Spatio-temporal Rule Mining: Issues and Techniques Győző Gidófalvi Geomatic ApS Center for Geoinformatik and Torben Bach Pedersen Aalborg University 13-11-2018 DaWaK 2005

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 13-11-2018 DaWaK 2005

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) 13-11-2018 DaWaK 2005

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} 13-11-2018 DaWaK 2005

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 13-11-2018 DaWaK 2005

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? 13-11-2018 DaWaK 2005

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. 13-11-2018 DaWaK 2005

Illustration of Pivoting INFATI pivoting example: pivoting attributes are “Location” and “Time”, pivoted attribute is “CarID” 13-11-2018 DaWaK 2005

Spatio-temporally Restricted vs. Unrestricted 13-11-2018 DaWaK 2005

Explicit Spatio-temporal Rule Mining 1 13-11-2018 DaWaK 2005

Explicit Spatio-temporal Rule Mining 2 13-11-2018 DaWaK 2005

DMI: Dynamic Attributes of Immobile Entities 13-11-2018 DaWaK 2005

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 13-11-2018 DaWaK 2005

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: 13-11-2018 DaWaK 2005

Pre-processed Example Trajectory Database 13-11-2018 DaWaK 2005

Extracted Long Common Patterns 13-11-2018 DaWaK 2005

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 13-11-2018 DaWaK 2005

Thank you for your attention! Questions? 13-11-2018 DaWaK 2005