Real Time Semantic Analysis of Streaming Sensor Data

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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
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Properties of Streaming Data
Huge Volume
Rapid
Continuous
Information Overload!!
Heterogeneous
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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”
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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

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Outline
Introduction
Architecture
Linked Sensor Data
Feature Streams
Demonstration
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Domain
Weather Domain
Features
Blizzard
Flurry
RainStorm
RainShower
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Explaining the title
Background Knowledge
Blizzard
Rain Storm
ABSTRACTION
Huge amount of
Raw Sensor Data
Features representing Real-World events
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Types of Abstractions
Summarization across Thematic Dimension
Summarization over the Temporal Dimension
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Types of Abstractions
Select
Join
Analyze
Features representing Real-World Events
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An example problem?
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“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

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Outline
Introduction
Architecture
Linked Sensor Data
Feature Streams
Demonstration
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System Architecture
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Outline
Introduction
Architecture
Linked Sensor Data
Feature Streams
Demonstration
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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
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What is Linked Sensor Data
Weather Sensors
Sensor Dataset
GPS Sensors
Satellite Sensors
Camera Sensors
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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
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Linked Sensor Data on LOD
- First Sensor Dataset on LOD
- Among the largest dataset on LOD
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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
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Data Generation Workflow
O&M2RDFCONVERTER
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Workflow – Phase 1
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OGC (Open Geospatial Consortium) standard for encoding sensor observations
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W3C SSN ontology
Ontology – formal representation of knowledge by a set of concepts and relationship between those concepts

Figure 1: System Components and Architecture

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

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Outline
Introduction
Architecture
Linked Sensor Data
Feature Streams
Demonstration
28

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.

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System Architecture
Streams Integration based on feature composition
Integrated Stream Analysis to check if the feature is being detected
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Feature Composition
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System Capability
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System Feature Integration
SELECT
JOIN
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System Architecture
Integrated Stream Analysis to check if the feature is being detected
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Feature Definition
RainStorm = HighWindSpeed(above 35mph) AND
Rain Precipitation AND
Temperature(greater than 32F)
SPARQL query for RainStorm
Temperature
Rain Precipitation
WindSpeed
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Rain Storm NOAA definition

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Feature Analysis
RDF Feature Stream
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Revisiting Abstractions
Select
Join
Analyze
Features representing Real-World Events
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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

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Answering the query
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“Find the sequence of weather events observed near Dayton James Cox Airport between
Jan 13th and Jan 18th?”
Feature Streams
Linked Sensor Data

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Outline
Introduction
Architecture
Linked Sensor Data
Feature Streams
Demonstration
40

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Demo
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Feature Streams Demo
http://knoesis1.wright.edu/EventStreams

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Evaluation
Data Used: Nevada Blizzard (April 1st – April 6th)
70% Data clear
30% Feature Observed
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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
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Thank You Committee
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Thank You
Semantic Sensor Web
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Demos, Papers and more at: http://wiki.knoesis.org/index.php/SSW
Semantic Sensor Web @ Kno.e.sis
QUESTIONS
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Real Time Semantic Analysis of Streaming Sensor Data

by Harshal Patni

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