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Table 3: Experimental results of the first data set

Table 3 Experimental results of the first data set

Related Figures (7)

Figure 1: An illustrative event sequence. The event type of an event is labeled above its occurrence
Figure 1: An illustrative event sequence. The event type of an event is labeled above its occurrence
Table 1: Summary of parameters of p-patterns Definition 5 A set of event types A, C A is a partially periodic temporal association (p-pattern)
Table 1: Summary of parameters of p-patterns Definition 5 A set of event types A, C A is a partially periodic temporal association (p-pattern)
algorithms to discover p-pattern with unknown periods by combining the two sub-tasks
algorithms to discover p-pattern with unknown periods by combining the two sub-tasks
Now, we characterize inter-arrivals for an ideal partial periodic point sequence with parameters p and
Now, we characterize inter-arrivals for an ideal partial periodic point sequence with parameters p and
associations. In contrast, the association-first algorithm still finds 100% of the p-patterns.
associations. In contrast, the association-first algorithm still finds 100% of the p-patterns.
Figure 5: Average run time vs. the number of events
Figure 5: Average run time vs. the number of events
Table 4: Experimental results of the Second data set
Table 4: Experimental results of the Second data set
Related topics:
Computer NetworksPhase NoiseTemporal LogicReal-world ApplicationDistributed Environment
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