Towards a general theory of action and time
Artificial Intelligence
Moments and points in an interval-based temporal logic
Computational Intelligence
Temporal reasoning based on semi-intervals
Artificial Intelligence
A survey on temporal reasoning in artificial intelligence
AI Communications
Maintaining knowledge about temporal intervals
Communications of the ACM
Algorithms for time series knowledge mining
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Data & Knowledge Engineering
Marking time in sequence mining
AusDM '06 Proceedings of the fifth Australasian conference on Data mining and analystics - Volume 61
Efficient mining of understandable patterns from multivariate interval time series
Data Mining and Knowledge Discovery
Unsupervised pattern mining from symbolic temporal data
ACM SIGKDD Explorations Newsletter - Special issue on data mining for health informatics
Mining fuzzy temporal patterns from process instances with weighted temporal graphs
International Journal of Data Analysis Techniques and Strategies
Trustworthy knowledge diffusion model based on risk discovery on peer-to-peer networks
Expert Systems with Applications: An International Journal
Discovering richer temporal association rules from interval-based data
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
Ordering events for dynamic geospatial domains
COSIT'05 Proceedings of the 2005 international conference on Spatial Information Theory
Automatic Learning of Temporal Relations Under the Closed World Assumption
Fundamenta Informaticae - Cognitive Informatics and Computational Intelligence: Theory and Applications
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The temporal interval relationships formalized by Allen, and later extended to accommodate semi-intervals by Freksa, have been widely utilized in both data modeling and artificial intelligence research to facilitate reasoning between the relative temporal ordering of events. In practice, however, some modifications to the relationships are necessary when linear temporal sequences are provided, when event times are aggregated, or when data is supplied to a granularity which is larger than required. This paper discusses these modifications and outlines a solution to this problem which accommodates any available knowledge of interval midpoints.