1. TCU Dept. of Computer Science CRESCENT Database Issues in Smart Homes Pervasive Intelligent Environments Spring 2004

2. TCU Dept. of Computer Science CRESCENT Topics: Lecture 1 What’s being done What do you need it for? Issues Where’s the data come from? Data sources DB Communication How do we store the data? Storing LOTS of data: –Data warehouses Now we’ve got it, what do we do with it? Looking ahead Next time: examples, more troubles…

3. TCU Dept. of Computer Science CRESCENT DB in Smart Environements

4. TCU Dept. of Computer Science CRESCENT UTA MavHome DB Active –Reactive & proactive (e.g., to predict) Distributed Information collection agents –Rules Local Agent: what data they need to collect Distributed: coordinate overall monitoring of collected information Continuous monitoring of events Extension of SNOOP

5. TCU Dept. of Computer Science CRESCENT Microsoft Easy Living DB (2002) Relational –Fast & robust, but awkward for some data World Model DB Describes: –Computing devices –People and their personal preferences/settings –Services –Rooms and doorways Serves as Abstraction Layer between sensors and application that use data from sensors –e.g. new sensors  no change to applications

6. TCU Dept. of Computer Science CRESCENT Stanford Interactive Workspace Uses LORELORE –A semi-structured XML DB system Still available, but work stopped in 2000 –Data stored is catalog of (index to) documents, images, 3-D models, application- specific domain models

7. TCU Dept. of Computer Science CRESCENT What do you need it for? Kitchen Entertainment General (many uses) –When does Molly usually come home? –Where is Rigel now? –What’s the rain forecast?

8. TCU Dept. of Computer Science CRESCENT Issues Data source –Local (sensors, input devices) –Outside (weather forecast) Data quality Data volume Data lifetime –Do you save images once info extracted (e.g. Ian walked in front door at 2:13pm) Data rep –Relational is awkward

9. TCU Dept. of Computer Science CRESCENT Data input LOTS AND LOTS OF DATA –Required for good prediction, decision making Inputs from –Sensors –Bar code / RF readers –Voice –PC keyboard Sensors Recording media choices

10. TCU Dept. of Computer Science CRESCENT Sensor Databases UTA IT Lab and Diane Cook –sensor-generated data collection, management, analysis, triggering –continuous queries, stream query processing Sharma Chakravarthy’s work –Active databases

11. TCU Dept. of Computer Science CRESCENT Real Sensor Data Input 9/8/2002 2:0:1 AM~A5 (Coffee Maker) ON 9/8/2002 1:6:59 AM~A9 (A/C) ON 9/8/2002 3:58:52 AM~A0 (Stereo) ON 9/8/2002 5:57:0 AM~A2 (Kitchen Light) ON 9/8/2002 3:1:42 AM~A5 (Coffee Maker) OFF 9/8/2002 7:8:3 AM~A3 (Stove) ON 9/8/2002 12:54:52 PM~A10 (Bathroom Light) ON 9/8/2002 4:58:5 AM~A0 (Stereo) OFF 9/8/2002 8:1:20 AM~A3 (Stove) OFF 9/8/2002 9:6:10 AM~A8 (Computer) ON 9/8/2002 10:8:19 AM~A4 (Bathtub Heater) ON 9/8/2002 11:9:4 AM~A0 (Stereo) ON 9/8/2002 9:4:5 AM~A8 (Computer) OFF 9/8/2002 10:9:4 AM~A4 (Bathtub Heater) OFF 9/8/2002 2:2:5 PM~A10 (Bathroom Light) OFF 9/8/2002 2:52:37 PM~A0 (Stereo) OFF 9/8/2002 4:2:0 PM~A9 (A/C) OFF

12. TCU Dept. of Computer Science CRESCENT Simulated Sensor Input 11/15/2001 7:3:53 AM (BedRoom Alarm) A9 ON 11/15/2001 7:4:2 AM (Bath Shower) A11 ON 11/15/2001 7:4:8 AM (Bath BathDisplay) A10 ON 11/15/2001 7:4:8 AM (Bath L4) A4 ON 11/15/2001 7:4:45 AM (Kitchen CoffeePot) A8 ON 11/15/2001 7:4:47 AM (Kitchen KitchenDisplay) A12 OFF 11/15/2001 7:4:55 AM (Kitchen KitchenDisplay) A12 ON 11/15/2001 7:4:47 AM (LivingRoom Thermostat) A16 ON 11/15/2001 7:4:49 AM (Kitchen L3) A3 ON 11/15/2001 7:4:50 AM (Garage/Patio Locks) A17 OFF 11/15/2001 9:29:59 AM (Yard Sprinklers) A14 ON 11/15/2001 9:29:59 AM (LivingRoom JanitorRobot) A13 ON 11/15/2001 6:59:53 PM (Garage/Patio Locks) A17 ON

13. TCU Dept. of Computer Science CRESCENT Media Viewing Data

14. TCU Dept. of Computer Science CRESCENT What data to collect? Digital Silhouettes (Predictive Networks) – Predicting web surfing behavior ($$$) Microsoft (2002) track TV viewing preferences –140 data items for each user Demographics (50) –Subcategories within gender, age, income, education, occupation, and race 90 Content preferences –golf, music, yoga

15. TCU Dept. of Computer Science CRESCENT Communication with the DB Agent communication languages –KQML –FIPA XML SOAP UPnP (upnp.org) For more information, slides 11-26 of –personal.tcu.edu/~lburnell/SE/SmartHomeAgents.zippersonal.tcu.edu/~lburnell/SE/SmartHomeAgents.zip

16. TCU Dept. of Computer Science CRESCENT KQML Examples Turn the TV on to channel 5 –(sendCommandToDevice :deviceName TV: type ask :command (alterSettings :isOn 1 :channel 5)) Can embed into an event –(event :year 2001 :month October :dayOfMonth 15 :hour 15 :minute 45 :command (sendCommandToDevice :deviceName TV: type ask :command (alterSettings :isOn 1 :channel 5)))

17. TCU Dept. of Computer Science CRESCENT Data Warehouses An organization-wide snapshot of data, typically used for decision-making Evolved via consultants, RDBMS vendors, and startup companies. –All had something to prove; to "differentiate their product". –Researchers making progress cleaning up the BIG mess they created A DBMS that runs decision-making queries efficiently sometimes called a "Decision Support System" DSS –OLAP (on-line analytical processing) is 1 class of DSS queries DSS systems and warehouses are typically separate from the on- line transaction processing (OLTP) system Data Mart – a mini-warehouse -- typically a DSS for one aspect or branch of a company, with lots of relatively homogeneous data (i.e. a straight DSS) 02.15.04 from http://redbook.cs.berkeley.edu/lec28.html

18. TCU Dept. of Computer Science CRESCENT Warehouse/DSS properties Very large: 100gigabytes to many terabytes Tends to include historical data Workload: mostly complex queries that access lots of data, and do many scans, joins, aggregations. Tend to look for "the big picture". Updates pumped to warehouse in batches (overnight) Data may be heavily summarized and/or consolidated in advance (must be done in batches too, must finish overnight). –Research work has been done (e.g. "materialized views") -- a small piece of the problem. 02.15.04 from http://redbook.cs.berkeley.edu/lec28.html

19. TCU Dept. of Computer Science CRESCENT Data Warehouses 02.15.04 from http://redbook.cs.berkeley.edu/lec28.html

20. TCU Dept. of Computer Science CRESCENT Data Warehouses Data Cleaning –Data Migration: simple transformation rules (replace "gender" with "sex") –Data Scrubbing: use domain-specific knowledge (e.g. zip codes) to modify data. Try parsing and fuzzy matching from multiple sources. –Data Auditing: discover rules and relationships (or signal violations thereof). Not unlike data mining. Data Loading –can take a very long time! (Sorting, indexing, summarization, integrity constraint checking, etc.) Parallelism a must. –Full load: like one big xact – change from old data to new is atomic. –Incremental loading ("refresh") makes sense for big warehouses, but transaction model is more complex – have to break the load into lots of transactions, and commit them periodically to avoid locking everything. Need to be careful to keep metadata & indices consistent along the way. 02.15.04 from http://redbook.cs.berkeley.edu/lec28.html

21. TCU Dept. of Computer Science CRESCENT Looking Ahead Using the data we have –Prediction –Decision making –Problem Solving –Getting better over time… Reinforcement learning Updating –Bayesian networks –Neural networks –Rules and cases

22. TCU Dept. of Computer Science CRESCENT Looking Ahead: Data Mining & Prediction Find patterns –Verify user supplied patterns –Generate patterns Sequences – HARD! Noise Missing data

23. TCU Dept. of Computer Science CRESCENT Decision Making: Bayes Nets What assumptions and methods allow us to turn observations into causal knowledge, and how can even incomplete causal knowledge be used in planning and prediction to influence and control our environment? * One solution: Bayesian nets –a.k.a. Bayes nets, Bayesian networks, belief networks *From from “Causation, Prediction, and Search, 2 nd Edition”, Spirtes, Glymour & Scheines

24. TCU Dept. of Computer Science CRESCENT Problem Solving Rule-based systems Case-based reasoning Neural networks Influence diagrams

25. TCU Dept. of Computer Science CRESCENT Looking Ahead: Reinforcement Learning "RL is learning what to do --- how to map situations to actions --- so as to maximize a numerical reward signal. The learner is not told which actions to take, as in most machine learning, but instead must discover which actions yield the most reward by trying them." from Reinforcement Learning: An Introduction.Reinforcement Learning: An Introduction. MDP & semi-MDP: assumptions about how world can be described and that you don’t have to remember the past. Agents in a state can choose actions to take in an environment. –Choice (decision) is rewarded or punished –Agent learns to make better choices Model can be stored in database. May have many states/actions/probabilities to store.

26. TCU Dept. of Computer Science CRESCENT More information Filip Perich, Anupam Joshi, Tim Finin, and Yelena Yesha, “On Data Management in Pervasive Computing Environments. IEEE Transactions on Knowledge and Data Engineering, October 12, 2003 –http://ebiquity.umbc.edu/v2.1/_file_directory_/papers/3.pdfhttp://ebiquity.umbc.edu/v2.1/_file_directory_/papers/3.pdf Fundamentals of Database Systems, 4 th edition. Elmasri and Navathe. http://mavhome.uta.edu/publications.html Reinforcement learning –http://www.aaai.org/Pathfinder/html/reinf.html –http://reinforcementlearning.ai-depot.com/Tutorials.htmlhttp://reinforcementlearning.ai-depot.com/Tutorials.html