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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.More at: http://wiki.knoesis.org/index.php/SSWDissertation Advisor: Prof. Amit Sheth
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xWEB DATA evolved over time
2
Static Document and files
Real-Time Sensor, Social, Multi-media
data
Dynamic User Generated Content
1990’s
2000’s
2010’s
xProperties of Streaming Data
3
Continuous
Rapid
Huge Volume
Heterogeneous
Information Overload!!
xSome Statistics
4
“Sensors Networks will produce 10-20 times the amount of generated by social media in the next few years” - GigaOmni Media
Solution - “Meaningfully summarize this data”“More data has been created in the last three years than in all the past 40,000 years”- Teradata
“A cross-country flight from New York to Los Angeles on a Boeing 737 plane generates a massive 240 terabytes of data”- GigaOmni Media
From Sensor Streams to Feature Streams in Real Time
Harshal PatniOhio Center of Excellence in Knowledge enabled Computing (Kno.e.sis)
Wright State University, Dayton, OH
Part of Semantic Sensor Web @ Kno.e.sis
48th ACM Southeast Conference. ACMSE 2010. Oxford, Mississippi. April 15-17, 2010.
xOutline
6
1. Introduction 2. Architecture3. Linked Sensor Data4. Feature Streams5. Demonstration
xDomain
7
Weather Domain Features
Blizzard Flurry
RainShower RainStorm
xExplaining the title
8
Huge amount of Raw Sensor Data
Background Knowledge
Features representing Real-World events
ABSTRACTION
BlizzardRain Storm
xTypes of Abstractions
9
Sum
mar
izat
ion
over
the
Tem
pora
l Dim
ensi
on
Summarization across Thematic Dimension
xTypes of Abstractions
10
Summarization across Thematic Dimension
Select
Join
Analyze
Background Knowledge
Features representing Real-World Events
xAn example problem?
11
“Find the sequence of weather events observed near Dayton James Cox Airport between Jan 13th and Jan 18th?”
Thematic Spatial Temporal
Technologies required - 1. Linked Sensor Data2. Feature Streams
xOutline
12
1. Introduction 2.Architecture3.Linked Sensor Data4.Feature Streams5. Demonstration
xSystem Architecture
13
xOutline
14
1. Introduction 2.Architecture3.Linked Sensor Data4.Feature Streams5. Demonstration
Technology1: Linked Sensor Data
48th ACM Southeast Conference. ACMSE 2010. Oxford, Mississippi. April 15-17, 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.
1. Find the sensor around Dayton James Cox Airport?2. Extract Data for the sensor near Dayton James Cox Airport?
Sensor Discovery Application
16
Weather Station ID
Weather Station Coordinates
Weather Station Phenomena
Current Observations from MesoWest
MesoWest – Project under Department of Meteorology, University of UTAH
GeoNames – Geographic dataset
What is Linked Sensor Data
17
Weather Sensors
Camera SensorsSatellite Sensors
GPS SensorsSensor Dataset
What is Linked Sensor Data
18
Sensor Dataset
Publicly Accessible
Recommended best practice for exposing, sharing, and connecting pieces of data, information, and knowledge on the Web using URIs and RDF
RDF – language for representing data on the Web
loca
tedNea
r
GeoNames Dataset
Linked Sensor Data on LOD
19
- First Sensor Dataset on LOD - Among the largest dataset on LOD
znSensor Datasets
20
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
MesoWest Service Data
OGC Observation and Measurement
(O&M)RDF Instance Virtuoso RDF store
Data Generation Workflow
21
O&M2RDFCONVERTER
Workflow – Phase 1
22
MesoWest Service Data
OGC Observation and
Measurement (O&M)
RDF Instance Virtuoso RDF store
MesoWest Service Data
OGC Observation and
Measurement (O&M)
RDF Instance Sesame RDF store
Workflow – Phase 2
23
OGC (Open Geospatial Consortium) standard for encoding sensor observations
Workflow – Phase 3
MesoWest Service Data
OGC Observation and
Measurement (O&M)
RDF Instance Virtuoso RDF store
Ontology – formal representation of knowledge by a set of concepts and relationship between those concepts
W3C SSN ontology
Figure 1: System Components and Architecture
Workflow – Phase 3
MesoWest Service Data
OGC Observation and
Measurement (O&M)
RDF Instance Virtuoso RDF store
Workflow – Phase 4
MesoWest Service Data
OGC Observation and
Measurement (O&M)
RDF Instance Virtuoso RDF store
Open Source RDF store by OpenLink Software for storing RDF data
PUBBY Linked Data Front End
Summarizing Linked Sensor Data
ObservationKB
Sensor KB Location KB(Geonames)
procedure locationlocation
procedure location720F Thermometer Dayton Airport
• ~2 billion triples
• MesoWest
• Static + Dynamic
• 20,000+ systems
• MesoWest
• ~Static
• 230,000+ locations
• Geonames
• ~Static
Find the sensor around Dayton James Cox Airport?
Extract Data for the sensor?
xOutline
28
1. Introduction 2.Architecture3.Linked Sensor Data4.Feature Streams5. Demonstration
Technology 2: Feature Streams
48th ACM Southeast Conference. ACMSE 2010. Oxford, Mississippi. April 15-17, 2010.
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.
1. What feature is currently being detected by sensor near Dayton Airport?
xSystem Architecture
30
Streams Integration based on feature composition
Integrated Stream Analysis to check if the feature is being detected
xFeature Composition
31
xSystem Capability
32
xSystem Feature Integration
33
SELECT
JOIN
xSystem Architecture
34
Integrated Stream Analysis to check if the feature is being detected
xFeature Definition
35
• Rain Storm NOAA definitionRainStorm = HighWindSpeed(above 35mph) AND
Rain Precipitation AND Temperature(greater than 32F)
SPARQL query for RainStorm
Temperature
Rain Precipitation
WindSpeed
xFeature Analysis
36
RDF Feature Stream
xRevisiting Abstractions
37
Summarization across Thematic Dimension
Select
Join
Analyze
Background Knowledge
Features representing Real-World Events
Summarizing Feature Streams
ObservationKB
Sensor KB Location KB(Geonames)
procedurelocation
procedure location720F Thermometer Dayton Airport
• ~2 billion triples
• MesoWest
• Static + Dynamic
• 20,000+ systems
• MesoWest
• ~Static
• 230,000+ locations
• Geonames
• ~Static
Feature StreamsKB
Find sequence of events near Dayton Airport?
xAnswering the query
39
“Find the sequence of weather events observed near Dayton James Cox Airport between Jan 13th and Jan 18th?”
Linked Sensor Data Feature Streams
xOutline
40
1. Introduction 2.Architecture3.Linked Sensor Data4.Feature Streams5. Demonstration
xDemo
41
• Feature Streams Demo
– http://knoesis1.wright.edu/EventStreams
xEvaluation
42
• Data Used: Nevada Blizzard (April 1st – April 6th)
70% Data clear
30% Feature Observed
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
Semantic Sensor Web
Thank You
45
Demos, Papers and more at: http://wiki.knoesis.org/index.php/SSW
Semantic Sensor Web @ Kno.e.sis
QUESTIONS
46