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Real Time Semantic Analysis of Streaming Sensor Data

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Harshal Patni, "Real Time Semantic Analysis of Streaming Sensor Data," MS Thesis Defense, Kno.e.sis Center, Wright State University, Dayton OH, March 21, 2001. …

Harshal Patni, "Real Time Semantic Analysis of Streaming Sensor Data," MS Thesis Defense, Kno.e.sis Center, Wright State University, Dayton OH, March 21, 2001.

More at: http://wiki.knoesis.org/index.php/SSW
Dissertation Advisor: Prof. Amit Sheth

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  • Good Morning Everyone. My name is Harshal Patni and I am here to present my thesis on Streaming Sensor Data but Before we begin lets have a look at how web data evolved over time
  • Social media is the dominant source of streaming data now, however in future sensors would …Data needs to be reduced
  • To walk through the implementation, lets take a sample questionThe question might look trivial, but it contains 3 important partsMarket the datasets we added on LOD
  • Move this slide above
  • Remove the precipitation (in) and also show the general streamAdd the image taken on the phoneRemove the stuff on left when you show select, join and analyze`
  • Remove the precipitation (in) and also show the general streamAdd the image taken on the phoneRemove the stuff on left when you show select, join and analyze`
  • To walk through the implementation, lets take a sample questionThe question might look trivial, but it contains 3 important partsMarket the datasets we added on LOD
  • To walk through the implementation, lets take a sample questionThe question might look trivial, but it contains 3 important partsMarket the datasets we added on LOD
  • To walk through the implementation, lets take a sample questionThe question might look trivial, but it contains 3 important partsAdd linked Sensor Data when highlightThe output of these phases is called LSD and its added on LOD
  • To walk through the implementation, lets take a sample questionThe question might look trivial, but it contains 3 important partsMarket the datasets we added on LOD
  • Get all sensors using well known location names – Problem to be solveAssociate sensor descriptions to well know locations.
  • Get all sensors using well known location names – Problem to be solve
  • Say the numbers in the table
  • RDF because of LOD
  • Highlight the important points in MesoWest DataThe sensor data file just 3 linesMapping file - shorten
  • Emphasize semantically annotated O&MAnd its an XMLTry to replace the cory/weather.owl
  • Use the ssn ontologyAdd the image of ontology for the (Sensor Ontology)http://www.w3.org/2005/Incubator/ssn/wiki/Report_Work_on_the_SSN_ontology
  • Add in block letters saying this is semantically annotated XML and RDF
  • Add Pubby to show derefenced dataPubby should be large to show what it is
  • To walk through the implementation, lets take a sample questionThe question might look trivial, but it contains 3 important partsMarket the datasets we added on LOD
  • To walk through the implementation, lets take a sample questionThe question might look trivial, but it contains 3 important partsMarket the datasets we added on LOD
  • Replace Air Temperature with Non Freezing Temperature
  • Replace Rain Precipitation with PrecipitationSame with airtempearure - temperature
  • To walk through the implementation, lets take a sample questionThe question might look trivial, but it contains 3 important partsMarket the datasets we added on LOD
  • Highlight the query with 3 boxes to show the temp,windspeed and precipitation streamHighlight the feature results too
  • Talk about the observations and features storage
  • Remove the precipitation (in) and also show the general streamAdd the image taken on the phoneRemove the stuff on left when you show select, join and analyze`
  • Linked Data explodes
  • To walk through the implementation, lets take a sample questionThe question might look trivial, but it contains 3 important partsMarket the datasets we added on LOD
  • Linked Data explodes
  • % of FeaturesThrow the text on the top for the statisticsMiddle of storm and hence we have 70 % data reductionElse it would be more
  • Transcript

    • 1.
    • 2. x
      WEB DATA evolved over time
      Real-Time Sensor, Social, Multi-media data
      2010’s
      Dynamic User Generated Content
      2000’s
      Static Document and files
      1990’s
      2
    • 3. x
      Properties of Streaming Data
      Huge Volume
      Rapid
      Continuous
      Information Overload!!
      Heterogeneous
      3
    • 4. x
      Some Statistics
      “A cross-country flight from New York to Los Angeles on a Boeing 737 plane generates a massive 240 terabytes of data”
      - GigaOmni Media
      “Sensors Networks will produce 10-20 times the amount of generated by social media in the next few years”
      - GigaOmni Media
      “More data has been created in the last three years than in all the past 40,000 years”
      - Teradata
      Solution - “Meaningfully summarize this data”
      4
    • 5. 48th ACM Southeast Conference. ACMSE 2010.
      Oxford, Mississippi. April 15-17, 2010.
      From Sensor Streams to Feature Streams
      in Real Time
      HarshalPatni
      Ohio Center of Excellence in Knowledge enabled Computing (Kno.e.sis)
      Wright State University, Dayton, OH
      Part of Semantic Sensor Web @ Kno.e.sis
    • 6. x
      Outline
      Introduction
      Architecture
      Linked Sensor Data
      Feature Streams
      Demonstration
      6
    • 7. x
      Domain
      Weather Domain
      Features
      Blizzard
      Flurry
      RainStorm
      RainShower
      7
    • 8. x
      Explaining the title
      Background Knowledge
      Blizzard
      Rain Storm
      ABSTRACTION
      Huge amount of
      Raw Sensor Data
      Features representing Real-World events
      8
    • 9. x
      Types of Abstractions
      Summarization across Thematic Dimension
      Summarization over the Temporal Dimension
      9
    • 10. x
      Types of Abstractions
      Summarization across Thematic Dimension
      Select
      Join
      Background Knowledge
      Analyze
      Features representing Real-World Events
      10
    • 11. x
      An example problem?
      11
      “Find the sequence of weather events observed near Dayton James Cox Airport between
      Jan 13th and Jan 18th?”
      Spatial
      Thematic
      Temporal
      Technologies required -
      Linked Sensor Data
      Feature Streams
    • 12. x
      Outline
      Introduction
      Architecture
      Linked Sensor Data
      Feature Streams
      Demonstration
      12
    • 13. x
      System Architecture
      13
    • 14. x
      Outline
      Introduction
      Architecture
      Linked Sensor Data
      Feature Streams
      Demonstration
      14
    • 15. 48th ACM Southeast Conference. ACMSE 2010.
      Oxford, Mississippi. April 15-17, 2010.
      Technology1: Linked Sensor Data
      Find the sensor around Dayton James Cox Airport?
      Extract Data for the sensor near Dayton James Cox Airport?
      Harshal Patni, Cory Henson, Amit Sheth, 'Linked Sensor Data,' In: Proceedings of 2010 International Symposium on Collaborative Technologies and Systems (CTS 2010), Chicago, IL, May 17-21, 2010.
    • 16. Sensor Discovery Application
      Weather Station ID
      Current Observations from MesoWest
      Weather Station Coordinates
      Weather Station Phenomena
      MesoWest – Project under Department of Meteorology, University of UTAH
      GeoNames – Geographic dataset
      16
    • 17. What is Linked Sensor Data
      Weather Sensors
      Sensor Dataset
      GPS Sensors
      Satellite Sensors
      Camera Sensors
      17
    • 18. What is Linked Sensor Data
      Recommended best practice for exposing, sharing, and connecting pieces of data, information, and knowledge on the Web using URIs and RDF
      GeoNames Dataset
      RDF – language for representing data on the Web
      locatedNear
      Sensor Dataset
      Publicly Accessible
      18
    • 19. Linked Sensor Data on LOD
      - First Sensor Dataset on LOD
      - Among the largest dataset on LOD
      19
    • 20. zn
      Sensor Datasets
      LinkedSensorDataset
      • RDF Descriptions of ~20,000 weather stations in US
      • 21. Average 5 sensors/weather station
      • 22. Spatial attributes of the weather station
      • 23. Links to locations in Geonames
      LinkedObservationDataset
      • RDF descriptions of Hurricanes and Blizzard observations in US
      • 24. Observations generated by sensors described in LinkedSensorDataset
      20
    • 25. Data Generation Workflow
      O&M2RDFCONVERTER
      21
    • 26. Workflow – Phase 1
      22
    • 27. Workflow – Phase 2
      OGC (Open Geospatial Consortium) standard for encoding sensor observations
      23
    • 28. Workflow – Phase 3
      W3C SSN ontology
      Ontology – formal representation of knowledge by a set of concepts and relationship between those concepts
    • 29. Workflow – Phase 3
      Figure 1: System Components and Architecture
    • 30. Workflow – Phase 4
      Open Source RDF store by OpenLink Software for storing RDF data
      PUBBY Linked Data Front End
    • 31. Summarizing Linked Sensor Data
      Find the sensor around
      Dayton James Cox Airport?
      Extract Data for the sensor?
      Observation
      KB
      Sensor KB
      Location KB
      (Geonames)
      location
      procedure
      location
      location
      procedure
      720F
      Thermometer
      Dayton Airport
    • x
      Outline
      Introduction
      Architecture
      Linked Sensor Data
      Feature Streams
      Demonstration
      28
    • 40. 48th ACM Southeast Conference. ACMSE 2010.
      Oxford, Mississippi. April 15-17, 2010.
      Technology 2: Feature Streams
      What feature is currently being detected by sensor near Dayton Airport?
      Harshal Patni, Cory Henson, Amit Sheth, Pramod Ananthram, ‘From Real Time Sensor Streams to Real Time Feature Streams,' Kno.e.sis Technical Report, January 2011.
    • 41. x
      System Architecture
      Streams Integration based on feature composition
      Integrated Stream Analysis to check if the feature is being detected
      30
    • 42. x
      Feature Composition
      31
    • 43. x
      System Capability
      32
    • 44. x
      System Feature Integration
      SELECT
      JOIN
      33
    • 45. x
      System Architecture
      Integrated Stream Analysis to check if the feature is being detected
      34
    • 46. x
      Feature Definition
      RainStorm = HighWindSpeed(above 35mph) AND
      Rain Precipitation AND
      Temperature(greater than 32F)
      SPARQL query for RainStorm
      Temperature
      Rain Precipitation
      WindSpeed
      35
      Rain Storm NOAA definition
    • 47. x
      Feature Analysis
      RDF Feature Stream
      36
    • 48. x
      Revisiting Abstractions
      Summarization across Thematic Dimension
      Select
      Join
      Background Knowledge
      Analyze
      Features representing Real-World Events
      37
    • 49. Summarizing Feature Streams
      Feature Streams
      KB
      Find sequence of events near Dayton Airport?
      Observation
      KB
      Sensor KB
      Location KB
      (Geonames)
      procedure
      location
      location
      procedure
      720F
      Thermometer
      Dayton Airport
    • x
      Answering the query
      39
      “Find the sequence of weather events observed near Dayton James Cox Airport between
      Jan 13th and Jan 18th?”
      Feature Streams
      Linked Sensor Data
    • 58. x
      Outline
      Introduction
      Architecture
      Linked Sensor Data
      Feature Streams
      Demonstration
      40
    • 59. x
      Demo
      41
      Feature Streams Demo
      http://knoesis1.wright.edu/EventStreams
    • 60. x
      Evaluation
      • Data Used: Nevada Blizzard (April 1st – April 6th)
      70% Data clear
      30% Feature Observed
      42
    • 61. WORKSHOP PAPERS
      Harshal Patni, Satya S. Sahoo, Cory Henson, Amit Sheth, Provenance Aware Linked Sensor Data, 2nd Workshop on Trust and Privacy on Social and Semantic Web,Co-Located with ESWC, Heraklion Greece, May 30th - June 3rd 2010
      Harshal Patni, Cory Henson, Amit Sheth, Linked Sensor Data, In: Proceedings of 2010 International Symposium on Collaborative Technologies and Systems (CTS 2010), Chicago, IL, May 17-21, 2010
      TECHNICAL REPORT
      Harshal Patni, Cory Henson, Amit Sheth, and Pramod Ananthram. From Real Time Sensor Streams to Real Time Feature Streams, Kno.e.sis Center Technical Report, December 2009
      Joshua Pschorr, Cory Henson, Harshal Patni, and Amit Sheth. Sensor Discovery on Linked Data, Kno.e.sis Center Technical Report, December 2009
      JOURNAL PAPER (In Progress)
      Semantic Sensor Web: Design and Application towards weaving a meaningful sensor web
      Publications
      43
    • 62. Thank You Committee
      44
    • 63. Thank You
      Semantic Sensor Web
      45
    • 64. Demos, Papers and more at: http://wiki.knoesis.org/index.php/SSW
      Semantic Sensor Web @ Kno.e.sis
      QUESTIONS
      46

    ×