SPARQL-to-SQL on Internet of Things Databases and Streams
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Transcript of SPARQL-to-SQL on Internet of Things Databases and Streams
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INTERNET OF THINGS DEVICES AND STREAMSSPARQL-TO-SQL ON
EUGENE SIOWTHANASSIS TIROPANISWENDY HALL
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Trekking up 913m
Mt. rokko
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Sensor DATA FROM MULTIPLE SOURCES
“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
LIMITED INTEROPERABILITY OF APPS
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LINKED DATA ON LIGHTWEIGHT COMPUTERS APPL
E
GOOG
LE
HONE
YWEL
L
CISC
O
HUAW
EI
GENE
RAL E
LECT
RIC
IBM
AMAZ
ON
INTE
L
MICR
OSOF
T
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INTRODUCING
LINKED DATAFOR INTEROPERABILITY
URI and ontologiesEstablish common data structures & References
ENABLES RICH METADATAwhat, where, WHEN, HOW of DATA
Integrates with Linked open datacyber, social, physical LOD on web
Barnaghi, P., Wang, W.: Semantics for the Internet of Things: early progress andback to the future. International Journal on Semantic Web and Information Systems (2012)
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
located athttp://thing.io/loc/1
latitude longitude
-1.4150.9
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LINKED DATA ON
DISTRIBUTEDLIGHTWEIGHT COMPUTERSTiered levels of applications between the “Ground” and “Cloud” for Meteorological Data
Irrigation Application
Query on Rainfall
Meteorological Station on Lightweight Computer
Data Stream
Environmental Sensors
GROUND
WeatherData
State InclementWeather PlanningApplication
SRBench: National Weather and Disaster Monitoring
CLOUD Broadcast Queries
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PERFORMANCESTORES DON’T SCALE & PERFORM WELL ON WEB YETBuil-Aranda, C., Hogan, A.: SPARQL Web-Querying Infrastructure: Ready for Action? ISWC 2013
CHALLENGES
RESOURCE CONSTRAINED DEVICES~500mhz CPU, 512mb ram, SD CARD
historical and streaming dataNeed storage and query efficiency ON BOTH
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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
20kUNIQUE DEVICES
dweet.io99.5%FLAT SCHEMATA
0.5%COMPLEX SCHEMATA
1
2,3
4
5
6+
Width
{timestamp : 1467673132,temperature : 32.0,humidity : 10.5,pressure : 1016,light: 120.0,
}
1234
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EFFICIENT QUERIES WITH
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
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OBSERVATION DATA
OBSERVATION METADATA
SENSOR METADATA
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
EFFICIENT QUERIES WITH
TIME-SERIESDATA
RDF TRIPLES
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OURAPPROACH
EFFICIENT QUERIES WITH
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
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SENSOR METADATA
OBSERVATION DATA
OURAPPROACH
EFFICIENT QUERIES WITH
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
OBSERVATION METADATA
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DESIGNING OUR ENGINE
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
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DESIGNING OUR ENGINE
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
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DESIGNING OUR ENGINE
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
MAX( )?TEMPERATURESELECT
?OBS TEMPERATURE OBSa
has value?OBS ?TEMPERATURE
has unit?OBS ?uom
{
}
𝞹
𝞬 (max ( ))?TEMPERATURE
?OBS TEMPERATURE OBSa
has value?OBS ?TEMPERATURE
has unit?OBS ?uom BGP
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DESIGNING OUR ENGINE
TEMPERATURE OBS
CELCIUS
TEMPERATURE
13.0 10.5
TABLE1.TEMPERATURE
has value
MAX( )?TEMPERATURESELECT
?OBS TEMPERATURE OBSa
has value?OBS ?TEMPERATURE
has unit?OBS ?uom
{
}
𝞹
𝞬 (max ( ))?TEMPERATURE
?OBS TEMPERATURE OBSa
has value?OBS ?TEMPERATURE
has unit?OBS ?uom BGP
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DESIGNING OUR ENGINE
MAX( )?TEMPERATURESELECT
?OBS TEMPERATURE OBSa
has value?OBS ?TEMPERATURE
has unit?OBS ?uom
{
}
𝞹
MAX( )?TEMPERATURE
?OBS ?TEMPERATURE ?uom
TABLE1.TEMPERATURE CELCIUSNODE_TEMP
𝞹
𝞬 (max ( ))?TEMPERATURE
?OBS TEMPERATURE OBSa
has value?OBS ?TEMPERATURE
has unit?OBS ?uom BGP
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DESIGNING OUR ENGINE
MAX( )?TEMPERATURESELECT
?OBS TEMPERATURE OBSa
has value?OBS ?TEMPERATURE
has unit?OBS ?uom
{
}
MAX( )TEMPERATURE
𝞹
𝞬 (max ( ))?TEMPERATURE
?OBS TEMPERATURE OBSa
has value?OBS ?TEMPERATURE
has unit?OBS ?uom BGP
FROM TABLE1
𝞹
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SPARQL
DESIGNING OUR ENGINE
MAX( )?TEMPERATURESELECT
?OBS TEMPERATURE OBSa
has value?OBS ?TEMPERATURE
has unit?OBS ?uom
{
}
SQL SELECT MAX( )TEMPERATURE FROM TABLE1
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LOW VISIBILITY STATIONS EXAMPLE
?OBS VISIBILITY OBSa
has value?OBS ?VISIBILITY
has unit?OBS ?uom
}
{
FILTER( ,<10 )?VISIBILITY
UNION
?OBS RAINFALL OBSa
has value?OBS ?RAINFALL
has unit?OBS ?uom
}
{
FILTER( ,>30 )?RAINFALL
?SENSORSELECT {
}
UNION
?OBS SNOWFALL OBSa
has value?OBS ?SNOWFALL
has unit?OBS ?uom}
{
𝞹
?OBS SNOWFALL OBSa
has value?OBS ?SNOWFALL
has unit?OBS ?uom BGP
UNION
UNION
FILTER( ,<10 )?VISIBILITY FILTER( ,>30 )?RAINFALL
?OBS VISIBILITY OBSa
has value?OBS ?VISIBILITY
has unit?OBS ?uom BGP
?OBS RAINFALL OBSa
has value?OBS ?RAINFALL
has unit?OBS ?uom BGP
FILTER( ,TRUE)?SNOWFALL
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LOW VISIBILITY STATIONS EXAMPLE
?OBS VISIBILITY OBSa
has value?OBS ?VISIBILITY
has unit?OBS ?uom
}
{
FILTER( ,<10 )?VISIBILITY
UNION
?OBS RAINFALL OBSa
has value?OBS ?RAINFALL
has unit?OBS ?uom
}
{
FILTER( ,>30 )?RAINFALL
?SENSORSELECT
}
UNION
?OBS SNOWFALL OBSa
has value?OBS ?SNOWFALL
has unit?OBS ?uom}
{
𝞹
UNION
UNION
FILTER( ,<10 )?VISIBILITY FILTER( ,>30 )?RAINFALL
?OBS ?snowfall ?uom
TABLE1.snowfall BOOLNODE_SNOW
?OBS ?VISIBILITY ?uom
TABLE1.VISIBILITY cmNODE_vis
?OBS ?rainfall ?uom
TABLE1.rainfall cmNODE_RAIN
{
FILTER( ,TRUE)?SNOWFALL
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LOW VISIBILITY STATIONS EXAMPLE
?OBS VISIBILITY OBSa
has value?OBS ?VISIBILITY
has unit?OBS ?uom
}
{
FILTER( ,<10 )?VISIBILITY
UNION
?OBS RAINFALL OBSa
has value?OBS ?RAINFALL
has unit?OBS ?uom
}
{
FILTER( ,>30 )?RAINFALL
?SENSORSELECT {
}
UNION
?OBS SNOWFALL OBSa
has value?OBS ?SNOWFALL
has unit?OBS ?uom}
{
𝞹
UNION
UNION
FILTER( ,<10 )VISIBILITY FILTER( ,>30 )RAINFALL
FROM TABLE1FROM TABLE1
FROM TABLE1
FILTER( ,TRUE)SNOWFALL
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LOW VISIBILITY STATIONS EXAMPLE
?OBS VISIBILITY OBSa
has value?OBS ?VISIBILITY
has unit?OBS ?uom
}
{
FILTER( ,<10 )?VISIBILITY
UNION
?OBS RAINFALL OBSa
has value?OBS ?RAINFALL
has unit?OBS ?uom
}
{
FILTER( ,>30 )?RAINFALL
?SENSORSELECT {
}
UNION
?OBS SNOWFALL OBSa
has value?OBS ?SNOWFALL
has unit?OBS ?uom}
{
𝞹
UNION
FROM TABLE1
WHERE RAINFALL>30
SELECT SENSOR FROM TABLE1
WHERE VISIBILITY<10
UNION
SELECT SENSOR FROM TABLE1
FILTER( ,TRUE)SNOWFALL
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SQLSPARQL
LOW VISIBILITY STATIONS EXAMPLE
?OBS VISIBILITY OBSa
has value?OBS ?VISIBILITY
has unit?OBS ?uom
}
{
FILTER( ,<10 )?VISIBILITY
UNION
?OBS RAINFALL OBSa
has value?OBS ?RAINFALL
has unit?OBS ?uom
}
{
FILTER( ,>30 )?RAINFALL
?SENSORSELECT {
}
UNION
?OBS SNOWFALL OBSa
has value?OBS ?SNOWFALL
has unit?OBS ?uom}
{
SELECT
UNION
WHERE RAINFALL>30
WHERE VISIBILITY<10
UNION
SENSOR FROM
SELECT SENSOR FROM TABLE1
WHERE SNOWFALL==TRUE
(
)
SELECT SENSOR FROM TABLE1
SELECT SENSOR FROM TABLE1
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HOW MAPPINGS WORK
SPARQL2SQL Mapping
S2SMLMAPPING
TABLE1.TEMPERATURE
has value
THING
TEMPERATURE OBS WIND SPEED OBS
CELCIUS PERCENT MPH
LOCATION
produces
located
HUMIDITY OBS
unit
TABLE1.humidity
has value
TABLE1.WINDSPEED
has value
?OBS TEMPERATURE OBSa
has value?OBS ?TEMPERATURE
has unit?OBS ?uom BGP
SPARQL
?OBS TEMPERATURE OBSa
has value?OBS ?TEMPERATURE
has unit?OBS ?uom
SELECT * WHERE {
}
JENA
SESAME
??
INTERFACE
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S2SMLMAPPING
WRITTEN IN RDFREFLECTS THE RDF IT REPRESENTS DIRECTLY
SUPPORTS COLLAPSING NODESEFFICIENT QUERIES WITH IOT TIME-SERIES DATA
R2rML COMPATIBLECAN BE TRANSLATED TO AND FROM R2RML
SUBJECT
object
predicate
IRImapIRI BNODE
IRI
FNODE
IRImapIRI BNODE FNODE
LITERAL LITERALMAP
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S2SMLMAPPING
Faux NODE HUMIDITY OBS
TABLE1.COL1
hasValue
LITERALMAP
<http://knoesis.wright.edu/ssw/obs/{readings.uuid}>
if (HUMIDITY OBS is projected):if(readings.uuid !exists):
create_col(readings.uuid)return IRImap(readings.uuid)
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RDF STREAM PROCESSING
sparql2streamSame engine and
mappings but translates to EPL instead of SQL
TRANSLATE QUERY
2
Stream WindowSPARQL query specifying
stream window size
REGISTER QUERY
1
Stream SocketsSupports multiple
platforms and streams with ZeroMQ
STREAM DATA
3
Real-time analyticsPIOTRe & Smart Home
Freeboard demo github.com/eugenesiow/iotwo
RECEIVE PUSH RESULTS
4
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SPARQL2stream
RSPQUERY
MAX( )?TEMPERATURESELECT
?OBS TEMPERATURE OBSa
has value?OBS ?TEMPERATURE
has unit?OBS ?uom
{
}
FROM Named Stream Obsstream Range 1h EPL SELECT MAX( )TEMPERATURE
FROM Weather.win:time(1 hour)Event Processing
Language
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EVALUATION WITH BENCHMARKS
SRBENCH
~20,000 Stations
100 – 300k triples
Wind, Rainfall, etc.
10 SRBench Queries
Zhang, Y, et al. (2012) "SRBench: a streaming RDF/SPARQL benchmark.”The 11th International Semantic Web Conference.
SMART HOME BENCH
Siow, E., Tiropanis, T., Hall, W. (2016). "Interoperable and Efficient: Linked Data for the Internet of Things." The 3rd International
Conference on Internet Science.
3 months, 1 home
~30k triples
Motion, energy, environment
4 Analytics Queries
GraphDB (OWLIM)
Ontop
Our Approach (S2S)
TDB
G
Morph
O
S
M
T
Raspberry Pi 2 Model B+1GB RAM, 900MHz Quad Core ARM Cortex A7, Class 10 SD Cards
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STORAGE SIZE
3ookHurricane Ike
1ookNEVADA BLIZZARD
3okSMART HOME
OUR APPROACH (s2S)
TDB
x15
x68
x112
GraphDB x9
x1352
x453
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Get the rainfall observed in a particular hour from all stations01
02
SRBENCH QUERY RESULTS
Q01 with an optional clause on unit of measure
x5
S2S
S
TDB GraphDB
Ontop Morph
x3
x13
x4k
x2
x4x4
x5k
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03
04
05
Detect if a hurricane has been observed
Get the average wind speed at the stations where the air temperature is >32
Join between wind observation and temperature observation subtrees time-consuming in low resource
environment (Raspberry Pi)
Detect if a station is observing a blizzard
x3
x6
x6
x88
x3
x3
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06
07
08
Get the stations with extremely low visibility
Detect stations that are recently broken
Get the daily minimal and maximal air temperature observed by the sensor at a given location
x2
x14
x4
x6
x6x5
x2
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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
Our Approach (s2s) is shown to be faster on all queries in the Distributed Meteorological System with SRBench
Join between wind force and wind direction observation subtrees is time-consuming in low resource
environment (Raspberry Pi)
x3
x3k
x2
x7
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Temperature aggregated by hour on a specified day01
02
SMART HOME RESULTS
Minimum and maximum temperature each day for a particular month
S2S TDB GraphDB
x7
x29
x3
x9
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03
04
Energy Usage Per Room By Day
Diagnose unattended appliances consuming energy with no motion in room
Our Approach (s2s) is shown, once again, to be faster on all queries for Smart Home Analytics
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.
x69
x13
x4
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STREAM PROCESSING EFFICIENCY
1 2 3 4 5 7 8 9 10
SRBENCH294 261
306
277k 3243k 5245
426
280k
98
Le-Phuoc, D., et al. (2011) "A native and adaptive approach for unified processing of linked streams and linked data.” The 10th International Semantic Web Conference. CQELSPerformance Improvement
For IoT Data Over
SMART HOME
196
21
167xImprovement
Query
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STREAM PROCESSING SCALABILITYVELOCITY>99% <1ms latency increasing from 1 to 1000 rows/ms
VOLUME33.5million rows, projected ~2.5 billion triples!
<1ms 10-100ms
1
2
5
10
100
1000
99% 100%
Rate
in ro
ws/m
s
Percentage Latency in ms Bands
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PERSONAL IOT REPOSITORY
Siow, E., Tiropanis, T. and Hall, W. (2016) PIOTRe: Personal Internet of Things Repository: The 15th International Semantic Web Conference P&D
github.com/eugenesiow/piotresparql2streamsparql2sql github.com/eugenesiow/sparql2sql
PIOTRE
Apps
sparql2stream sparql2sql
Metadata
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“It's a long road, it's a long and narrow way. If I can't work up to you, you'll surely have to work down to me someday.”
Narrow Way by Bob Dylan
eugene_siow
Travel Supported By:
LINKED DATA FOR INTEROPERABILITYA rich model to describe things and integrate connected thing’s data
OPTIMISED PERFORMANCE FOR STREAMS AND HISTORICAL DATAFROM 2 to 3 orders of magnitude performance improvement