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

2. Michael Franklin UC Berkeley EECS Somewhere in Holland…

3. Michael Franklin UC Berkeley EECS 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.

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

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

6. Michael Franklin UC Berkeley EECS 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. 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. Michael Franklin UC Berkeley EECS 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. 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. Michael Franklin UC Berkeley EECS Is it a Vendor-Specific Problem? 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 “Box Bias” - center of home is…

9. Michael Franklin UC Berkeley EECS “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 Is it an AI Problem?

10. Michael Franklin UC Berkeley EECS 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 Digital Home  “Smart” Home?

11. Michael Franklin UC Berkeley EECS The Aware Home

12. Michael Franklin UC Berkeley EECS The Adaptive House

13. Michael Franklin UC Berkeley EECS 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.

14. Michael Franklin UC Berkeley EECS Our Approach The home is becoming an increasingly data-intensive environment. 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.

15. Michael Franklin UC Berkeley EECS 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.

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

17. Michael Franklin UC Berkeley EECS 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.

18. Michael Franklin UC Berkeley EECS 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.

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

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

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

22. Michael Franklin UC Berkeley EECS Software Architecture Discoverer (upnp) apps Learning Engine Bus Media generators Archive Queries & Rules Sensors Actuators “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.

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

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

25. Michael Franklin UC Berkeley EECS 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?

26. Michael Franklin UC Berkeley EECS 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.

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

28. Michael Franklin UC Berkeley EECS Temporal Aggregation 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” A typical streaming query Result Tuple(s) Data Stream Result Tuple(s) … Window Clause

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

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

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

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

33. Michael Franklin UC Berkeley EECS 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 …

34. Michael Franklin UC Berkeley EECS 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.

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

36. Michael Franklin UC Berkeley EECS 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