data management challenges and opportunities in the digital home*

Data Management Challenges and Opportunities in the Digital

Home*

ICMEAmsterdamJuly 2005

Mike FranklinUC Berkeley

*in collaboration with Intel Research Berkeley

Michael Franklin UC Berkeley EECS

Somewhere in Holland…


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.


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?


Is it a Networking Problem? – Audio

The devices depicted in these scenarios are for illustrative purposes onlyand have no relation to specific products planned by any manufacturer.

Server

From the Digital Home Working Group, 2004


Is it a Networking Problem? – Images





Is it a Networking Problem? – Video





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…


“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?


• 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?


The Aware Home


The Adaptive House


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.


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.


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.


So, it’s a Database Problem???


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.


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.


Energy Management Application

Energy Management

Application Space

Infrastructure Space

Rules, Models, Archive

User/Environment Context

Calendar Information

$Pricing Signals


Home Portal - “InLook” (summer ‘04)

Dwell detector

Preferences

User context

Sensors


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.


Software Architecture

Discoverer(upnp)

appsLearning Engine

Bus

Mediagenerators

ArchiveQueries & 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.


UCB/IRB Digital Home Project

3 Challenges in Data Furnace Development

• Schema and Metadata

• Monitoring and Complex Event Processing

• Integrating Sensors


The Metadata Challenge

Need a model of:• People

•Family members and others.

•Roles, relationships,…

•Preferences• Home Layout• Devices & Data


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?


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.


Data Stream Processing

QueriesQueries

Event SpecsEvent Specs

Subscrip-Subscrip-tionstions

QueriesQueries

Data

Traditional Database

Data Stream Processor

Result Tuples Result Tuples

•Data streams are unending

•Continuous, long-running queries

•Real-time processing

Data

http://telegraph.cs.berkeley.edu


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


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


Sensor-based Systems

• Receptors everywhere!• Wireless sensor networks, RFID technologies,

security systems, smart appliances, input devices ...

Need proper abstractions for dealing with varied devices


Metaphysical Data Independence

“Virtual Device(VICE)API”

Problem: how to deal with the complexity of physical devices?

http://hifi.cs.berkeley.edu


Integrating Heterogeneous Devices Using VICE: RFID & Sensor Motes

The Loudmouth Detector


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• …


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.


Conclusions

via Anind Dey (CMU)

Our message: Home is where the bits are…


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

data management challenges and opportunities in the digital home*

Documents

home isis

approachthe home

smart home applications

digital home applications

smart home technology

digital home working

specific products

illustrative purposes