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Data Management Systems for Sensor Data. Magdalena Balazinska University of Washington. Processing Sensor Data Outside Sensor Networks. Approach 1: Store data first, then process it Traditional databases, IrisNet Approach 2: Process data as it streams

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Data management systems for sensor data l.jpg

Data Management Systems for Sensor Data

Magdalena Balazinska

University of Washington


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Processing Sensor Data Outside Sensor Networks

  • Approach 1: Store data first, then process it

    • Traditional databases, IrisNet

  • Approach 2: Process data as it streams

    • Stream processing engines: Borealis, STREAM, etc.

  • But users want both at the same time:

    • Need to integrate live streams with stream archives

    • Challenges: archive size, stream speed, distribution, federation, fault-tolerance, etc.

    • Moirae project at the University of Washingtonhttp://data.cs.washington.edu/moirae/

    • Other projects: HiFi and LATTE


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Sensor Data Cleaning

  • Sensor data contains errors

    • Can clean some but not all errors inside sensor network

  • Need to clean data at higher levels of abstraction

    • Using deterministic techniques (e.g., ESP framework)

    • Using models (e.g., BBQ project and follow ons)

    • Using integrity constraints (StreamClean project at UW) http://data.cs.washington.edu/streamclean/

  • Cannot clean all errors deterministically

    • Need to build systems that can handle probabilistic data


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Extracting High-Level Information from Sensor Data

  • Sensors produce ambiguous, low-level information

  • But applications are interested in high-level events

    • These events are increasingly more sophisticated as sensor deployments and sensor diversity grow

  • Need new languages and systems to extract events

    • Probabilistic Event EXtraction: PEEX project at UW

      http://data.cs.washington.edu/peex/

    • Other projects: SASE, activity inference in AI


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Summary

  • As sensor deployments

    • Become common place

    • Are used for long-lasting applications

  • Need new, powerful data management systems

  • Requirements include

    • Integrate live data streams with stream archives

    • Perform data cleaning

    • Extract high-level information from low-level sensor data

    • All this in a distributed and federated environment


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