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INTEROPERABLE &EFFICIENT
EUGENE SIOW
THANASSIS TIROPANIS
WENDY HALL
LINKED DATA FOR THE INTERNET OF THINGS
“NOTHING ISWORKING.”
“UNPLUGWHAT?”
“EVERYTHING IS INSIDE THE WALLS.”
DEVICES& DATA:SECURITY,PRIVACY,LOCALITY?
“The Internet of Things is currently beset by product silos.”
W3C Web of Things I...
DATA OWNERSHIP & PRIVACY WITH
LIGHTWEIGHT COMPUTERS
A Smart Home Scenario implementing a Personal IoT Repository
Smart Hom...
DATA LOCALITY WITH
LIGHTWEIGHT COMPUTERS
A Distributed Meteorological Scenario, minimising cloud dependencyfor Storage and...
INTRODUCING
LINKED DATA
FOR INTEROPERABILITY
URI andontologies
Establish commondata structures& References
http://thing.io...
THE SHAPE OFIOT TIME-SERIES DATA
{
timestamp : 1467673132,
temperature : {
max: 22.0,
min: 15.0,
current: 17.0,
error: {
p...
THE SHAPE OFIOT TIME-SERIES DATA
20kUNIQUE DEVICES
dweet.io
18.5kNON-EMPTY
SCHEMATA
92.3%
18k
99.5%
FLAT SCHEMATA
92
0.5%
...
OPTIMISINGFOR
TIME-SERIES
DATA
THING
TEMPERATURE OBS
HUMIDITY OBS
WIND SPEED OBS
13.0
2016-01-0106:00:00
CELCIUS
93.0
2016...
THING
TEMPERATURE OBS
HUMIDITY OBS
WIND SPEED OBS
13.0
LOCATION
produces
produces
located
produces
has value
THING
THING
T...
SHARE COLUMN HEADERS
NO JOINS WITHIN ROWS
‘JUST IN TIME’ METADATA
OUR
APPROACH
OPTIMISINGFOR
TIME-SERIES
DATA
THING
TEMPER...
DESIGNING OURENGINE
THING
TEMPERATURE OBS WIND SPEED OBS
CELCIUS PERCENT MPH
LOCATION
produces
located
HUMIDITY OBS
unit
T...
DESIGNING OURENGINE
THING
TEMPERATURE OBS WIND SPEED OBS
CELCIUS PERCENT MPH
LOCATION
produces
located
HUMIDITY OBS
unit
T...
DESIGNING OURENGINE
THING
TEMPERATURE OBS
CELCIUS PERCENT
produces
loc
HUMIDITY OBS
unit
TEMPERATURE HUMID
13.0 93.0
TIME
...
DESIGNING OURENGINE
TEMPERATURE OBS
CELCIUS
TEMPERATURE
13.0
TABLE1.TEMPERATURE
has value
MAX( )?TEMPERATURESELECT
?OBS TE...
SPARQL
SQL
DESIGNING OURENGINE
MAX( )?TEMPERATURESELECT
?OBS TEMPERATURE OBSa
has value?OBS ?TEMPERATURE
has unit?OBS ?uom...
BENCHMARKS & IOT Scenarios
Meteorological
SYSTEM
~20,000 Stations
100 – 300k triples
Wind, Rainfall, etc.
10 SRBench Queri...
STORAGESIZE
3ook
HurricaneIke
1ook
NEVADABLIZZARD
3ok
SMARTHOME
OUR APPROACH(s2S)
NATIVE STORE (TDB)
x15
x68
x112
Get the rainfall observed in a particular
hour from all stations01
02
SRBENCH QUERYRESULTS
Q01 with an optional clause
on ...
03
04
05
Detect if a hurricane has been observed
X3.4
Get the average wind speed at the stations
where the air temperature...
06
07
08
Get the stations with extremely low visibility
X6
Detect stations that are recently broken
x14
X5.6
Get the daily...
09
10
Get the daily average wind force and direction
observed by the sensor at a given location
Get the locations where a ...
Temperature aggregated by hour on a
specified day01
02
SMARTHOME QUERYRESULTS
Minimum and maximum temperature
each day for...
03
04
Energy Usage Per Room By Day
Diagnose unattended appliances consuming
energy with no motion in room
x69
Our Approach...
WHY IS
OUR APPROACH
FASTER THAN NATIVE RDF?
FASTER AGGREGATIONS ON LESS RESOURCES
CAN SPECIFCALLYBUILD INDEXES FOR FAST RA...
RELATEDWORK
Rodriguez-Muro,M., Rezk, M. (2014) Efficient SPARQL-to-SQLwith R2RML mappings.Web Semantics: Science, Services...
“Until they become conscious they will never rebel and until after
they have rebelled they cannot become conscious.”
DATA ...
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Interoperable & Efficient: Linked Data for the Internet of Things (INSCI16)

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Presentation at The 3rd International Conference for Internet Science (INSCI16) in Florence, Italy.

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

  1. 1. INTEROPERABLE &EFFICIENT EUGENE SIOW THANASSIS TIROPANIS WENDY HALL LINKED DATA FOR THE INTERNET OF THINGS
  2. 2. “NOTHING ISWORKING.” “UNPLUGWHAT?” “EVERYTHING IS INSIDE THE WALLS.”
  3. 3. DEVICES& DATA:SECURITY,PRIVACY,LOCALITY? “The Internet of Things is currently beset by product silos.” W3C Web of Things Interest Group CURRENT STATE OFTHE INTERNET OF THINGS PRODUCT &DATASILOS DEPENDENCYON THE CLOUD PERFORMANCE OF APPLICATIONS &ANALYTICS
  4. 4. DATA OWNERSHIP & PRIVACY WITH LIGHTWEIGHT COMPUTERS A Smart Home Scenario implementing a Personal IoT Repository Smart Home Dashboard Personal IoT Repository Environmental Sensors Energy Meters Data Stream Energy Saving Analytics Stream & Historical Queries Motion Sensors Data ownership Own andstore your dataathome Less Cloud ENCRYPTION BETTERPERFORMANCE SPECIFIC POLICIES/CONTROL ONLINE/OFFLINE, TRUST, ACCESS CONTROL
  5. 5. DATA LOCALITY WITH LIGHTWEIGHT COMPUTERS A Distributed Meteorological Scenario, minimising cloud dependencyfor Storage and Processing Irrigation Application Soil Moisture Analytics Environmental Sensors Lightweight ComputerHub Data Stream Weather Data State Inclement Weather Planning Application National Disaster Monitoring Application Cloud
  6. 6. INTRODUCING LINKED DATA FOR INTEROPERABILITY URI andontologies Establish commondata structures& References http://thing.io/1 is a http://ont/weather_sensor CLASS produces http://thing.io/obs/1 http://ont/temp_observation is a 13.0 has value CLASS ℃ unit ENABLES RICH METADATA what,where, WHEN,HOW of DATA located at http://thing.io/loc/1 latitude longitude -1.4150.9 PERFORMANCE CHALLENGES STORES DON’T SCALE & PERFORM WELLON WEB YET Buil-Aranda, C., Hogan, A.: SPARQL Web-Querying Infrastructure: Ready for Action? ISWC 2013
  7. 7. THE SHAPE OFIOT 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, } 1 2 3 4 5 WIDTH
  8. 8. THE SHAPE OFIOT TIME-SERIES DATA 20kUNIQUE DEVICES dweet.io 18.5kNON-EMPTY SCHEMATA 92.3% 18k 99.5% FLAT SCHEMATA 92 0.5% COMPLEX SCHEMATA 1 2,3 4 5 6+ Width
  9. 9. OPTIMISINGFOR TIME-SERIES DATA THING TEMPERATURE OBS HUMIDITY OBS WIND SPEED OBS 13.0 2016-01-0106:00:00 CELCIUS 93.0 2016-01-0106:00:00 PERCENT 10.5 2016-01-0106:00:00 MPH LOCATION produces produces located produces has value unit time RDF GRAPH
  10. 10. THING TEMPERATURE OBS HUMIDITY OBS WIND SPEED OBS 13.0 LOCATION produces produces located produces has value THING THING THING TEMPERATURE OBS timeTEMPERATURE OBS 2016-01-0106:00:00 unitTEMPERATURE OBS celcius 93.0has valueHUMIDITY OBS timeHUMIDITY OBS 2016-01-0106:00:00 unitHUMIDITY OBS PERCENT 10.5has valueWIND SPEED OBS timeWIND SPEED OBS 2016-01-0106:00:00 unitWIND SPEED OBS MPH OPTIMISINGFOR TIME-SERIES DATA RDF TRIPLES
  11. 11. SHARE COLUMN HEADERS NO JOINS WITHIN ROWS ‘JUST IN TIME’ METADATA OUR APPROACH OPTIMISINGFOR TIME-SERIES DATA 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
  12. 12. DESIGNING OURENGINE THING TEMPERATURE OBS WIND SPEED OBS 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
  13. 13. DESIGNING OURENGINE THING TEMPERATURE OBS WIND SPEED OBS 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
  14. 14. DESIGNING OURENGINE THING TEMPERATURE OBS CELCIUS PERCENT produces loc HUMIDITY OBS unit TEMPERATURE HUMID 13.0 93.0 TIME 2016-01-01 06:00:00 TABLE1.TEMPERATURE has value has va TABLE1.H MAX( )?TEMPERATURESELECT ?OBS TEMPERATURE OBSa has value?OBS ?TEMPERATURE has unit?OBS ?uom { } SELECT MAX( )?TEMPERATURE ?OBS TEMPERATURE OBSa has value?OBS ?TEMPERATURE has unit?OBS ?uom
  15. 15. DESIGNING OURENGINE TEMPERATURE OBS CELCIUS TEMPERATURE 13.0 TABLE1.TEMPERATURE has value MAX( )?TEMPERATURESELECT ?OBS TEMPERATURE OBSa has value?OBS ?TEMPERATURE has unit?OBS ?uom { } SELECT MAX( )?TEMPERATURE ?OBS TEMPERATURE OBSa has value?OBS ?TEMPERATURE has unit?OBS ?uom
  16. 16. SPARQL SQL DESIGNING OURENGINE MAX( )?TEMPERATURESELECT ?OBS TEMPERATURE OBSa has value?OBS ?TEMPERATURE has unit?OBS ?uom { } SELECT MAX( )?TEMPERATURE ?OBS TEMPERATURE OBSa has value?OBS ?TEMPERATURE has unit?OBS ?uom SELECT MAX( )?TEMPERATURE ?OBS ?TEMPERATURE ?uom TABLE1.TEMPERATURE CELCIUSNODE_TEMP SELECT MAX( )TEMPERATURE FROM TABLE1
  17. 17. BENCHMARKS & IOT Scenarios 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 SMARTHOME ANALYTICS LIGHTWEIGHT COMPUTER COMPUTER/SERVER CLUSTER DEVICE SENSOR Compute&Storage LevelofDistribution github.com/eugenesiow/sparql2sql
  18. 18. STORAGESIZE 3ook HurricaneIke 1ook NEVADABLIZZARD 3ok SMARTHOME OUR APPROACH(s2S) NATIVE STORE (TDB) x15 x68 x112
  19. 19. Get the rainfall observed in a particular hour from all stations01 02 SRBENCH QUERYRESULTS Q01 with an optional clause on unit of measure OUR APPROACH(S2S) NATIVE STORE (TDB) x4.6 x4
  20. 20. 03 04 05 Detect if a hurricane has been observed X3.4 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) X2.7 Detect if a station is observing a blizzard
  21. 21. 06 07 08 Get the stations with extremely low visibility X6 Detect stations that are recently broken x14 X5.6 Get the daily minimal and maximal air temperature observed by the sensor at a given location
  22. 22. 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)
  23. 23. Temperature aggregated by hour on a specified day01 02 SMARTHOME QUERYRESULTS Minimum and maximum temperature each day for a particular month OUR APPROACH(S2S) NATIVE STORE (TDB) x29 x9
  24. 24. 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.
  25. 25. WHY IS OUR APPROACH FASTER THAN NATIVE RDF? FASTER AGGREGATIONS ON LESS RESOURCES CAN SPECIFCALLYBUILD INDEXES FOR FAST RANGE QUERIES EFFICIENT SQL QUERIES OPTIMISE FLAT & WIDE DATA ACCESS REDUCE JOINS BETWEENSUBGRAPHSON THESAME ROW COLLAPSE INTERMEDIATE NODES REDUCE JOINS W/BLANK OR FAUXNODES IN MAPPINGS
  26. 26. RELATEDWORK Rodriguez-Muro,M., Rezk, M. (2014) Efficient SPARQL-to-SQLwith 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 Experiencesof R2RMLbased SPARQL to SQL Query Translation using Morph. Proceedings of the 23rd International Conference on World Wide Web pp. 479–489 GENERALONTOLOGY BASEDDATA ACCESSENGINES 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/piotre Siow, 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)
  27. 27. “Until they become conscious they will never rebel and until after they have rebelled they cannot become conscious.” DATA OWNERSHIP & DATA LOCALITY DISTRIBUTED LIGHTWEIGHTCOMPUTERSFOR STORAGE AND PROCESSING IN THEIOT 1984 by George Orwell LINKED DATA FORINTEROPERABILITY A rich model todescribe thingsand integrateconnected thing’sdata NOVELTIERED LINKED DATA STORE FROM 3to3orders of magnitudeperformance improvement @eugene_siow

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