1. Vikramaditya Jakkula

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

4. A “before” B “finishes-by” C

6. If Pills are to be taken “After” Food, we can notice violation of this activity! Anomaly Detection If Cooker is Spoiled should we call emergency or normal repair? Maintenance If Oven used for Turkey, Is turkey at Home? Temporary Need Analysis Increase prediction accuracy with association rules! Improve Prediction

7.  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. Database D Scan D C1C1 L1L1 L2L2 C2C2 C2C2 C3C3 L3L3

9. Frequent Relation Pairs on Real Datasets Lamp Sensor J16 BEFORE Radio Sensor J11 Lamp Sensor I14 AFTER Lamp Sensor C9 Lamp Sensor I4 EQUALS Lamp Sensor I4 Frequent Relation Pairs on Synthetic Datasets Cooker Before Oven Fan After Cooker Lamp Before Cooker Table 3: Sample of Frequent Relation Pairs.  Use the above define temporal relations with the weight based rule given below to identify the best temporal relations.

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

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