Introduction  Use Allen’s Temporal Relations [3] to identify temporal relations among Activities in Daily Life of the resident.  Allen’s relations form the basic representation of the temporal intervals, which when used with constraints become a powerful method of expressing expected temporal orderings between events in a smart environment.  In this poster we consider the problem of activity prediction based on the discovery and application of temporal relations. Smart Home Goals: Data Collection Environment Literature cited Temporal Relations “It is common to describe scenarios using time intervals rather than time points” - James F. Allen Step 3: Temporal Rules Enhancement to the Prediction. Mining Sensor Data in Smart Environment for Temporal Activity Prediction Vikramaditya R. Jakkula & Diane J. Cook Washington State University First International Workshop on Knowledge Discovery from Sensor Data (Sensor-KDD '07) Conclusions Figure 2. Real & Synthetic Datasets. Figure 3. Smart Home Scenario illustrated using temporal relations. Acknowledgments This work is supported by NSF grant IIS Contact Us: Vikramaditya R. Jakkula Diane J. Cook Algorithm : Temporal Interval Analyzer Input: data timestamp, event name and state Repeat While [Event && Event + 1 found] Find paired “ON” or “OFF” events in data to determine temporal range. Read next event and find temporal range. Identify relation type between event pair from possible relation types (see Table 1). Record relation type and related data. Increment Event Pointer Loop until End of Input..  Due to small datasets used, we use the top rules generated with a minimum confidence of 0.5 and a minimum support of  Confidence level above 0.5 and support above 0.05 could not be used, as they could not result in any viable rules.  The major goal of MavHome project is to design an environment that acts as an intelligent agent and can acquire information about the resident and the environment in order to adapt the environment to the residents and meet the goals of comfort and efficiency.  This sensor network consists of around 100 sensors include motion, devices, light, pressure, humidity and more.  Unified project incorporating varied AI techniques cross disciplinary with mobile computing, databases,multimedia, and others. Figure 1. MavHome Smart Home Architecture [1] Adapt to Needs Cost Effective and Reliable Maximum Comfort and Security Food “Contains” water or Water “Before” pills or Food “Meets” pills or Food “Contains” water “before” pills Food Water Pills Time Interval  Why Temporal Relations? Reminder system based on temporal relations. Reminder Assistance 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 a normal repair? Maintenance If oven used for turkey, is turkey at home? Temporary Need Analysis Increase predictive accuracy by incorporating additional temporal information. Improve Prediction Temporal Relation Usable Before X After During Contains X Overlaps X Overlapped-By Meets X Met-by Starts Started-By Finishes Finished-By Equals Allen’s 13 Relations Experimentation & Results Step 1: Process raw data to form temporal intervals Datasets Parameter Setting No of Days No of Events No of Intervals Identified Size of Data Synthetic KB Real KB [3] Raw Sensor Data Interval Data Temporal Relations Data Raw Sensor Data Timestamp Sensor State Sensor ID 3/3/ :18:00 AM OFF E16 3/3/ :23:00 AM ON G12 Identify Time Intervals Date Sensor ID Start Time End time. 03/02/2003 G11 01:44:00 01:48:00 03/02/2003 G19 02:57:00 01:48:00 Associated Temporal Relations Date time Sensor ID Temporal Relation Sensor ID 3/3/ :00:00 AM G12 DURING E16 3/3/ :00:00 AM E16 BEFORE I14 Step 2: Association rule generation using Weka  Use Apriori classifier in Weka [2] for generating best rules with a given support and confidence. Equation to calculate evidence using Probability of occurrence: P(Z|Y) = |After(Y,Z)| + |During(Y,Z)| + |OverlappedBy(Y,Z)| + |MetBy(Y,Z)| + |Starts(Y,Z)| + |StartedBy(Y,Z)| + |Finishes(Y,Z)| + |FinishedBy(Y,Z)| + |Equals(Y,Z)| / |Y| Results: D ATA S ET A CCURACY %E RROR % R EAL (W ITHOUT R ULES )5545 S YNTHETIC (W ITHOUT R ULES ) 6436 R EAL (W ITH R ULES )5644 S YNTHETIC (W ITH R ULES )6931 Online Model: Enhance existing ALZ prediction [4]. Prediction c :=P(C|P) :=P(C|P) SEQ +P(C|P) TEM/Global – (α * P(C|P) TEM) Where α = | #C PHRASE | / | #C GLOBAL |.  Real data had 1.86% and synthetic data had 7.81% prediction improvements.  Good model for offline prediction of multiple events.  Cannot adapt to online dynamic model of the environment. Pseudo code: Temporal Rules Enhanced prediction. [1] Get the current predicted output and check for any rule which satisfies it. If yes proceed else goto next predicted. [2] Now we check for the relation and based on the evidence as calculated by equation displayed below if it is greater than Mean+2* Std. Dev. Then add this to the predicted. [3] If relation is after the evidence becomes cumulative until greater then Mean +2*Std. Dev. [1] G. Michael Youngblood, Lawrence B. Holder, and Diane J. Cook. Managing Adaptive Versatile Environments. Proceedings of the IEEE International Conference on Pervasive Computing and Communications, [2] Ian H. Witten, Eibe Frank Data Mining: Practical Machine Learning Tools and Techniques, 2nd Edition. Morgan Kaufmann, San Francisco. [3]James F. Allen, and George Ferguson, Actions and Events in Interval Temporal Logic, Technical Report 521, July [4] K. Gopalratnam & D. J. Cook (2004). Active LeZi: An Incremental Parsing Algorithm for Sequential Prediction. International Journal of Artificial Intelligence Tools. 14(1-2):  Unique and new Approach.  Real data had 1.86% and synthetic data had 7.81% improvement.  Larger datasets would be incorporated.  Extended model includes direct application of temporal relations based probability to calculate the prediction.  expansion of the temporal relations by including more temporal relations, such as until, since, next, and so forth, to create a richer collection of useful temporal relations. Sample of the best rules observed in a real smart environment dataset: Activity=C11 Relation=CONTAINS 36 ==> Activity=A14 36 Activity=D15 Relation=FINISHES 32 ==> Activity=D9 32 Activity=D15 Relation=FINISHESBY 32 ==> Activity=D9 32 Activity=C14 Relation=DURING 18 ==> Activity=B9 18