TCU Dept. of Computer Science CRESCENT Database Issues in Smart Homes Pervasive Intelligent...

TCU Dept. of Computer Science

Database Issues in Smart Homes

Pervasive Intelligent EnvironmentsSpring 2004


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…


DB in Smart Environements

EasyLiving Microsoft Relational

Hal

MIT Artificial Intelligence Laboratory

Home Automation IBM

Smart Home for Health MonitoringMedical Automation Research Center

House_n MIT

IIBTrinity College Dublin

i-LAND Ambiente

Interactive Workspaces Stanford University XML DBMS: LORE

MavHomeUniversity of Texas at Arlington Active, distributed


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


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


Stanford Interactive Workspace

• Uses LORE– 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


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?


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


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


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


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


Simulated Sensor Input

11/15/2001 7:3:53 AM (BedRoom Alarm) A9 ON11/15/2001 7:4:2 AM (Bath Shower) A11 ON11/15/2001 7:4:8 AM (Bath BathDisplay) A10 ON11/15/2001 7:4:8 AM (Bath L4) A4 ON11/15/2001 7:4:45 AM (Kitchen CoffeePot) A8 ON11/15/2001 7:4:47 AM (Kitchen KitchenDisplay) A12 OFF11/15/2001 7:4:55 AM (Kitchen KitchenDisplay) A12 ON11/15/2001 7:4:47 AM (LivingRoom Thermostat) A16 ON11/15/2001 7:4:49 AM (Kitchen L3) A3 ON11/15/2001 7:4:50 AM (Garage/Patio Locks) A17 OFF11/15/2001 9:29:59 AM (Yard Sprinklers) A14 ON11/15/2001 9:29:59 AM (LivingRoom JanitorRobot) A13 ON11/15/2001 6:59:53 PM (Garage/Patio Locks) A17 ON


Media Viewing DataWatching Events

Date Day Mood Start End Device Program name Type Comments Others Rating

020302 Su normal 1330 1600 T nba basketball sports dallas mavericks go team none 5

020302 Su normal 1700 2100 t super bowl sports gotta watch the commercials Dad 5

020402 m normal 1900 2000 t boston public drama hot teachers none 5

020402 m normal 2000 2100 t ally mcbeal drama funny lawyers none 4

020402 m normal 2300 100 V WWF RAW wrestling testosterone none 5

020502 t normal 2100 2200 t philly drama hot lawyers none 4

020602 w bored 1830 2200 t nba basketball sports GO MAVS none 5

020702 th tired 1900 2100 t wwf smackdown wrestling its me soap none 5

020702 th tired 2100 2200 t ER drama good show none 4

020802 f excited 1900 2230 t olympics sports gotta watch none 4

020902 sa excited 1900 2230 t olympics sports gotta watch none 4

021002 su ecstatic 1500 1800 t NBA allstar game sports gotta see what

happens none 3

012802 M normal 1900 2000 T Boston Public Drama hot chicks teaching none 5

012802 M normal 2000 2100 T Ally McBeal Drama hot chicks lawyering none 5


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


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.zip


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)))


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


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.



Data Warehouses



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.



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


Looking Ahead: Data Mining & Prediction

• Find patterns – Verify user supplied patterns– Generate patterns

• Sequences – HARD!• Noise • Missing data


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, 2nd Edition”, Spirtes, Glymour & Scheines


Problem Solving

• Rule-based systems• Case-based reasoning• Neural networks• Influence diagrams


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.

• 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.


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.pdf

• Fundamentals of Database Systems, 4th 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.html

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