This document discusses ontology-based access to sensor data streams. It motivates the need to provide universal web-based access to sensor data as streams. Existing approaches are discussed for querying static sensor data and streaming data using different stream processing engines. The author proposes using ontology models to continuously query real-time sensor data streams. Several hypotheses are presented regarding representing streaming data as ontology instances, extending SPARQL for streaming queries, rewriting ontology-based streaming queries to engine-specific queries using mappings, and evaluating the overhead of query rewriting. The document concludes that the hypotheses were confirmed by enabling ontology-based access to streaming sources and characterizing sensor metadata.
Efficient Data Stream Processing in the Internet of Things - SoftwareCampus A...Jonas Traub
This talk was presented for the SoftwareCampus Alumni e.V. on 07.12.2020. For more Information about the program check https://softwarecampus-alumni.de/ and https://softwarecampus.de/
Abstract: The Internet of Things (IoT) consists of billions of devices which form a cloud of network-connected sensor nodes. These sensor nodes supply a vast number of data streams with massive amounts of sensor data. Real-time sensor data enables diverse applications including traffic-aware navigation, machine monitoring, and home automation. In this talk, we will dive into recent research which optimizes real-time data gathering and data analysis in the IoT. The talk will provide an overview of available techniques which can be deployed on sensor nodes, intermediate network nodes, and central analysis systems. We will look into the state-of-the-art in practice and research and make you aware of important tradeoffs in real-time IoT data analysis.
CV: Jonas Traub is a postdoctoral researcher at the Database Systems and Information Management group at TU Berlin. His main research interests include stream processing, sensor data analysis, and data acquisition techniques. In his PhD, he studied efficient data gathering, processing, and transmission in the IoT. His research shows that one can save up to 87% in sensor reads and data transfers by applying smart data reduction techniques on sensor nodes. He further introduced a demand-based control layer which optimizes the data acquisition from thousands of sensors. With his Scotty-framework, he contributed a general aggregation technique for streaming systems which outperforms alternative solutions by an order of magnitude in throughput. His work received a Best Paper Award at the 22nd International Conference on Extending Database Technology (EDBT). Prior to his work at TU Berlin, he studied at KTH Stockholm and DHBW Stuttgart and worked several years at IBM in Germany and the USA. Jonas is an alumnus of Software Campus where he worked with SAP as industry partner.
Open Source Lambda Architecture for deep learningPatrick Nicolas
This presentation describes the various layers and open source components that can be used to design and implement a lambda architecture enabled to support batch processing for model training and streaming for prediction
Efficient Data Stream Processing in the Internet of Things - SoftwareCampus A...Jonas Traub
This talk was presented for the SoftwareCampus Alumni e.V. on 07.12.2020. For more Information about the program check https://softwarecampus-alumni.de/ and https://softwarecampus.de/
Abstract: The Internet of Things (IoT) consists of billions of devices which form a cloud of network-connected sensor nodes. These sensor nodes supply a vast number of data streams with massive amounts of sensor data. Real-time sensor data enables diverse applications including traffic-aware navigation, machine monitoring, and home automation. In this talk, we will dive into recent research which optimizes real-time data gathering and data analysis in the IoT. The talk will provide an overview of available techniques which can be deployed on sensor nodes, intermediate network nodes, and central analysis systems. We will look into the state-of-the-art in practice and research and make you aware of important tradeoffs in real-time IoT data analysis.
CV: Jonas Traub is a postdoctoral researcher at the Database Systems and Information Management group at TU Berlin. His main research interests include stream processing, sensor data analysis, and data acquisition techniques. In his PhD, he studied efficient data gathering, processing, and transmission in the IoT. His research shows that one can save up to 87% in sensor reads and data transfers by applying smart data reduction techniques on sensor nodes. He further introduced a demand-based control layer which optimizes the data acquisition from thousands of sensors. With his Scotty-framework, he contributed a general aggregation technique for streaming systems which outperforms alternative solutions by an order of magnitude in throughput. His work received a Best Paper Award at the 22nd International Conference on Extending Database Technology (EDBT). Prior to his work at TU Berlin, he studied at KTH Stockholm and DHBW Stuttgart and worked several years at IBM in Germany and the USA. Jonas is an alumnus of Software Campus where he worked with SAP as industry partner.
Open Source Lambda Architecture for deep learningPatrick Nicolas
This presentation describes the various layers and open source components that can be used to design and implement a lambda architecture enabled to support batch processing for model training and streaming for prediction
On the need for applications aware adaptive middleware in real-time RDF data ...Zia Ush Shamszaman
Introduction
Problems
Analysis
Evaluation
Adaptive approach
Conclusion
Streams are originated from a variety of sources (physical or virtual sensors)
Data is produced continuously (usually at short intervals) with a time stamp.
Queries over RDF streams are executed once but continuously monitored to report any change.
Different RSP Engines
CQELS, C-SPARQL, SPARQLstream, EPSPARQL, ETALIS, SPARKWAVE, etc.
Various features of RSP engines
Query
Input Data Model
Execution Strategy
Output Data Model
Input rate
Memory consumptions
RSP Engines Characteristics Categorization
Design Time includes aspects such as input data model, language to define processing rules, operational semantics, and supported streaming operators, etc.
Run Time includes aspects such as Memory, Latency, processing & optimization techniques, quality of service (QoS), load balancing, etc.
Is there any single best RSP engine that can adapt to the diverse application requirements?
•There is no single best system:
•according to the evaluation results and
•few RSP
benchmarks.
•The different features of RSP affects:
•user satisfaction, and
•RSP engines performance
•We need an adaptive middleware which can:
•bridge the gap between applications and RSP engines
•can satisfy diverse user requirements
Database Research at TU Berlin DIMA and DFKI IAM - USA Excursion Slides 2019Jonas Traub
In April 2019, we did an USA excursion and presented selected publications of the TU Berlin DIMA and the DFKI IAM research groups. This slide set contains the four teaser talks which we presented on the tour:
1) Jonas Traub: Optimized On-Demand Data Streaming from Sensor Nodes
2) Sebastian Breß: Generating Custom Code for Efficient Query Execution on Heterogeneous Processors
3) Martin Kiefer: Estimating Join Selectivities using Bandwidth Optimized Kernel Density Models
4) Andreas Kunft: BlockJoin: Efficient Matrix Partitioning through Joins
Towards efficient processing of RDF data streamsAlejandro Llaves
Presentation of short paper submitted to OrdRing workshop, held at ISWC 2014 - http://streamreasoning.org/events/ordring2014.
In the last years, there has been an increase in the amount of real-time data generated. Sensors attached to things are transforming how we interact with our environment. Extracting meaningful information from these streams of data is essential for some application areas and requires processing systems that scale to varying conditions in data sources, complex queries, and system failures. This paper describes ongoing research on the development of a scalable RDF streaming engine.
Towards efficient processing of RDF data streamsAlejandro Llaves
In the last years, there has been an increase in the amount of real-time data generated. Sensors attached to things are transforming how we interact with our environment. Extracting meaningful information from these streams of data is essential for some application areas and requires processing systems that scale to varying conditions in data sources, complex queries, and system failures. This paper describes ongoing research on the development of a scalable RDF streaming engine.
Presented at OrdRing workshop, International Semantic Web Conference 2014.
http://streamreasoning.org/events/ordring2014
Semantic Web Technologies for Intelligent Engineering ApplicationsMarta Sabou
Presentation at the closing event of the Christian Doppler Laboratory „Software Engineering Integration for Flexible Automation Systems“ (CDL-Flex) (http://cdl.ifs.tuwien.ac.at/).
Transient and persistent RDF views over relational databases in the context o...Nikolaos Konstantinou
As far as digital repositories are concerned, numerous benefits emerge from the disposal of their contents as Linked Open Data (LOD). This leads more and more repositories towards this direction. However, several factors need to be taken into account in doing so, among which is whether the transition needs to be materialized in real-time or in asynchronous time intervals. In this paper we provide the problem framework in the context of digital repositories, we discuss the benefits and drawbacks of both approaches and draw our conclusions after evaluating a set of performance measurements. Overall, we argue that in contexts with infrequent data updates, as is the case with digital repositories, persistent RDF views are more efficient than real-time SPARQL-to-SQL rewriting systems in terms of query response times, especially when expensive SQL queries are involved.
Finance market prediction has always been one of the hottest topics in Data Science and Machine Learning. However, the prediction algorithm is just a small piece of the puzzle. Building a data stream pipeline that is constantly combining the latest price info with high volume historical data is extremely challenging using traditional platforms, requiring a lot of code and thinking about how to scale or move into the cloud. This session is going to walk-through the architecture and implementation details of an application built on top of open-source tools that demonstrate how to easily build a stock prediction solution with no source code - except a few lines of R and the web interface that will consume data through a RESTful endpoint, real-time. The solution leverages in-memory data grid technology for high-speed ingestion, combining streaming of real-time data and distributed processing for stock indicator algorithms
On the need for applications aware adaptive middleware in real-time RDF data ...Zia Ush Shamszaman
Introduction
Problems
Analysis
Evaluation
Adaptive approach
Conclusion
Streams are originated from a variety of sources (physical or virtual sensors)
Data is produced continuously (usually at short intervals) with a time stamp.
Queries over RDF streams are executed once but continuously monitored to report any change.
Different RSP Engines
CQELS, C-SPARQL, SPARQLstream, EPSPARQL, ETALIS, SPARKWAVE, etc.
Various features of RSP engines
Query
Input Data Model
Execution Strategy
Output Data Model
Input rate
Memory consumptions
RSP Engines Characteristics Categorization
Design Time includes aspects such as input data model, language to define processing rules, operational semantics, and supported streaming operators, etc.
Run Time includes aspects such as Memory, Latency, processing & optimization techniques, quality of service (QoS), load balancing, etc.
Is there any single best RSP engine that can adapt to the diverse application requirements?
•There is no single best system:
•according to the evaluation results and
•few RSP
benchmarks.
•The different features of RSP affects:
•user satisfaction, and
•RSP engines performance
•We need an adaptive middleware which can:
•bridge the gap between applications and RSP engines
•can satisfy diverse user requirements
Database Research at TU Berlin DIMA and DFKI IAM - USA Excursion Slides 2019Jonas Traub
In April 2019, we did an USA excursion and presented selected publications of the TU Berlin DIMA and the DFKI IAM research groups. This slide set contains the four teaser talks which we presented on the tour:
1) Jonas Traub: Optimized On-Demand Data Streaming from Sensor Nodes
2) Sebastian Breß: Generating Custom Code for Efficient Query Execution on Heterogeneous Processors
3) Martin Kiefer: Estimating Join Selectivities using Bandwidth Optimized Kernel Density Models
4) Andreas Kunft: BlockJoin: Efficient Matrix Partitioning through Joins
Towards efficient processing of RDF data streamsAlejandro Llaves
Presentation of short paper submitted to OrdRing workshop, held at ISWC 2014 - http://streamreasoning.org/events/ordring2014.
In the last years, there has been an increase in the amount of real-time data generated. Sensors attached to things are transforming how we interact with our environment. Extracting meaningful information from these streams of data is essential for some application areas and requires processing systems that scale to varying conditions in data sources, complex queries, and system failures. This paper describes ongoing research on the development of a scalable RDF streaming engine.
Towards efficient processing of RDF data streamsAlejandro Llaves
In the last years, there has been an increase in the amount of real-time data generated. Sensors attached to things are transforming how we interact with our environment. Extracting meaningful information from these streams of data is essential for some application areas and requires processing systems that scale to varying conditions in data sources, complex queries, and system failures. This paper describes ongoing research on the development of a scalable RDF streaming engine.
Presented at OrdRing workshop, International Semantic Web Conference 2014.
http://streamreasoning.org/events/ordring2014
Semantic Web Technologies for Intelligent Engineering ApplicationsMarta Sabou
Presentation at the closing event of the Christian Doppler Laboratory „Software Engineering Integration for Flexible Automation Systems“ (CDL-Flex) (http://cdl.ifs.tuwien.ac.at/).
Transient and persistent RDF views over relational databases in the context o...Nikolaos Konstantinou
As far as digital repositories are concerned, numerous benefits emerge from the disposal of their contents as Linked Open Data (LOD). This leads more and more repositories towards this direction. However, several factors need to be taken into account in doing so, among which is whether the transition needs to be materialized in real-time or in asynchronous time intervals. In this paper we provide the problem framework in the context of digital repositories, we discuss the benefits and drawbacks of both approaches and draw our conclusions after evaluating a set of performance measurements. Overall, we argue that in contexts with infrequent data updates, as is the case with digital repositories, persistent RDF views are more efficient than real-time SPARQL-to-SQL rewriting systems in terms of query response times, especially when expensive SQL queries are involved.
Finance market prediction has always been one of the hottest topics in Data Science and Machine Learning. However, the prediction algorithm is just a small piece of the puzzle. Building a data stream pipeline that is constantly combining the latest price info with high volume historical data is extremely challenging using traditional platforms, requiring a lot of code and thinking about how to scale or move into the cloud. This session is going to walk-through the architecture and implementation details of an application built on top of open-source tools that demonstrate how to easily build a stock prediction solution with no source code - except a few lines of R and the web interface that will consume data through a RESTful endpoint, real-time. The solution leverages in-memory data grid technology for high-speed ingestion, combining streaming of real-time data and distributed processing for stock indicator algorithms
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
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1. Ontology-based Access to
Sensor Data Streams
Jean-Paul Calbimonte
Supervisor: Oscar Corcho
Ontology Engineering Group
Facultad de Informática, Universidad Politécnica de Madrid
jp.calbimonte@upm.es
PhD Thesis Defense
18.4.2013
5. Sensor Networks and the Web
5
Sensor Networks
users
applications
data streams
Volume
Velocity
Variety WEB
Universal Web-based access to Sensor data
6. Querying the semantic sensor Web
6
e.g. publish sensor data as RDF/Linked Data?
URIs as names of things
HTTP URIs
useful information when URI
is dereferenced
Link to other URIs
users
applications
WEB
Use ontology models to continuously query real-
time data streams originated from sensors?
1
static vs. streams
one-off vs. continuous
7. Research questions & hypotheses
7
Ontology models to query real-time sensor data streams?
Access heterogeneous SPEs using ontologies as an
overarching data model?
SPARQL streaming extensions for querying data from SPEs
(stream processing engines)?
1
H1: Sensor streaming data instances of an ontology model
H2: SPARQL extensions streaming operators & continuous processing
H3: Ontology-based streaming queries rewritten to relational-based
queries using mappings
H4: Ontology-based streaming queries abstract expressions
concrete executable SPE queries
H5: Query rewriting Pull & Push delivery acceptable overhead
10. Characterizing semantic sensor metadata
10
users
applications
WEB
Characterizing sensor data, deriving semantic
metadata from the sensor observations
2
different publishers
different metadata
publish streams
Search/query relevant
data sources?
GSN
11. Research questions & hypotheses
11
Data representation suitable for extracting data features
that characterize a set of sensor streams?
Classification and mining techniques to characterize
sensor data streams?
2
H6: Sensor data series find characteristic patterns
make it recognizable among other types
H7: Slope representations semantic properties such as the type of data
learned with classification techniques
acceptable precision
12. Contributions
12
SPARQL extensions & formalization
rewriting to algebra expressions
using declarative mappings
results data translation
query evaluation pluggable to ≠ SPEs
query rewriting using R2RML mappings
data representation as slope distributions
characterize types of sensor data
classifying sensor time series
extract metadata features
derive semantic properties & R2RML
SPARQLStream
Sensor metadata characterization
QueryingMetadata
2
1
13. Limitations
13
L1: Rewriting medium sampling throughput, e.g. Env. monitoring
L2: Query expressivity is limited to underlying SPEs’.
L3: Adapters implemented for custom sources.
L4: Querying only simple entailment
L5: Arbitrarily noisy sensor series no accurate characterization.
L6: Classification number of sensor time series in training set
L7: Data characterization is not computed in real-time, but offline
14. 14
Outline
Motivation
Background
Conclusions
Semantic stream query processing
Sensor metadata characterization
Ontology-based Access to Sensor Data Streams
Hypotheses & contributions
Challenges
Data Streams Continuous queries Window
SPEs Ontology-based data access
17. Stream Processing Engines (SPE)
17
Data Stream Management Systems (DSMS)
Complex Event Processors (CEP)
Sensor Data Middleware
CQL/Strea
m
Borealis
TelegraphCQ
StreamMill
Cayuga
GEM CEDR
NiagaraCQ
Rapide
CosmHourglass
SStreamWare GSN
IBM InfoSphere
Sybase CEP
Microsoft StreamInsight
Oracle CEP
Esper
StreamBase
Diverse query languages
Different query capabilities
Different query models
18. Extracting data from relational databases
18
WEB
Ontology-based
data access
one-off SPARQL
queries
data as RDF
relational database
RDB to RDF
mappings
static data
D2R
Morph
ODEMapster Triplify
UltraWrap Mastro
R2RML
W3C SSN Ontology
19. Summary
19
Existing SPEs available and producing data streams
Ontology-based access only for stored data
SPARQL query language not suitable for streams
SPEs are highly heterogeneous in models and queries
20. 20
Outline
Motivation
Background
Conclusions
Semantic stream query processing
Sensor metadata characterization
Ontology-based Access to Sensor Data Streams
Hypotheses & contributions
SPARQLStream
Challenges
Query rewritingRDF Stream
Mappings using R2RML Execution over SPEs
21. RDF Streams
21
s,p,o
<aemet:observation1, qudt:hasNumericValue, “15.5”>
<aemet:observation1, ssn:observedBy, aemet:Sensor3>
For streams?
( s,p,o ,τ)
(<aemet:observation1, qudt:hasNumericValue, “15.5”>,34532)
timestamped triples
• Gutierrez et al. (2007) Introducing time into RDF. IEEE TKDE
• Rodríguez et al. (2009) Semantic management of streaming data. SSN
22. SPARQLStream extensions
22
SELECT (MAX(?temperature) AS ?maxtemp) ?sensor
WHERE {
?obs ssn:observedBy ?sensor.
?obs ssn:observationResult ?res.
?res aemet:hasAirTemperatureValue ?val.
?val qu:numericValue ?temperature.
}
GROUP BY ?sensor
SELECT (MAX(?temp) AS ?maxtemp) ?sensor
FROM NAMED STREAM <http://aemet.linkeddata.es/observations.srdf> [NOW-1 HOURS]
WHERE {
?obs ssn:observedBy ?sensor.
?obs ssn:observationResult ?res.
?res aemet:hasAirTemperatureValue ?val.
?val qu:numericValue ?temp.
}
GROUP BY ?sensor
SPARQLStream
Named streams
Time windows
Other approaches: Streaming SPARQL (2008), C-SPARQL (2009), CQELS
(2011), EP-SPARQL (2011), INSTANS (2012)
23. Streaming SPARQL execution approaches
23
Extend RDF for streaming data
Extend SPARQL for streaming RDF
Use a SPE internally for evaluation
Query rewriting to SPEs
RDF Streaming engine from scratch
Logic-programming based query evaluation
~Similarities
Divergence
streams
DSMSs
CEPs
Middleware
SPARQLStream
24. Mapping SPE schemas and ontologies
24
wan7
timed: datetime PK
sp_wind: float
timed sp_wind
1 3.4
2 5.6
3 11.2
4 1.2
5 3.1
.. …
Queries
SELECT sp_wind
FROM wan7 [NOW -5 HOUR]
WHERE sp_wind >10
SPE
SPE data schemas
ssn:Observation
Ontology models
SPARQLStream Queries
Stream-to-ontology
mappings
SELECT ?wspeed
FROM STREAM <SensorReadings.srdf> [NOW–5 HOUR]
WHERE {
?obs a ssn:ObservationValue;
qudt:numericalValue ?wspeed;
FILTER (?wspeed>10) }
35. Experiments AEMET
Confusion matrix AEMET
H6: Sensor data series
find characteristic patterns
make it recognizable among other types
35
Classification according to type
FPs on subclasses of the same property
36. Evaluation vs SAX
36
H7: Slope representations
type of data: semantic property
learned through classification
39. Conclusions
H1: Sensor streaming data instances of an ontology model
H2: SPARQL extensions streaming operators & continuous processing
H3: Ontology-based streaming queries rewritten to relational-based
queries using mappings
Mapping sensor data to ontology instances, e.g. SSN Ontology
SPARQLStream data model, extensions syntax, semantics
SPARQLStream semantics of query rewriting to relational steaming
algebra
usage of declarative mappings (W3C R2RML)
Calbimonte, Corcho & Gray. Enabling ontology-based access to streaming data sources. ISWC 2010
Gray, García-Castro, Kyzirakos, Karpathiotakis, Calbimonte, Page et al. A semantically enabled service
architecture for mashups over streaming and stored data. ESWC 2011
Gray, Sadler, Kit, Kyzirakos, Karpathiotakis, Calbimonte, Page, García-Castro, et al. A semantic sensor
web for environmental decision support applications. Sensors, MDPI, 2011
Calbimonte, Corcho & Gray. Ontology-based Access to Streaming Data. In Posters ESWC 2010
39
40. Conclusions
40
H4: Ontology-based streaming queries abstract expressions
concrete executable SPE queries
Instantiate, execute ≠ SPEs: SNEE (DSMS), Esper (CEP), GSN & Cosm (Middlwr)
Available implementation
application in different domains
H5: Query rewriting Pull & Push delivery evaluation overhead
SPARQLStream evaluation overhead wrt. native execution
Push & pull delivery evaluation
Calbimonte, Jeung, Corcho & Aberer. Enabling Query Technologies for the Semantic Sensor Web. IJSWIS 2012.
Calbimonte & Corcho. Evaluating SPARQL Queries over RDF Streams. Linked Data Management: Principles
and Techniques, CRC Press, 2013 (under review)
Zhang, Duc, Corcho & Calbimonte. SRBench: A Streaming RDF/SPARQL Benchmark. ISWC 2012.
Ruckhaus, Calbimonte, García-Castro & Corcho. Short Paper: From Streaming Data to Linked Data–A Case
Study with Bike Sharing Systems. ISWC SSN 2012
41. Conclusions
41
H6: Sensor data series analyze in order to find characteristic patterns
make it recognizable among other types
H7: Slope representations semantic properties such as the type of data
learned with classification techniques
acceptable precision
41
Raw observations analysis slope distribution representation
compared with SoA representations i.e. SAX
Evaluation of classification task real world datasets AEMET, SwissEx
in presence of noisy data
deriving semantic metadata
Calbimonte, Yan, Jeung, Corcho & Aberer. Deriving Semantic Sensor Metadata from Raw Measurements.
ISWC SSN 2012
Calbimonte, Jeung, Corcho, & Aberer. Semantic Sensor Data Search in a Large-Scale Federated Sensor
Network. ISWC SSN 2011
42. Future directions
42
WEB
SPARQLStream queries
Publishing Linked Stream Data
Currently static
SPARQL streaming
standards
Dereferencing streaming
data
Query Federation
Distributed sensor data
Static and streaming sources
Stream Reasoning
query rewriting, expanding queries
Expresiveness
Integrate with the Web of Data
Inferencing
43. Future directions
WEB
Sensor pattern classification
Combine with query
processing
Live data classification
Statistical & quality analysis Integrate statistic analyisis
Mappings to statistical models
Data quality filtering
Parallel Massive Stream Processing Online stream analysis
Scalable stream processing
S4, Storm, Streamcloud
Heterogeneity
43
44. Ontology-based Access to
Sensor Data Streams
Jean-Paul Calbimonte
Supervisor: Oscar Corcho
Ontology Engineering Group
Facultad de Informática, Universidad Politécnica de Madrid
18.4.2013
jp.calbimonte@upm.es
PhD Thesis Defense
52. RDF Streams and SPARQLStream
52
RDF Stream
Time window
Window-Stream
53. Mappings
53
Subject, predicate, object
Given a triple pattern t p = (sp, pp,op), the semantics of its evaluation over a
lational streams referenced by a set of mappings M , is given by eval (t p,M), wh
n algebra expression defined as:
eval (t p,M) = ρf s→sp,f p→pp,f o→opπf s,f p,f o(s)
where ρ is the relational rename operation and π is the relational projection
on. s is the stream referenced by the mapping µ = f i ndM appi ng(t p,M) and f s
,
e the functions of µ that generate the projection expressions for producing respec
e subject, predicate and object, for every tuple of s.
For the previous example, the evaluation of t p1 is given by:
eval (t p1,M) = ρf s→sp,f p→pp,f o→opπf s
µ1
(s1.ts),f
p
µ1
(),f o
µ1
()(s1)
The resulting algebra expression projects the s1.ts attribute, applying the f s
on to create the subject. The functions f
p
µ1
and f o
µ1
in this case are constants,
edicate and object are the same for all tuples of s1. For the evaluation of more co
Evaluate query
54. Rewrite to algebra
54
Then, the evaluation of gp can be represented as the following algebra expression:
eval (t p,M) = ωts,te,δ πf s
µ1
(s1) ✶ πf s
µ2
,f o
µ2
(s1) ✶ πf s
µ4
,f o
µ4
(s1) ✶πf s
µ5
,f o
µ5
(s1)
This expression can be represented as a tree (Figure 4.1), where the leaf nodes are the
streams and the other nodes are the relational streaming operators.
Figure 4.1: Tree representation of the evaluation of a SPARQL Stream query rewritten as an alge-
bra expression.
eval (t p, M ) = ωts,te,δ πf s
µ1
(s1) ✶ πf s
µ2
,f o
µ2
(s1) ✶ πf s
µ4
,f o
µ4
(s1) ✶πf s
µ5
,f o
µ5
(s1)
This expression can be represented as a tree (Figure 4.1), where the leaf nodes are th
streams and the other nodes are the relational streaming operators.
Figure 4.1: Tree representation of the evaluation of a SPARQL Stream query rewritten as an alg
bra expression.
59. Query Features
59
Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17
1.Graph pattern
matching
A A,F,O A A,F A A,F,U A A A A A,F A,F,U A,F A,F,U A,F A,F A,F
2. Solution modifier P,D P,D P P P P P,D P P P,D P,D P P P,D P P P
3. Query form S S A S C S S S S S S S S S S S S
4. SPARQL 1.1 F,P A A,E,M
,F
A,S N A,E,M A,E,M A,S,M
,F
A,S,E,
M,F,P
A,E,M
,F,P
F,P A,E,M
,P
P P
5. Reasoning C R C A C
6. Streaming T T T T T T T,D T T T T T T T T
7. Dataset O O O O O O O O,S O,S O,S O,S O,S,G O,S,G O,S,G O,S,D O,S,G
,D
S
1. And, Filter, Union, Optional
2. Projection, Distinct
3. Select, Construct, Ask
4. Aggregate, Subquery, Negation, Expr in SELECT, assignMent,
Functions&operators, PropertyPath
5. subClassOf, subpRopertyOf, owl:sameAs
6. Time-based window, Istream, Dstream,Rstream
7. LinkedObservationData, LinkedSensorMetadata, GeoNames, Dbpedia