Discovering patterns in sequences of events
Artificial Intelligence
Artificial Intelligence - Special issue on knowledge representation
Theoretical Computer Science
Research perspectives for time series management systems
ACM SIGMOD Record
Combinatorial pattern discovery for scientific data: some preliminary results
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Logical design for temporal databases with multiple granularities
ACM Transactions on Database Systems (TODS)
The Design and Analysis of Computer Algorithms
The Design and Analysis of Computer Algorithms
A general framework for time granularity and its application to temporal reasoning
Annals of Mathematics and Artificial Intelligence
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
Implementing Calendars and Temporal Rules in Next Generation Databases
Proceedings of the Tenth International Conference on Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Identifying and Using Patterns in Sequential Data
ALT '93 Proceedings of the 4th International Workshop on Algorithmic Learning Theory
An approach to discovering temporal association rules
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
Similarity based retrieval from sequence databases using automata as queries
Proceedings of the eleventh international conference on Information and knowledge management
A general framework for time granularity and its application to temporal reasoning
Annals of Mathematics and Artificial Intelligence
Solving multi-granularity temporal constraint networks
Artificial Intelligence
A Survey of Temporal Knowledge Discovery Paradigms and Methods
IEEE Transactions on Knowledge and Data Engineering
Exploiting Data Mining Techniques for Broadcasting Data in Mobile Computing Environments
IEEE Transactions on Knowledge and Data Engineering
Discovering calendar-based temporal association rules
Data & Knowledge Engineering - Special issue: Temporal representation and reasoning
Recognizing and Discovering Complex Events in Sequences
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
Discovering Sequential Association Rules with Constraints and Time Lags in Multiple Sequences
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
MAPS: A Method for Identifying and Predicting Aberrant Behavior in Time Series
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
Deriving Abstract Views of Multi-granularity Temporal Constraint Networks
DEXA '02 Proceedings of the 13th International Conference on Database and Expert Systems Applications
Efficient Feature Mining in Music Objects
DEXA '01 Proceedings of the 12th International Conference on Database and Expert Systems Applications
Mining Asynchronous Periodic Patterns in Time Series Data
IEEE Transactions on Knowledge and Data Engineering
Sequential Association Rule Mining with Time Lags
Journal of Intelligent Information Systems
Incremental, Online, and Merge Mining of Partial Periodic Patterns in Time-Series Databases
IEEE Transactions on Knowledge and Data Engineering
Discovery of temporal patterns from process instances
Computers in Industry - Special issue: Process/workflow mining
Mining Surprising Periodic Patterns
Data Mining and Knowledge Discovery
Temporalized logics and automata for time granularity
Theory and Practice of Logic Programming
Periodicity Detection in Time Series Databases
IEEE Transactions on Knowledge and Data Engineering
Discovering Frequent Episodes and Learning Hidden Markov Models: A Formal Connection
IEEE Transactions on Knowledge and Data Engineering
Mining for weak periodic signals in time series databases
Intelligent Data Analysis
Rule discovery for event histories
Intelligent Data Analysis
Unsupervised pattern mining from symbolic temporal data
ACM SIGKDD Explorations Newsletter - Special issue on data mining for health informatics
Mining sequential patterns across time sequences
New Generation Computing
Mining Frequent Diamond Episodes from Event Sequences
MDAI '07 Proceedings of the 4th international conference on Modeling Decisions for Artificial Intelligence
Mining periodic patterns in spatio-temporal sequences at different time granularities
Intelligent Data Analysis
Keeping the resident in the loop: adapting the smart home to the user
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Mining Temporal Patterns for Humanoid Robot Using Pattern Growth Method
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Looking into the seeds of time: Discovering temporal patterns in large transaction sets
Information Sciences: an International Journal
Analysis on repeat-buying patterns
Knowledge-Based Systems
A review on time series data mining
Engineering Applications of Artificial Intelligence
Mining sectorial episodes from event sequences
DS'06 Proceedings of the 9th international conference on Discovery Science
Processing count queries over event streams at multiple time granularities
Information Sciences: an International Journal
Querying temporal clinical databases on granular trends
Journal of Biomedical Informatics
An adaptive sensor mining framework for pervasive computing applications
Sensor-KDD'08 Proceedings of the Second international conference on Knowledge Discovery from Sensor Data
A tree structure for event-based sequence mining
Knowledge-Based Systems
Closeness Preference - A new interestingness measure for sequential rules mining
Knowledge-Based Systems
A prediction framework based on contextual data to support Mobile Personalized Marketing
Decision Support Systems
Hi-index | 0.00 |
An important usage of time sequences is to discover temporal patterns. The discovery process usually starts with a user-specified skeleton, called an event structure, which consists of a number of variables representing events and temporal constraints among these variables; the goal of the discovery is to find temporal patterns, i.e., instantiations of the variables in the structure that appear frequently in the time sequence. This paper introduces event structures that have temporal constraints with multiple granularities, defines the pattern-discovery problem with these structures, and studies effective algorithms to solve it. The basic components of the algorithms include timed automata with granularities (TAGs) and a number of heuristics. The TAGs are for testing whether a specific temporal pattern, called a candidate complex event type, appears frequently in a time sequence. Since there are often a huge number of candidate event types for a usual event structure, heuristics are presented aiming at reducing the number of candidate event types and reducing the time spent by the TAGs testing whether a candidate type does appear frequently in the sequence. These heuristics exploit the information provided by explicit and implicit temporal constraints with granularity in the given event structure. The paper also gives the results of an experiment to show the effectiveness of the heuristics on a real data set.