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Linear Temporal Sequences and Their Interpretation Using Midpoint Relationships
January 2005 (vol. 17 no. 1)
pp. 133-135
ASCII Text
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John F. Roddick, Carl H. Mooney, "Linear Temporal Sequences and Their Interpretation Using Midpoint Relationships," IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 1, pp. 133-135, January, 2005.
 
 
BibTex
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@article{ 10.1109/TKDE.2005.12,
author = {John F. Roddick and Carl H. Mooney},
title = {Linear Temporal Sequences and Their Interpretation Using Midpoint Relationships},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {17},
number = {1},
issn = {1041-4347},
year = {2005},
pages = {133-135},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2005.12},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - Linear Temporal Sequences and Their Interpretation Using Midpoint Relationships
IS - 1
SN - 1041-4347
SP133
EP135
EPD - 133-135
A1 - John F. Roddick,
A1 - Carl H. Mooney,
PY - 2005
KW - Temporal reasoning
KW - temporal uncertainty
KW - Allen temporal relationships
KW - Freksa semi-intervals.
VL - 17
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
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.

[1] J.F. Allen, “Maintaining Knowledge About Temporal Intervals,” Comm. ACM, vol. 26, no. 11, pp. 832-843, 1983.
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[5] C. Freksa, “Temporal Reasoning Based on Semi-Intervals,” Artificial Intelligence, vol. 54, pp. 199-227, 1992.
[6] M.B. Vilain, “A System for Reasoning About Time,” Proc. Nat'l Conf. Artificial Intelligence (AAAI '82), pp. 197-201, 1982.
[7] C. Mooney and J.F. Roddick, “Mining Relationships Between Interacting Episodes,” Proc. 2004 SIAM Int'l Conf. Data Mining, U. Dayal and M.W. Berry, eds., 2004.
[8] J.F. Allen, “An Interval-Based Representation of Temporal Knowledge,” Proc. Seventh Int'l Joint Conf. Artificial Intelligence, A. Drinan, ed., pp. 221-226, 1981.
[9] J.F. Allen, “Towards a General Theory of Action and Time,” Artificial Intelligence, vol. 23, no. 2, pp. 123-154, 1984.
[10] J.F. Allen and P.J. Hayes, “Moments and Points in an Interval-Based Temporal Logic,” Computational Intelligence, vol. 5, pp. 225-238, 1989.

Index Terms:
Temporal reasoning, temporal uncertainty, Allen temporal relationships, Freksa semi-intervals.
Citation:
John F. Roddick, Carl H. Mooney, "Linear Temporal Sequences and Their Interpretation Using Midpoint Relationships," IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 1, pp. 133-135, Jan. 2005, doi:10.1109/TKDE.2005.12
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