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Temporal Pattern Discovery in Smart Homes

Vikramaditya Jakkula. Temporal Pattern Discovery in Smart Homes. Smart Homes. Experimentation Environment. MavPad Argus Sensor Network around 100 Sensors. include Motion, Devices, Light, Pressure, Humidity and more. . Temporal Relations In Smart Homes. A “before” B “finishes-by” C.

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Temporal Pattern Discovery in Smart Homes

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  1. Vikramaditya Jakkula Temporal Pattern Discovery in Smart Homes

  2. Smart Homes

  3. Experimentation Environment • MavPad Argus Sensor Network • around 100 Sensors. • include Motion, Devices, Light, Pressure, • Humidity and more.

  4. Temporal Relations In Smart Homes A “before” B “finishes-by” C

  5. Allen’s 13 Temporal Relations Bounded

  6. Why Temporal Relations?

  7. Experimentation • Step 1: Eliminate Unnecessary datasets and identify the most frequent Itemset using Apriori Algorithm. • Step 2: Use Weight based Relation analysis to identify best relation to remove ambiguity.

  8. Step 1:The Apriori Algorithm Example Database D C1 L1 Scan D C2 C2 L2 Scan D L3 C3 Scan D

  9. Table 3: Sample of Frequent Relation Pairs. Step 2: Relation Formation • Use the above define temporal relations with the weight based rule given below to identify the best temporal relations.

  10. Future Directions • Prediction of activity. • Anomaly detection mechanism. • Visualization of temporal intervals for monitoring daily activities and lifestyle.

  11. Conclusion • Time for Questions! • Thank you From AI Lab@ WSU!

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