representations of the data.
Restrictions on frequent itemsets
Closed frequent itemsets
Maximal frequent itemsets
DaWaK 2005

6. Taxonomy of Spatio-temporal Data
Examples of spatio-temporal data:
Space Time Man (STM): activities performed by mobile users at particular times and locations
HUR1: number of passengers getting on/off busses at particular times and locations
HUR2: Personal chip cards recording travels of individuals
DMI: periodic atmospheric measurements like temperature, humidity, and pressure for 5 km grid cells
INFATI: day–to–day movements of 20 private cars on the road network of Aalborg
Criteria for categorization of spatio-temporal data:
Are the measured entities mobile or immobile?
Are the attribute values of the measured entities static or dynamic?
DaWaK 2005

7. How to Mine Spatio-temporal Rules?
Knowledge extractable by association rules is about dependencies between items within baskets. -> Need to construct spatio-temporal baskets.
Pivoting is the process of grouping a set of records based on one or more attributes (pivoting attributes) and assigning the values of an another attribute (pivoted attribute) to groups or baskets.
Spatio-temporal rules that can be mined from spatio-temporal baskets can be either implicit or explicit.
DaWaK 2005

8. Illustration of Pivoting
INFATI pivoting example: pivoting attributes are “Location” and “Time”, pivoted attribute is “CarID”
DaWaK 2005

9. Spatio-temporally Restricted vs. Unrestricted
DaWaK 2005

10. Explicit Spatio-temporal Rule Mining 1
DaWaK 2005

11. Explicit Spatio-temporal Rule Mining 2
DaWaK 2005

12. DMI: Dynamic Attributes of Immobile Entities
DaWaK 2005

13. Mining Long, Common Patterns (LCP) in Trajectories of Moving Objects
Trajectories of moving objects contain regularities or patterns
These patterns can be used in indexing, tracking, and LBS
LBS example: intelligent rideshare application
Find common routes for a set of commuters and suggest rideshare possibilities to them
Unique requirements:
Patterns should rather be long than frequent
Patterns should be shareable, i.e.: common
Unique challenges:
Patterns are extremely long
Interesting patterns have relatively low support
Not all sub-patterns are interesting
DaWaK 2005

14. Method to Mine LCP in Trajectories
Pre-processing:
Identify trips, i.e.: gaps
Map date-time domain to time-of-day domain
Substitute noisy GPS measurements with spatio-temporal regions
Use / exploit unique requirements:
Prune search space if extractable patterns are doomed to be short
Define unique support measure: n-support – # of transactions satisfying an itemset if the number of distinct objects associated with those transactions >= n, 0 otherwise
It can be shown that interesting patterns are closed frequent itemsets
In current work, a projection-based FIM algorithm is being extended to meet and use and meet these requirements
Illustrative example:
DaWaK 2005

15. Pre-processed Example Trajectory Database
DaWaK 2005

16. Extracted Long Common Patterns
DaWaK 2005

17. Conclusions and Future Directions
Today:
Taxonomy of spatio-temporal data
Pivoting to obtain spatio-temporal baskets
Mining explicit and implicit spatio-temporal rules
Spatio-temporally restricted vs. unrestricted mining
Mining long, common patterns in trajectories
Tomorrow:
Incorporate spatio-temporal indexes in spatio-temporal rule mining or vice versa
Incorporate various spatio-temporal space partitioning methods into mining
DaWaK 2005

18. Thank you for your attention! Questions?
DaWaK 2005