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