Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
DOMINO: databases fOr MovINg Objects tracking
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Aggregation and comparison of trajectories
Proceedings of the 10th ACM international symposium on Advances in geographic information systems
Modeling Moving Objects over Multiple Granularities
Annals of Mathematics and Artificial Intelligence
Mining Sequential Patterns with Regular Expression Constraints
IEEE Transactions on Knowledge and Data Engineering
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
SPIRIT: Sequential Pattern Mining with Regular Expression Constraints
VLDB '99 Proceedings of the 25th 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
Moving Objects Databases: Issues and Solutions
SSDBM '98 Proceedings of the 10th International Conference on Scientific and Statistical Database Management
A Spatiotemporal Model and Language for Moving Objects on Road Networks
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Querying Multidimensional Databases
DBLP-6 Proceedings of the 6th International Workshop on Database Programming Languages
Modeling, Storing, and Mining Moving Object Databases
IDEAS '04 Proceedings of the International Database Engineering and Applications Symposium
Temporal management of RFID data
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Geoinformatica
Trajectory clustering: a partition-and-group framework
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Similarity Search in Trajectory Databases
TIME '07 Proceedings of the 14th International Symposium on Temporal Representation and Reasoning
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Adaptive fastest path computation on a road network: a traffic mining approach
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
A model for enriching trajectories with semantic geographical information
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
A conceptual view on trajectories
Data & Knowledge Engineering
Aggregation languages for moving object and places of interest
Proceedings of the 2008 ACM symposium on Applied computing
Efficient constraint evaluation in categorical sequential pattern mining for trajectory databases
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
A Data Model for Moving Objects Supporting Aggregation
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
Building real-world trajectory warehouses
Proceedings of the Seventh ACM International Workshop on Data Engineering for Wireless and Mobile Access
A hybrid model and computing platform for spatio-semantic trajectories
ESWC'10 Proceedings of the 7th international conference on The Semantic Web: research and Applications - Volume Part I
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A typical problem in the field of moving object MO databases consists in discovering interesting trajectory patterns. To solve this problem, data mining techniques are commonly used. Due to the huge volume of these trajectory data, some form of compression facilitates the data processing. One of such compression techniques is based on the notion of stops and moves. In this approach, a set of places that are relevant to the application, denoted Places of Interest POIs is selected. If a moving object spends a pre-defined amount of time in a place of interest, this place is considered a stop for the object's trajectory. Thus, raw trajectories given by O_{id}, t, x, y-tuples can be replaced by a sequence of application-relevant stops. This leads to the concept of semantic trajectory, in short, a trajectory obtained by replacing raw trajectory data with a sequence of stops, and enriched with metadata of the POIs corresponding to such stops. We present a language based on regular expressions over constraints, denoted RE-SPaM, that can intensionally express sequential patterns. The constraints in RE-SPaM are defined as conjunctions of equalities over metadata of the POIs. In addition, we introduce a data mining algorithm, based on sequential pattern mining techniques, where uninteresting sequences are pruned in advance making use of the automaton that accepts a RE-SPaM expression. This makes the task of the analyst easier, and the mining algorithm more efficient. We also show that RE-SPaM can be extended to support spatial functions, thus integrating spatial data in a moving object setting proposals so far only account for the MO trajectories themselves. We denote the resulting language RE-SPaM^{+S}. We show that the overhead of this extension is negligible, due to caching techniques that we explain in the paper. We close the paper with a case study over which we perform experiments to study the main variables that impact the performance of the mining algorithm.