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Real Time Semantic Analysis of Streaming Sensor Data
 

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

Real Time Semantic Analysis of Streaming Sensor Data Real Time Semantic Analysis of Streaming Sensor Data Presentation Transcript

  • 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
  • x
    Properties of Streaming Data
    Huge Volume
    Rapid
    Continuous
    Information Overload!!
    Heterogeneous
    3
  • 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
  • 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
  • x
    Outline
    Introduction
    Architecture
    Linked Sensor Data
    Feature Streams
    Demonstration
    6
  • x
    Domain
    Weather Domain
    Features
    Blizzard
    Flurry
    RainStorm
    RainShower
    7
  • x
    Explaining the title
    Background Knowledge
    Blizzard
    Rain Storm
    ABSTRACTION
    Huge amount of
    Raw Sensor Data
    Features representing Real-World events
    8
  • x
    Types of Abstractions
    Summarization across Thematic Dimension
    Summarization over the Temporal Dimension
    9
  • x
    Types of Abstractions
    Summarization across Thematic Dimension
    Select
    Join
    Background Knowledge
    Analyze
    Features representing Real-World Events
    10
  • 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
  • x
    Outline
    Introduction
    Architecture
    Linked Sensor Data
    Feature Streams
    Demonstration
    12
  • x
    System Architecture
    13
  • x
    Outline
    Introduction
    Architecture
    Linked Sensor Data
    Feature Streams
    Demonstration
    14
  • 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.
  • 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
  • What is Linked Sensor Data
    Weather Sensors
    Sensor Dataset
    GPS Sensors
    Satellite Sensors
    Camera Sensors
    17
  • 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
  • Linked Sensor Data on LOD
    - First Sensor Dataset on LOD
    - Among the largest dataset on LOD
    19
  • zn
    Sensor Datasets
    LinkedSensorDataset
    • RDF Descriptions of ~20,000 weather stations in US
    • Average 5 sensors/weather station
    • Spatial attributes of the weather station
    • Links to locations in Geonames
    LinkedObservationDataset
    • RDF descriptions of Hurricanes and Blizzard observations in US
    • Observations generated by sensors described in LinkedSensorDataset
    20
  • Data Generation Workflow
    O&M2RDFCONVERTER
    21
  • Workflow – Phase 1
    22
  • Workflow – Phase 2
    OGC (Open Geospatial Consortium) standard for encoding sensor observations
    23
  • Workflow – Phase 3
    W3C SSN ontology
    Ontology – formal representation of knowledge by a set of concepts and relationship between those concepts
  • Workflow – Phase 3
    Figure 1: System Components and Architecture
  • Workflow – Phase 4
    Open Source RDF store by OpenLink Software for storing RDF data
    PUBBY Linked Data Front End
  • 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
    • ~2 billion triples
    • MesoWest
    • Static + Dynamic
    • 20,000+ systems
    • MesoWest
    • ~Static
    • 230,000+ locations
    • Geonames
    • ~Static
  • x
    Outline
    Introduction
    Architecture
    Linked Sensor Data
    Feature Streams
    Demonstration
    28
  • 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.
  • x
    System Architecture
    Streams Integration based on feature composition
    Integrated Stream Analysis to check if the feature is being detected
    30
  • x
    Feature Composition
    31
  • x
    System Capability
    32
  • x
    System Feature Integration
    SELECT
    JOIN
    33
  • x
    System Architecture
    Integrated Stream Analysis to check if the feature is being detected
    34
  • 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
  • x
    Feature Analysis
    RDF Feature Stream
    36
  • x
    Revisiting Abstractions
    Summarization across Thematic Dimension
    Select
    Join
    Background Knowledge
    Analyze
    Features representing Real-World Events
    37
  • 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
    • ~2 billion triples
    • MesoWest
    • Static + Dynamic
    • 20,000+ systems
    • MesoWest
    • ~Static
    • 230,000+ locations
    • Geonames
    • ~Static
  • 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
  • x
    Outline
    Introduction
    Architecture
    Linked Sensor Data
    Feature Streams
    Demonstration
    40
  • x
    Demo
    41
    Feature Streams Demo
    http://knoesis1.wright.edu/EventStreams
  • x
    Evaluation
    • Data Used: Nevada Blizzard (April 1st – April 6th)
    70% Data clear
    30% Feature Observed
    42
  • 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
  • Thank You Committee
    44
  • Thank You
    Semantic Sensor Web
    45
  • Demos, Papers and more at: http://wiki.knoesis.org/index.php/SSW
    Semantic Sensor Web @ Kno.e.sis
    QUESTIONS
    46