The Future Home of Data? Michael Franklin UC Berkeley VLDB Conf. - - PDF document

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The Future Home of Data?

Michael Franklin

UC Berkeley

VLDB Conf. August 2002

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The Pervasive Computing Argument

Increasingly ubiquitous networking at all

scales.

– ad hoc sensor nets, wireless, global Internet Explosion in number, locations, and types, of

data sources and sinks.

– mobile devices, P2P networks, data centers Emerging software infrastructure to put it all

together.

– pub/sub, XML, web services, … As a result…

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Prediction

Looking back in ten years, debating the “Future Home of Data” will seem as sensible as debating the “Future Home of Air”. The relevant issue for our community is instead, to determine what our role should be in this new world.

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Data Management in a Networked World

The concept of a “home” for data shows our

intellectual bias.

– This stems from our success to date in

solving previous enterprise data management problems.

Data is a commodity, and like all commodities,

its value is realized only when it is moved to where it is needed.

Thus, we need to apply data management

techniques and insights to data that is constantly in motion.

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Implications

Shift emphasis from “storage” to movement. – must process data “on-the-fly”. – inspiration from and collaboration with networks

and systems research.

Can’t orchestrate processing, must be reactive. – static, global (and probably local) planning and

• ptimization won’t work.

– can be opportunistic (for resources and data). Transactional and Semantic consistency are

huge bottlenecks (they cause blocking).

– Will be used/expected only in limited and

extreme cases.

– CWA is inappropriate.

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Example 1 - Data Stream Processing

Current “hot” trend in our field.

– This is not an accident.

Existing technology can’t cut it:

– Need event-based push/pull processing. – Need continuously-adaptive, shared processing. – Need appropriate data model & query lang.

Time & Window semantics: input and output Continuously improving answers Notification semantics & thresholds

– Approximation, satisficing, and QoS

Must be driven by user needs and context Adapt to available resources & time constraints

– Integration & interaction with “pooled” data.

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Example 2: Sensor Networks

Tiny (or not so tiny) devices measure the

physical world.

– Berkeley “motes”, Smart Dust, Smart Tags – Applications: Transportation, Seismic, Energy, Military… – 2 way – can actuate to effect or actively

monitor the environment

Form dynamic ad hoc networks. Aggregate and communicate streams of values.

– Query Proc. & Routing Protocols work together.

Database insights are crucial here.

– programmability + semantic optimizations. – see Madden et al. OSDI 02

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Conclusions

Data will be everywhere and constantly in

motion.

Need to shift our focus to address the

wealth of new issues raised by pervasive connectivity.

Data management is central to this

emerging environment, and database insights are crucial.