Fast discovery of association rules
Advances in knowledge discovery and data mining
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Deviation and Association Patterns for Subgroup Mining in Temporal, Spatial, and Textual Data Bases
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Discovery of Spatial Association Rules in Geographic Information Databases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
STING+: An Approach to Active Spatial Data Mining
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Information Systems - Databases: Creation, management and utilization
Mining, indexing, and querying historical spatiotemporal data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Temporal moving pattern mining for location-based service
Journal of Systems and Software
Efficient mining of sequential patterns with time constraints by delimited pattern growth
Knowledge and Information Systems
Mining Frequent Spatio-Temporal Sequential Patterns
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Discovering Frequent Arrangements of Temporal Intervals
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Discovery of Periodic Patterns in Spatiotemporal Sequences
IEEE Transactions on Knowledge and Data Engineering
A model for enriching trajectories with semantic geographical information
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
Spatio-temporal discretization for sequential pattern mining
Proceedings of the 2nd international conference on Ubiquitous information management and communication
Efficient sequential pattern mining algorithms
AIKED'05 Proceedings of the 4th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering Data Bases
Dynamic modeling of trajectory patterns using data mining and reverse engineering
ER '07 Tutorials, posters, panels and industrial contributions at the 26th international conference on Conceptual modeling - Volume 83
Mining frequent trajectory patterns in spatial-temporal databases
Information Sciences: an International Journal
A method for predicting future location of mobile user for location-based services system
Computers and Industrial Engineering
Mining frequent arrangements of temporal intervals
Knowledge and Information Systems
Mining frequent closed patterns in pointset databases
Information Systems
Traffic density-based discovery of hot routes in road networks
SSTD'07 Proceedings of the 10th international conference on Advances in spatial and temporal databases
Effective spatio-temporal analysis of remote sensing data
APWeb'08 Proceedings of the 10th Asia-Pacific web conference on Progress in WWW research and development
DASFAA'06 Proceedings of the 11th international conference on Database Systems for Advanced Applications
Motion-Alert: automatic anomaly detection in massive moving objects
ISI'06 Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics
Discovery of spatiotemporal patterns in mobile environment
APWeb'06 Proceedings of the 8th Asia-Pacific Web conference on Frontiers of WWW Research and Development
STMPE: an efficient movement pattern extraction algorithm for spatio-temporal data mining
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part II
Spatio–temporal rule mining: issues and techniques
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
Distance measure for querying sequences of temporal intervals
Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive Environments
Mining generalized spatio-temporal patterns
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
Deductive and inductive reasoning on spatio-temporal data
INAP'04/WLP'04 Proceedings of the 15th international conference on Applications of Declarative Programming and Knowledge Management, and 18th international conference on Workshop on Logic Programming
On the spatiotemporal burstiness of terms
Proceedings of the VLDB Endowment
The pattern next door: towards spatio-sequential pattern discovery
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Mining trajectory patterns using hidden Markov models
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
International Journal of Intelligent Information and Database Systems
Hi-index | 0.01 |
The problem of mining spatiotemporal patterns is finding sequences of events that occur frequently in spatiotemporal datasets. Spatiotemporal datasets store the evolution of objects over time. Examples include sequences of sensor images of a geographical region, data that describes the location and movement of individual objects over time, or data that describes the evolution of natural phenomena, such as forest coverage. The discovered patterns are sequences of events that occur most frequently. In this paper, we present DFS_MINE, a new algorithm for fast mining of frequent spatiotemporal patterns in environmental data. DFS_MINE, as its name suggests, uses a Depth-First-Search-like approach to the problem which allows very fast discoveries of long sequential patterns. DFS_MINE performs database scans to discover frequent sequences rather than relying on information stored in main memory, which has the advantage that the amount of space required is minimal. Previous approaches utilize a Breadth-First-Search-like approach and are not efficient for discovering long frequent sequences. Moreover, they require storing in main memory all occurrences of each sequence in the database and, as a result, the amount of space needed is rather large. Experiments show that the I/O cost of the database scans is offset by the efficiency of the DFS-like approach that ensures fast discovery of long frequent patterns. DFS_MINE is also ideal for mining frequent spatiotemporal sequences with various spatial granularities. Spatial granularity refers to how fine or how general our view of the space we are examining is.