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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

Data Management Systems for Sensor Data

Magdalena Balazinska

University of Washington

processing sensor data outside sensor networks
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
sensor data cleaning
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
extracting high level information from sensor data
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
summary
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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