Interoperable & Efficient: Linked Data for the Internet of Things (INSCI16)

INTEROPERABLE & EFFICIENT

EUGENE SIOWTHANASSIS TIROPANISWENDY HALL

LINKED DATA FOR THE INTERNET OF THINGS

UNIVERSITY OF SOUTHAMPTON

“NOTHING IS WORKING.”

“UNPLUG WHAT?”

“EVERYTHING IS INSIDE THE WALLS.”

DEVICES & DATA: SECURITY, PRIVACY, LOCALITY?

“The Internet of Things is currently beset by product silos.” W3C Web of Things Interest Group

CURRENT STATE OF THE

INTERNET OF THINGSPRODUCT & DATA SILOS

DEPENDENCY ON THE CLOUD

PERFORMANCE OF APPLICATIONS & ANALYTICS

DATA OWNERSHIP & PRIVACY WITH

LIGHTWEIGHT COMPUTERSA Smart Home Scenario implementing a Personal IoT Repository

Smart Home Dashboard

Personal IoT Repository

Environmental Sensors

Energy Meters

Data Stream

Energy SavingAnalytics

Stream & Historical Queries

Motion Sensors

Data ownershipOwn and store your data at home

Less Cloud ENCRYPTIONBETTER PERFORMANCE

SPECIFIC POLICIES/CONTROLONLINE/OFFLINE, TRUST, ACCESS CONTROL

DATA LOCALITY WITH

LIGHTWEIGHT COMPUTERSA Distributed Meteorological Scenario, minimising cloud dependency for Storage and Processing

Irrigation Application

Soil Moisture Analytics

Environmental Sensors

Lightweight Computer Hub

Data Stream

WeatherData

State InclementWeather PlanningApplication

National DisasterMonitoring Application

Cloud

INTRODUCING

LINKED DATAFOR INTEROPERABILITY

URI and ontologiesEstablish common data structures & References

http://thing.io/1is a

http://ont/weather_sensorCLASS

produceshttp://thing.io/obs/1

http://ont/temp_observation

is a

13.0

has value

CLASS

℃

unit

ENABLES RICH METADATAwhat, where, WHEN, HOW of DATA

located athttp://thing.io/loc/1

latitude longitude

-1.4150.9

PERFORMANCE CHALLENGESSTORES DON’T SCALE & PERFORM WELL ON WEB YETBuil-Aranda, C., Hogan, A.: SPARQL Web-Querying Infrastructure: Ready for Action? ISWC 2013

THE SHAPE OF IOT TIME-SERIES DATA

{timestamp : 1467673132,temperature : {

max: 22.0,min: 15.0,current: 17.0,error: {

percentage: 5.0}

}}

FLAT{

timestamp : 1467673132,temperature : 32.0,wind_speed : 10.5,pressure : 1016

}

COMPLEX{

timestamp : 1467673132,temperature : 32.0,wind_speed : 10.5,pressure : 1016,precipitation: 0,humidity: 93.0,

}

12345

WIDTH

THE SHAPE OF IOT TIME-SERIES DATA

20kUNIQUE DEVICES

dweet.io

18.5kNON-EMPTYSCHEMATA

92.3%

18k99.5%

FLAT SCHEMATA

920.5%

COMPLEX SCHEMATA

1

2,3

4

5

6+

Width

OPTIMISING FOR

TIME-SERIESDATA

THING

TEMPERATURE OBS

HUMIDITY OBS

WIND SPEED OBS

13.0

2016-01-01 06:00:00

CELCIUS

93.0

2016-01-01 06:00:00

PERCENT

10.5

2016-01-01 06:00:00

MPH

LOCATION

produces

produces

located

produces

has value

unit

time

RDF GRAPH

THING

TEMPERATURE OBS

HUMIDITY OBS

WIND SPEED OBS

13.0

LOCATION

produces

produces

located

produces

has value

THING

THING

THING

TEMPERATURE OBS

timeTEMPERATURE OBS 2016-01-01 06:00:00

unitTEMPERATURE OBS celcius

93.0has valueHUMIDITY OBS

timeHUMIDITY OBS 2016-01-01 06:00:00

unitHUMIDITY OBS PERCENT

10.5has valueWIND SPEED OBS

timeWIND SPEED OBS 2016-01-01 06:00:00

unitWIND SPEED OBS MPH

OPTIMISING FOR

TIME-SERIESDATA

RDF TRIPLES

SHARE COLUMN HEADERS

NO JOINS WITHIN ROWS

‘JUST IN TIME’ METADATA

OURAPPROACH

OPTIMISING FOR

TIME-SERIESDATA

THING

TEMPERATURE OBS WIND SPEED OBS

CELCIUS PERCENT MPH

LOCATION

produces

located

HUMIDITY OBS

unit

TEMPERATURE HUMIDITY WIND SPEED

13.0 93.0 10.5

TIME

2016-01-01 06:00:00

DESIGNING OUR ENGINE

THING


CELCIUS PERCENT MPH

LOCATION

produces

located

HUMIDITY OBS

unit

TEMPERATURE HUMIDITY WINDSPEED

13.0 93.0 10.5

TIME

2016-01-01 06:00:00

Table1

TABLE1.TEMPERATURE

has value has value

TABLE1.HUMIDITY

has value

TABLE1.WINDSPEED


THING


CELCIUS PERCENT MPH

LOCATION

produces

located

HUMIDITY OBS

unit


13.0 93.0 10.5

TIME

2016-01-01 06:00:00

Table1

TABLE1.TEMPERATURE

has value has value

TABLE1.HUMIDITY

has value

TABLE1.WINDSPEED


THING


CELCIUS PERCENT MPH

LOCATION

produces

located

HUMIDITY OBS

unit


13.0 93.0 10.5

TIME

2016-01-01 06:00:00

Table1

TABLE1.TEMPERATURE

has value has value

TABLE1.HUMIDITY

has value

TABLE1.WINDSPEED

MAX( )?TEMPERATURESELECT

?OBS TEMPERATURE OBSa

has value?OBS ?TEMPERATURE

has unit?OBS ?uom

{

}

SELECT

MAX( )?TEMPERATURE



has unit?OBS ?uom


TEMPERATURE OBS

CELCIUS

TEMPERATURE

13.0 10.5

TABLE1.TEMPERATURE

has value




has unit?OBS ?uom

{

}

SELECT

MAX( )?TEMPERATURE



has unit?OBS ?uom

SPARQL

SQL





has unit?OBS ?uom

{

}

SELECT

MAX( )?TEMPERATURE



has unit?OBS ?uom

SELECT

MAX( )?TEMPERATURE

?OBS ?TEMPERATURE ?uom

TABLE1.TEMPERATURE CELCIUSNODE_TEMP

SELECT MAX( )TEMPERATURE FROM TABLE1

BENCHMARKS & IOT Scenarios

17

Meteorological SYSTEM

~20,000 Stations

100 – 300k triples

Wind, Rainfall, etc.

10 SRBench Queries

ANALYTICS HUB

STATION HUBSTATION HUB

Weather SENSORS

Weather SENSORS

Weather SENSORS

3 months, 1 home

~30k triples

Motion, energy, env

4 Analytics Queries

PERSONAL STORE

Weather SENSORS

Weather SENSORS

DEVICES W/SENSORS

SMART HOMEANALYTICS

LIGHTWEIGHT COMPUTER

COMPUTER/SERVER

CLUSTER

DEVICE

SENSORCo

mpu

te &

Sto

rage

Level of Distribution

github.com/eugenesiow/sparql2sql

STORAGE SIZE

3ookHurricane Ike

1ookNEVADA BLIZZARD

3okSMART HOME

OUR APPROACH (s2S)

NATIVE STORE (TDB)

x15

x68

x112

Get the rainfall observed in a particular hour from all stations01

02

SRBENCH QUERY RESULTS

Q01 with an optional clause on unit of measure

OUR APPROACH (S2S)

NATIVE STORE (TDB)

x4.6

x4

03

04

05

Detect if a hurricane has been observedX3.4

Get the average wind speed at the stations where the air temperature is >32

x88

Join between wind observation and temperature observation subtrees time-consuming in low resource

environment (Raspberry Pi)

X2.7Detect if a station is observing a blizzard

06

07

08

Get the stations with extremely low visibilityX6

Detect stations that are recently brokenx14

X5.6

Get the daily minimal and maximal air temperature observed by the sensor at a given location

09

10

Get the daily average wind force and direction observed by the sensor at a given location

Get the locations where a heavy snowfall has been observed

x305

X7

Our Approach (s2s) is shown to be faster on all queries in the Distributed Meteorological System

Join between wind force and wind direction observation subtrees is time-consuming in low resource

environment (Raspberry Pi)

Temperature aggregated by hour on a specified day01

02

SMART HOME QUERY RESULTS

Minimum and maximum temperature each day for a particular month

OUR APPROACH (S2S)

NATIVE STORE (TDB)

x29

x9

03

04

Energy Usage Per Room By Day

Diagnose unattended appliances consuming energy with no motion in room

x69

Our Approach (s2s) is shown, once again, to be faster on all queries for Smart Home Analytics

x3.6

Involves motion and meter data (much larger set), with space-time aggregations and joins between motion and

meter tables/subgraphs.

Involves meter data (larger set), with space-time aggregations.

WHY IS

OUR APPROACHFASTER THAN NATIVE RDF?

FASTER AGGREGATIONS ON LESS RESOURCESCAN SPECIFCALLY BUILD INDEXES FOR FAST RANGE QUERIES

EFFICIENT SQL QUERIES

OPTIMISE FLAT & WIDE DATA ACCESSREDUCE JOINS BETWEEN SUBGRAPHS ON THE SAME ROW

COLLAPSE INTERMEDIATE NODESREDUCE JOINS W/ BLANK OR FAUX NODES IN MAPPINGS

RELATED WORK

Rodriguez-Muro, M., Rezk, M. (2014) Efficient SPARQL-to-SQL with R2RML mappings. Web Semantics: Science, Services and Agents on the World Wide Web 33, pp. 141–169

-ontop-

morph

sparql2stream

Priyatna, F., Corcho, O., Sequeda, J. (2014) Formalisation and Experiences of R2RMLbased SPARQL to SQL Query Translation using Morph. Proceedings of the 23rd International Conference on World Wide Web pp. 479–489

GENERAL ONTOLOGY BASED DATA ACCESS ENGINES

sparql2sql

Siow, Eugene, Tiropanis, Thanassis and Hall, Wendy (2016) SPARQL-to-SQL on internet of things databases and streams. Proceedings of the 15th International Semantic Web Conference (accepted, to be published)

github.com/eugenesiow/sparql2sql

github.com/eugenesiow/piotreSiow, Eugene, Tiropanis, Thanassis and Hall, Wendy (2016) PIOTRe: Personal IoT Repository. Proceedings of the 15th International Semantic Web Conference P&D (accepted, to be published)

“Until they become conscious they will never rebel and until after they have rebelled they cannot become conscious.”

DATA OWNERSHIP & DATA LOCALITYDISTRIBUTED LIGHTWEIGHT COMPUTERS FOR STORAGE AND PROCESSING IN THE IOT

1984 by George Orwell

LINKED DATA FOR INTEROPERABILITYA rich model to describe things and integrate connected thing’s data

NOVEL TIERED LINKED DATA STOREFROM 3 to 3 orders of magnitude performance improvement

@eugene_siow

Data & Analytics

Interoperable & Efficient: Linked Data for the Internet of Things (INSCI16)