Data Management Challenges and Opportunities in the Digital Home*

1. Data Management Challenges and Opportunities in the Digital Home* Mike Franklin UC Berkeley *in collaboration with Intel Research Berkeley ICME Amsterdam July 2005

2. Somewhere in Holland… Michael Franklin UC Berkeley EECS

3. Data in the Home - Today • Many sources and sinks • Many media types, file formats • “Outside” sources (e.g. CDDB, Tivo) • Ad hoc, manual sharing/synching • Minimal backup/archive support • Manual organization, annotation, and search. • Minimal sharing and integration across devices or applications. Michael Franklin UC Berkeley EECS

4. Data in the Home - Where it’s Headed • Still no help with: backup, archive, organization, search, annotation, sharing, and integration. • Who/What will manage all of this? • Standards enable new connections • Even more sources and sinks • Everything becomes “smart” Michael Franklin UC Berkeley EECS

5. Is it a Networking Problem? – Audio Server The devices depicted in these scenarios are for illustrative purposes only and have no relation to specific products planned by any manufacturer. Michael Franklin UC Berkeley EECS From the Digital Home Working Group, 2004

6. Is it a Networking Problem? – Images The devices depicted in these scenarios are for illustrative purposes only and have no relation to specific products planned by any manufacturer. Michael Franklin UC Berkeley EECS From the Digital Home Working Group, 2004 The devices depicted in these scenarios are for illustrative purposes only and have no relation to specific products planned by any manufacturer.

7. Is it a Networking Problem? – Video The devices depicted in these scenarios are for illustrative purposes only and have no relation to specific products planned by any manufacturer. Michael Franklin UC Berkeley EECS From the Digital Home Working Group, 2004 The devices depicted in these scenarios are for illustrative purposes only and have no relation to specific products planned by any manufacturer.

8. Is it a Vendor-Specific Problem? “Box Bias” - center of home is… • PC and OS vendors - more powerful desktop machines with media-friendly OS’s. • TV vendors • Set-top Box vendors • DVR vendors • Game Console vendors • Security System vendors • Home networking vendors • Home automation vendors Michael Franklin UC Berkeley EECS

9. Is it an AI Problem? “A residence equipped with computing and information technology which anticipates and responds to the needs of the occupants, working to promote their comfort, convenience, security and entertainment through the management of technology within the home and connections to the world beyond” Harper [2003] “How smart does the bed in your house have to be before you are afraid to go to sleep at night?” Rich Gold, The Plentitude Michael Franklin UC Berkeley EECS

10. Digital Home  “Smart” Home? • Multidisciplinary collaborations of Technologists, Ethnographers, Architects • Sensors enable home to monitor: • Temperature • Light • Occupancy • Interactions? • Mood? • Learning algorithms use measurements and feedback to predict occupant actions and needs. Aware Adaptive Michael Franklin UC Berkeley EECS

11. The Aware Home Michael Franklin UC Berkeley EECS

12. The Adaptive House Michael Franklin UC Berkeley EECS

13. Current Status • These and many other labs have helped push the research. • Although except for Moser’s Adaptive House, they have not been really lived-in. • But, smart home technology has been slow to make it to the mass market. Michael Franklin UC Berkeley EECS

14. Our Approach The home is becoming an increasingly data-intensiveenvironment. Point solutions will not scale. A shared, data-centric infrastructure is needed. A successful solution will enable “digital” home applications today, and provide a basis for “smart” home applications in the future. Michael Franklin UC Berkeley EECS

15. What can we learn from Enterprise Data Management? • Data Modeling - identifying and organizing entities and their relationships. • Integration - combining disparate data. • Declarative Queries - set-based languages for saying what you want, not how to get it. • Indexing - accelerators for searching large data sets. • Data Protection - Backup, Recovery, Archiving, Persistence, Consistency, Security. Michael Franklin UC Berkeley EECS

16. So, it’s a Database Problem??? Michael Franklin UC Berkeley EECS

17. The Home is Different • No IT Staff to run it  hands-off operation. • Minimal IT budget  must be cost-effective. • User’s can and will reject it  flexibility, adaptibility, context-awareness, “calmness”. • People, families, homes, and contents change. • Roles, needs, relationships not so clearly defined  “SAP” for the home unlikely; privacy concerns are challenging. Michael Franklin UC Berkeley EECS

18. Our Driving Applications • Preservation and location of digital information. • Increasingly crucial data being stored on inherently short-lived devices. Want automatic backup, recovery, and caching. • Tests: basic data management infrastructure, self-management. • Energy management • Balance comfort and expense • Tests: sensor inputs, house temperature response models. • Information displays - Home Portal • Example: InLook prototype • Personalized news • Context-based media retrieval • State of family members, house, etc. • Tests: Use of large/cheap displays, explore/demonstrate advantages of data integration. Michael Franklin UC Berkeley EECS

19. $ Pricing Signals Energy Management Application Michael Franklin UC Berkeley EECS

20. Home Portal - “InLook” (summer ‘04) User context Preferences Sensors Dwell detector Michael Franklin UC Berkeley EECS

21. Hardware - The “data furnace” Goal: Invisible locus of control and reliable storage for the digital home. (not a PC) Requirements: • Self- configuring, maintaining, tuning • Highly-reliable • Long life (~ 25 years) • Continually expandable/upgradable • Reasonable Cost No more cost or trouble than the home’s furnace. Michael Franklin UC Berkeley EECS

22. Queries & Rules Archive Software Architecture apps Learning Engine Discoverer (upnp) • “Data-centric” view • Leverage our previous work on sensors and monitoring. • Bus-based architecture for flexibility. • Central storage with caching at devices. • Repository for Data and Metadata. • Repository for cross device/app Indexes. Media generators Bus Sensors Actuators Michael Franklin UC Berkeley EECS

23. UCB/IRB Digital Home Project 3 Challenges in Data Furnace Development • Schema and Metadata • Monitoring and Complex Event Processing • Integrating Sensors Michael Franklin UC Berkeley EECS

24. The Metadata Challenge Need a model of: • People • Family members and others. • Roles, relationships,… • Preferences • Home Layout • Devices & Data Michael Franklin UC Berkeley EECS

25. Schema: Home, Place, Person, Event, Sensor Some Issues: • Model must evolve with the home and its members. • Self-configuring: Cannot require significant human “start up” effort. • Can such highly-personal entities such as homes be captured in a common schema? Michael Franklin UC Berkeley EECS

26. Complex Event Processing • Needed for monitoring and actuation. • Basis for system self-maintenance. • Key to prioritization (e.g., of detail data) • Can be implemented as simple extensions to a streaming Query Language. • Challenge: a single system that simultaneously handles events spanning seconds to years. Michael Franklin UC Berkeley EECS

27. Data Data Stream Processing Result Tuples Result Tuples Queries Event Specs Subscrip-tions Queries Data Traditional Database Data Stream Processor • Data streams are unending • Continuous, long-running queries • Real-time processing http://telegraph.cs.berkeley.edu Michael Franklin UC Berkeley EECS

28. Temporal Aggregation A typical streaming query Window Clause SELECT S.room, AVG(temp) FROM SOME_STREAM S [range by ‘5 seconds’ slide by ‘5 seconds’] WHERE S.floor = ‘first’ GROUP BY S.room “I want to look at 5 seconds worth of data” “I want a result tuple every 5 seconds” Data Stream … Result Tuple(s) Result Tuple(s) Michael Franklin UC Berkeley EECS

29. Spatial Aggregation • Continuous and Streaming • Hierarchical • Coarser spatial and temporal granularity as you go up? • Some Issues • Automatic placement and optimization • Sharing of lower-level streams “I provide avg values for the entire house” “I provide avg values for a floor” “I provide avg values for a single room” “I provide raw readings for an area” Michael Franklin UC Berkeley EECS

30. Sensor-based Systems • Receptors everywhere! • Wireless sensor networks, RFID technologies, security systems, smart appliances, input devices ... Need proper abstractions for dealing with varied devices Michael Franklin UC Berkeley EECS

31. “Virtual Device (VICE) API” Metaphysical Data Independence Problem: how to deal with the complexity of physical devices? Michael Franklin UC Berkeley EECS http://hifi.cs.berkeley.edu

32. Integrating Heterogeneous Devices Using VICE: RFID & Sensor Motes The Loudmouth Detector Michael Franklin UC Berkeley EECS

33. The Virtues of VICE • Once you have the right abstractions: • Soft Sensors (e.g., a “person detector”) • Quality and lineage streams • Pushdown of external validation information • Power management and other optimizations • Data Archiving • Model-based sensing • “Non-declarative” code • … Michael Franklin UC Berkeley EECS

34. Putting it all Together • We are proposing a data-centric view towards digital home infrastructure. • The goal is to adapt enterprise-class data management techniques to the home. • Non-trivial differences between home and enterprise. • Currently focused on: • Data modeling for the home. • Self-managing hardware and software platforms using complex event processing and continuous queries. • Sensor integration using the VICE API. • We are also strengthening our collaborations with ethnographers and architects. Michael Franklin UC Berkeley EECS

35. Conclusions Our message: Home is where the bits are… via Anind Dey (CMU) Michael Franklin UC Berkeley EECS

36. Acknowledgements This is joint work with the Digital Home project at UC Berkeley and Intel Research Berkeley, and the UC Berkeley Database Group: • Ryan Aipperspach • Kurt Brown • John Canny • Lilia Gutnik • Wei Hong • Allison Woodruff • Gustavo Alonso • Shawn Jeffery • Sailesh Krishnamurthy • Shariq Rizvi Michael Franklin UC Berkeley EECS