TY - GEN
T1 - Mining interval time series
AU - Villafane, Roy
AU - Hua, Kien A.
AU - Tran, Duc
AU - Maulik, Basab
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 1999.
PY - 1999
Y1 - 1999
N2 - Data mining can be used to extensively automate the data analysis process. Techniques for mining interval time series, however, have not been considered. Such time series are common in many applications. In this paper, we investigate mining techniques for such time series. Specifically, we propose a technique to discover temporal containment relationships. An item A is said to contain an item B if an event of type B occurs during the time span of an event of type A, and this is a frequent relationship in the data set. Mining such relationships allows the user to gain insight on the temporal relationships among various items. We implement the technique and analyze trace data collected from a real database application. Experimental results indicate that the proposed mining technique can discover interesting results. We also introduce a quantization technique as a preprocessing step to generalize the method to all time series.
AB - Data mining can be used to extensively automate the data analysis process. Techniques for mining interval time series, however, have not been considered. Such time series are common in many applications. In this paper, we investigate mining techniques for such time series. Specifically, we propose a technique to discover temporal containment relationships. An item A is said to contain an item B if an event of type B occurs during the time span of an event of type A, and this is a frequent relationship in the data set. Mining such relationships allows the user to gain insight on the temporal relationships among various items. We implement the technique and analyze trace data collected from a real database application. Experimental results indicate that the proposed mining technique can discover interesting results. We also introduce a quantization technique as a preprocessing step to generalize the method to all time series.
UR - https://www.scopus.com/pages/publications/84958047581
UR - https://www.scopus.com/pages/publications/84958047581#tab=citedBy
U2 - 10.1007/3-540-48298-9_34
DO - 10.1007/3-540-48298-9_34
M3 - Conference contribution
AN - SCOPUS:84958047581
SN - 3540664580
SN - 9783540664581
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 318
EP - 330
BT - Data Warehousing and Knowledge Discovery - 1st International Conference, DaWaK 1999, Proceedings
A2 - Tjoa, A. Min
A2 - Mohania, Mukesh
PB - Springer Verlag
T2 - 1st International Conference on Data Warehousing and Knowledge Discovery, DaWaK 1999
Y2 - 30 August 1999 through 1 September 1999
ER -