Abstract
Several Systems have been designed to solve the task of abstraction of time-stamped raw data into domain-specific meaningful concepts and patterns. All approaches had to some degree severe limitations in their treatment of incompleteness and uncertainty that typically underlie the raw data, on which the temporal reasoning is performed, and have generally narrowed their interest to a single subject. We have designed a new probability-oriented methodology to overcome these conceptual limitations. The new method includes also a practical parallel computational model that is geared specifically for implementing our probabilistic approach.
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Ramati, M., Shahar, Y. (2005). Probabilistic Abstraction of Uncertain Temporal Data for Multiple Subjects. In: Zucker, JD., Saitta, L. (eds) Abstraction, Reformulation and Approximation. SARA 2005. Lecture Notes in Computer Science(), vol 3607. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527862_23
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DOI: https://doi.org/10.1007/11527862_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27872-6
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