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Table 2 Various types of dynamic time segments used in different applications

From: Context-aware rule learning from smartphone data: survey, challenges and future directions

Base technique Description References
Single parameter A predefined value of time interval, e.g., 15 min is used to generate segments Ozer et al. [60]
A different value of time interval, e.g., 30 min is used for segmentation Do et al. [61], Farrahi et al. [62]
A relatively large value of the parameter, e.g., 2-h is used to generate time segments Karatzoglou et al. [63]
Another large value of time interval, e.g., 3-h is used for segmentation to make the number of segments small Phithakkitnukoon et al. [64]
Calendar Various calendar schedules and corresponding time boundaries are used to model users’ behavior in temporal context Khail et al. [65], Dekel et al. [66], Zulkernain et al. [67], Seo et al. [68], Sarker et al. [28, 59]
Multi-thresholds To identify the lower and upper boundary of a particular segment for the purpose of segmenting time-series log data Halvey et al. [38]
Data shape A data shape based time-series data analysis Zhang et al. [45], Shokoohi et al. [69]
Sliding window A sliding window is used to analyze time-series data Hartono et al. [70], Keogh et al. [71]
Clustering A predefined number of clusters is used to discover rules from time-series data Das et al. [72]
Genetic algorithm A genetic algorithm is used to analyze time-series data Lu et al. [73], Kandasamy et al. [74]