SlideShare a Scribd company logo
1 of 26
Download to read offline
Toward Semantic Data Stream
Technologies and Applications
Raja CHIKY
raja.chiky@isep.fr
2013-2014
16/01/20141
About me
}  Associate professor in Computer Science – LISITE-RDI
}  Research interest: Data stream mining, scalability and resource optimization in cloud
computing, recommender systems
}  Research field: Large scale data management
1. Real-time and
distributed
processing of various
data sources
2. Use semantic
technologies to add
a semantic layer
3. Distributive and
collaborative data
mining
4. Optimizing resources in large scale systems
Heterogeneous	
  and	
  
dynamic	
  data	
  streams	
  
Heterogeneous	
  and	
  
sta1c	
  data	
  
sensors
5. Modeling and validation of complex systems
16/01/20142
Outline
}  What is a data stream ?
}  Data stream management systems
}  New applications
}  Semantic Data Stream
}  Conclusion
16/01/20143
What is a data stream?
}  Golab & Oszu (2003): “A data stream is a real-time, continuous, ordered
(implicitly by arrival time or explicitly by timestamp) sequence of items. It is
impossible to control the order in which items arrive, nor is it feasible to
locally store a stream in its entirety.”
}  Massive volumes of structured data, items arrive at a high rate.
16/01/20144
Data Stream Management Systems
DBMS DSMS
Data model Permanent updatable relations Streams and permanent updatable
relations
Storage Data is stored on disk Permanent relations are stored on disk
Streams are processed on the fly
Query SQL language
Creating structures
Inserting/updating/deleting data
Retrieving data (one-time query)
SQL-like query language
Standard SQL on permanent relations
Extended SQL on streams with
windowing
Continuous queries
Performance Large volumes of data Optimization of computer resources to
deal with
Several streams
Several queries
Ability to face variations in arrival rates
without crash
16/01/20145
Existing DSMS
16/01/20146
World of data streams
Website logs
Network
monitoring Financial services
eCommerce Traffic control
Power
consumption
Weather
forecasting
16/01/20147
New applications : 360-degree view of
the customer
Why?	
  
What?	
  
How?	
  
When/
Where?	
  
Who?	
  
Opera1onal	
  
data	
  
Behavioral	
  
data	
  
Descrip1ve	
  
data	
  
Interac1on	
  
data	
  
Contextual	
  
data	
  
16/01/20148
New applications : 360-degree view of
the customer (Contd.)
Descrip1ve	
  
data	
  
•  Profile	
  
•  Contractual	
  
data	
  
Interac1on	
  
data	
  
•  Mul1-­‐chanel	
  
•  Email/chat/
call	
  center	
  
Opera1onal	
  
data	
  
•  Orders	
  	
  
•  Transac1ons	
  
•  Payment	
  
history	
  
Behavioral	
  
data	
  
•  Web	
  clicks	
  
•  Usage	
  
history	
  
•  Social	
  media	
  
•  Opinions	
  
Contextual	
  
data	
  
•  Geographical	
  
data	
  
•  Devices	
  
Internal	
  and	
  External	
  sources	
  
16/01/20149
New applications: Social networks
}  Twitter:A massive Data stream
}  Free social networking and micro-blogging service
}  3 billion requests per day
}  Sentiment Analysis
}  Tracking trending hashtags
}  Detect breaking news
}  Analyze Marketing campaigns
Tag1
Tag2
Tag2Tag1
Time
16/01/201410
}  E.g., Twitter + MetaCarta
[source: http://blog.blprnt.com/blog/blprnt/just-landed-processing-twitter-metacarta-hidden-data ]
16/01/201412
Combination of stream and static data
}  BOTTARI: An augmented reality application for
personalized recommendation of restaurants in Seoul
What have you seen?
16/01/201413 E. Della valle’s talk , LarKC
Too much data streams
16/01/201414
Semantic Web, Linked Data
}  Semantic Web
}  Collaborative movement to promote common data formats on
the World Wide Web
}  Inclusion of semantic content in web pages
}  From unstructured and semi-structured documents to a
« Web of data »
}  Linked Data
}  Best practices to represent, publish, link data on the Semantic
Web
}  Linked Data Cloud: collection of datasets that have been
published in Linked data format
16/01/201415
Linked data
16 16/01/2014
Semantic Web technologies for data
stream
}  Annotate stream data with semantic metadata
}  Apply Linked Data principles to publish streaming data
}  Interlink streaming data with existing datasets
}  Integrate data stream processing + reasoning
}  Objectives : interoperability, automation,
enrichment
16/01/201417
Existing prototypes
CQELS
SPARQL-
Stream
EP-
SPARQL
C-
SPARQL
16/01/201418
Introduction to RDF
}  Statement: "The author of http://www.w3schools.com/rdf
is Jan Egil Refsnes”
}  The subject of the statement above is: http://
www.w3schools.com/rdf
}  The predicate is: author
}  The object is: Jan Egil Refsnes
19
Subject Object
predicate
Predicate
16/01/2014
RDF Stream
}  RDF Stream is defined as an ordered sequence of
pair
}  Each pair consists of a RDF triplet and a timestamp
}  Example
(< :traveller1 :justLanded :placeA >, T1)
(< :traveller2 :justLanded :placeB >, T1)
(< :traveller3 :justLanded :placeA >, T2)
(< :traveller1 :justLanded :placeC >, T3)
16/01/201420
RDF Stream – Several definitions
}  Several possibilities:
}  An RDF stream is an infinite sequence of timestamped events
(triples or graphs), …
<<s,p,o>i,ti >
<<s,p,o>i+1,ti+1 >
<<s,p,o>i+2,ti+2 >
…
}  An RDF stream is an infinite sequence of triple occurrences
<<s,p,o>,tα,tω> where <s,p,o> is an RDF triple and tα and tω
are the start and end of the interval
16/01/201421
Approaches
In RDF Stream
models
(timestamps,
events, time
intervals, triple-
based, graph-
based …)
16/01/201422
Example: SRBench
ASK
WHERE {
STREAM <http://www.cwi.nl/SRBench/observations> [RANGE 10800s SLIDE 600s]
{?observation om-owl:procedure ?sensor ;
om-owl:observedProperty weather:WindSpeed ;
om-owl:result [ om-owl:floatValue ?value ] .}
}
GROUP BY ?sensor
HAVING ( AVG(?value) >= "74"^^xsd:float )
Q: Detect if a hurricane has been observed
“A hurricane has a sustained wind (for more than 3 hours) of at least 33 metres
per second or 74 miles per hour (119 km/h).”
ASK
FROM STREAM <http://www.cwi.nl/SRBench/observations> [RANGE 3h STEP 10m]
WHERE {  
?observation om-owl:procedure ?sensor ;
om-owl:observedProperty weather:WindSpeed ;
om-owl:result [ om-owl:floatValue ?value ] . }
GROUP BY ?sensor
HAVING ( AVG(?value) >= "74"^^xsd:float )
16/01/201423
W3C RDF Stream Processing
Community
}  http://www.w3.org/community/rsp/
16/01/201424
W3C RSP Community Group mission
“The mission of the RDF Stream Processing
Community Group (RSP) is to define a common model
for producing, transmitting and continuously querying
RDF Streams. This includes extensions to both RDF
and SPARQL for representing streaming data, as well
as their semantics. Moreover this work envisions an
ecosystem of streaming and static RDF data sources
whose data can be combined through standard
models, languages and protocols (...)”
16/01/201425
Conclusion: Research Challenges
}  Benchmarking: LSBench, SRBench
}  Data Quality: Dealing with incomplete and noisy data
}  Reasoning on streams: formal semantics, efficiency,
scalability
}  Distributed and parallel processing: data stream sources
are distributed in nature
}  Scalability: approximation, load shedding, etc.
16/01/201426
Thanks for your attention
raja.chiky@isep.fr
16/01/201427

More Related Content

Viewers also liked

Semantic Discovery and Integration of Urban Data Streams
Semantic Discovery and Integration of Urban Data StreamsSemantic Discovery and Integration of Urban Data Streams
Semantic Discovery and Integration of Urban Data StreamsAli Intizar
 
Research Seminar Presentation - A framework for partitioning and execution of...
Research Seminar Presentation - A framework for partitioning and execution of...Research Seminar Presentation - A framework for partitioning and execution of...
Research Seminar Presentation - A framework for partitioning and execution of...malinga2009
 
Real-Time Big Data Stream Analytics
Real-Time Big Data Stream AnalyticsReal-Time Big Data Stream Analytics
Real-Time Big Data Stream AnalyticsAlbert Bifet
 
Data Stream Processing with Apache Flink
Data Stream Processing with Apache FlinkData Stream Processing with Apache Flink
Data Stream Processing with Apache FlinkFabian Hueske
 

Viewers also liked (6)

Semantic Discovery and Integration of Urban Data Streams
Semantic Discovery and Integration of Urban Data StreamsSemantic Discovery and Integration of Urban Data Streams
Semantic Discovery and Integration of Urban Data Streams
 
Research Seminar Presentation - A framework for partitioning and execution of...
Research Seminar Presentation - A framework for partitioning and execution of...Research Seminar Presentation - A framework for partitioning and execution of...
Research Seminar Presentation - A framework for partitioning and execution of...
 
Real-Time Big Data Stream Analytics
Real-Time Big Data Stream AnalyticsReal-Time Big Data Stream Analytics
Real-Time Big Data Stream Analytics
 
EU FP7 CityPulse Project
EU FP7 CityPulse ProjectEU FP7 CityPulse Project
EU FP7 CityPulse Project
 
18 Data Streams
18 Data Streams18 Data Streams
18 Data Streams
 
Data Stream Processing with Apache Flink
Data Stream Processing with Apache FlinkData Stream Processing with Apache Flink
Data Stream Processing with Apache Flink
 

Similar to Toward Semantic Data Stream - Technologies and Applications

A Data-driven Approach for Internet of Things Applications: Methods and Case ...
A Data-driven Approach for Internet of Things Applications: Methods and Case ...A Data-driven Approach for Internet of Things Applications: Methods and Case ...
A Data-driven Approach for Internet of Things Applications: Methods and Case ...Suparna De
 
URBAN TRAFFIC DATA HACK - ROLAND MAJOR
URBAN TRAFFIC DATA HACK - ROLAND MAJORURBAN TRAFFIC DATA HACK - ROLAND MAJOR
URBAN TRAFFIC DATA HACK - ROLAND MAJORBig Data Week
 
Jewei Hans & Kamber Chapter 8
Jewei Hans & Kamber  Chapter 8Jewei Hans & Kamber  Chapter 8
Jewei Hans & Kamber Chapter 8Houw Liong The
 
Chapter 08 Data Mining Techniques
Chapter 08 Data Mining Techniques Chapter 08 Data Mining Techniques
Chapter 08 Data Mining Techniques Houw Liong The
 
On the need for a W3C community group on RDF Stream Processing
On the need for a W3C community group on RDF Stream ProcessingOn the need for a W3C community group on RDF Stream Processing
On the need for a W3C community group on RDF Stream ProcessingPlanetData Network of Excellence
 
OrdRing 2013 keynote - On the need for a W3C community group on RDF Stream Pr...
OrdRing 2013 keynote - On the need for a W3C community group on RDF Stream Pr...OrdRing 2013 keynote - On the need for a W3C community group on RDF Stream Pr...
OrdRing 2013 keynote - On the need for a W3C community group on RDF Stream Pr...Oscar Corcho
 
Extracting City Traffic Events from Social Streams
 Extracting City Traffic Events from Social Streams Extracting City Traffic Events from Social Streams
Extracting City Traffic Events from Social StreamsPramod Anantharam
 
Paper id 25201431
Paper id 25201431Paper id 25201431
Paper id 25201431IJRAT
 
Open government data portals: from publishing to use and impact
Open government data portals: from publishing to use and impactOpen government data portals: from publishing to use and impact
Open government data portals: from publishing to use and impactElena Simperl
 
Seminaire bigdata23102014
Seminaire bigdata23102014Seminaire bigdata23102014
Seminaire bigdata23102014Raja Chiky
 
Towards efficient processing of RDF data streams
Towards efficient processing of RDF data streamsTowards efficient processing of RDF data streams
Towards efficient processing of RDF data streamsAlejandro Llaves
 
Towards efficient processing of RDF data streams
Towards efficient processing of RDF data streamsTowards efficient processing of RDF data streams
Towards efficient processing of RDF data streamsAlejandro Llaves
 
Semantic web design for www.data.gov.sg - Presentation
Semantic web design for www.data.gov.sg - PresentationSemantic web design for www.data.gov.sg - Presentation
Semantic web design for www.data.gov.sg - PresentationMuthu Kumaar Thangavelu
 
Certain Analysis on Traffic Dataset based on Data Mining Algorithms
Certain Analysis on Traffic Dataset based on Data Mining AlgorithmsCertain Analysis on Traffic Dataset based on Data Mining Algorithms
Certain Analysis on Traffic Dataset based on Data Mining AlgorithmsIRJET Journal
 
TransportDCAT-AP and PhD Thesis at Civic Lab Brussels
TransportDCAT-AP and PhD Thesis at Civic Lab BrusselsTransportDCAT-AP and PhD Thesis at Civic Lab Brussels
TransportDCAT-AP and PhD Thesis at Civic Lab BrusselsDavid Chaves-Fraga
 
The data streaming processing paradigm and its use in modern fog architectures
The data streaming processing paradigm and its use in modern fog architecturesThe data streaming processing paradigm and its use in modern fog architectures
The data streaming processing paradigm and its use in modern fog architecturesVincenzo Gulisano
 
From open data to data-driven services
From open data to data-driven servicesFrom open data to data-driven services
From open data to data-driven servicesSlim Turki, Dr.
 

Similar to Toward Semantic Data Stream - Technologies and Applications (20)

Research Plan 2014
Research Plan 2014Research Plan 2014
Research Plan 2014
 
A Data-driven Approach for Internet of Things Applications: Methods and Case ...
A Data-driven Approach for Internet of Things Applications: Methods and Case ...A Data-driven Approach for Internet of Things Applications: Methods and Case ...
A Data-driven Approach for Internet of Things Applications: Methods and Case ...
 
URBAN TRAFFIC DATA HACK - ROLAND MAJOR
URBAN TRAFFIC DATA HACK - ROLAND MAJORURBAN TRAFFIC DATA HACK - ROLAND MAJOR
URBAN TRAFFIC DATA HACK - ROLAND MAJOR
 
Jewei Hans & Kamber Chapter 8
Jewei Hans & Kamber  Chapter 8Jewei Hans & Kamber  Chapter 8
Jewei Hans & Kamber Chapter 8
 
Chapter 08 Data Mining Techniques
Chapter 08 Data Mining Techniques Chapter 08 Data Mining Techniques
Chapter 08 Data Mining Techniques
 
On the need for a W3C community group on RDF Stream Processing
On the need for a W3C community group on RDF Stream ProcessingOn the need for a W3C community group on RDF Stream Processing
On the need for a W3C community group on RDF Stream Processing
 
OrdRing 2013 keynote - On the need for a W3C community group on RDF Stream Pr...
OrdRing 2013 keynote - On the need for a W3C community group on RDF Stream Pr...OrdRing 2013 keynote - On the need for a W3C community group on RDF Stream Pr...
OrdRing 2013 keynote - On the need for a W3C community group on RDF Stream Pr...
 
Extracting City Traffic Events from Social Streams
 Extracting City Traffic Events from Social Streams Extracting City Traffic Events from Social Streams
Extracting City Traffic Events from Social Streams
 
Paper id 25201431
Paper id 25201431Paper id 25201431
Paper id 25201431
 
Open government data portals: from publishing to use and impact
Open government data portals: from publishing to use and impactOpen government data portals: from publishing to use and impact
Open government data portals: from publishing to use and impact
 
Seminaire bigdata23102014
Seminaire bigdata23102014Seminaire bigdata23102014
Seminaire bigdata23102014
 
Towards efficient processing of RDF data streams
Towards efficient processing of RDF data streamsTowards efficient processing of RDF data streams
Towards efficient processing of RDF data streams
 
Towards efficient processing of RDF data streams
Towards efficient processing of RDF data streamsTowards efficient processing of RDF data streams
Towards efficient processing of RDF data streams
 
Semantic web design for www.data.gov.sg - Presentation
Semantic web design for www.data.gov.sg - PresentationSemantic web design for www.data.gov.sg - Presentation
Semantic web design for www.data.gov.sg - Presentation
 
Certain Analysis on Traffic Dataset based on Data Mining Algorithms
Certain Analysis on Traffic Dataset based on Data Mining AlgorithmsCertain Analysis on Traffic Dataset based on Data Mining Algorithms
Certain Analysis on Traffic Dataset based on Data Mining Algorithms
 
Stream Processing
Stream Processing Stream Processing
Stream Processing
 
TransportDCAT-AP and PhD Thesis at Civic Lab Brussels
TransportDCAT-AP and PhD Thesis at Civic Lab BrusselsTransportDCAT-AP and PhD Thesis at Civic Lab Brussels
TransportDCAT-AP and PhD Thesis at Civic Lab Brussels
 
The data streaming processing paradigm and its use in modern fog architectures
The data streaming processing paradigm and its use in modern fog architecturesThe data streaming processing paradigm and its use in modern fog architectures
The data streaming processing paradigm and its use in modern fog architectures
 
From open data to data-driven services
From open data to data-driven servicesFrom open data to data-driven services
From open data to data-driven services
 
Integrating Sensor and Social Data for Understanding City Events
Integrating Sensor and Social Data for Understanding City EventsIntegrating Sensor and Social Data for Understanding City Events
Integrating Sensor and Social Data for Understanding City Events
 

Recently uploaded

Technical Management of cement industry.pdf
Technical Management of cement industry.pdfTechnical Management of cement industry.pdf
Technical Management of cement industry.pdfMadan Karki
 
Technology Features of Apollo HDD Machine, Its Technical Specification with C...
Technology Features of Apollo HDD Machine, Its Technical Specification with C...Technology Features of Apollo HDD Machine, Its Technical Specification with C...
Technology Features of Apollo HDD Machine, Its Technical Specification with C...Apollo Techno Industries Pvt Ltd
 
Tachyon 100G PCB Performance Attributes and Applications
Tachyon 100G PCB Performance Attributes and ApplicationsTachyon 100G PCB Performance Attributes and Applications
Tachyon 100G PCB Performance Attributes and ApplicationsEpec Engineered Technologies
 
Modelling Guide for Timber Structures - FPInnovations
Modelling Guide for Timber Structures - FPInnovationsModelling Guide for Timber Structures - FPInnovations
Modelling Guide for Timber Structures - FPInnovationsYusuf Yıldız
 
Popular-NO1 Kala Jadu Expert Specialist In Germany Kala Jadu Expert Specialis...
Popular-NO1 Kala Jadu Expert Specialist In Germany Kala Jadu Expert Specialis...Popular-NO1 Kala Jadu Expert Specialist In Germany Kala Jadu Expert Specialis...
Popular-NO1 Kala Jadu Expert Specialist In Germany Kala Jadu Expert Specialis...Amil baba
 
Summer training report on BUILDING CONSTRUCTION for DIPLOMA Students.pdf
Summer training report on BUILDING CONSTRUCTION for DIPLOMA Students.pdfSummer training report on BUILDING CONSTRUCTION for DIPLOMA Students.pdf
Summer training report on BUILDING CONSTRUCTION for DIPLOMA Students.pdfNaveenVerma126
 
specification estimation and valuation of a building
specification estimation and valuation of a buildingspecification estimation and valuation of a building
specification estimation and valuation of a buildingswethasekhar5
 
The relationship between iot and communication technology
The relationship between iot and communication technologyThe relationship between iot and communication technology
The relationship between iot and communication technologyabdulkadirmukarram03
 
Graphics Primitives and CG Display Devices
Graphics Primitives and CG Display DevicesGraphics Primitives and CG Display Devices
Graphics Primitives and CG Display DevicesDIPIKA83
 
me3493 manufacturing technology unit 1 Part A
me3493 manufacturing technology unit 1 Part Ame3493 manufacturing technology unit 1 Part A
me3493 manufacturing technology unit 1 Part Akarthi keyan
 
Guardians and Glitches: Navigating the Duality of Gen AI in AppSec
Guardians and Glitches: Navigating the Duality of Gen AI in AppSecGuardians and Glitches: Navigating the Duality of Gen AI in AppSec
Guardians and Glitches: Navigating the Duality of Gen AI in AppSecTrupti Shiralkar, CISSP
 
A Seminar on Electric Vehicle Software Simulation
A Seminar on Electric Vehicle Software SimulationA Seminar on Electric Vehicle Software Simulation
A Seminar on Electric Vehicle Software SimulationMohsinKhanA
 
solar wireless electric vechicle charging system
solar wireless electric vechicle charging systemsolar wireless electric vechicle charging system
solar wireless electric vechicle charging systemgokuldongala
 
Quasi-Stochastic Approximation: Algorithm Design Principles with Applications...
Quasi-Stochastic Approximation: Algorithm Design Principles with Applications...Quasi-Stochastic Approximation: Algorithm Design Principles with Applications...
Quasi-Stochastic Approximation: Algorithm Design Principles with Applications...Sean Meyn
 
Design Analysis of Alogorithm 1 ppt 2024.pptx
Design Analysis of Alogorithm 1 ppt 2024.pptxDesign Analysis of Alogorithm 1 ppt 2024.pptx
Design Analysis of Alogorithm 1 ppt 2024.pptxrajesshs31r
 
Introduction to Machine Learning Unit-3 for II MECH
Introduction to Machine Learning Unit-3 for II MECHIntroduction to Machine Learning Unit-3 for II MECH
Introduction to Machine Learning Unit-3 for II MECHC Sai Kiran
 

Recently uploaded (20)

Technical Management of cement industry.pdf
Technical Management of cement industry.pdfTechnical Management of cement industry.pdf
Technical Management of cement industry.pdf
 
Technology Features of Apollo HDD Machine, Its Technical Specification with C...
Technology Features of Apollo HDD Machine, Its Technical Specification with C...Technology Features of Apollo HDD Machine, Its Technical Specification with C...
Technology Features of Apollo HDD Machine, Its Technical Specification with C...
 
Tachyon 100G PCB Performance Attributes and Applications
Tachyon 100G PCB Performance Attributes and ApplicationsTachyon 100G PCB Performance Attributes and Applications
Tachyon 100G PCB Performance Attributes and Applications
 
Lecture 2 .pdf
Lecture 2                           .pdfLecture 2                           .pdf
Lecture 2 .pdf
 
Modelling Guide for Timber Structures - FPInnovations
Modelling Guide for Timber Structures - FPInnovationsModelling Guide for Timber Structures - FPInnovations
Modelling Guide for Timber Structures - FPInnovations
 
Présentation IIRB 2024 Chloe Dufrane.pdf
Présentation IIRB 2024 Chloe Dufrane.pdfPrésentation IIRB 2024 Chloe Dufrane.pdf
Présentation IIRB 2024 Chloe Dufrane.pdf
 
Lecture 2 .pptx
Lecture 2                            .pptxLecture 2                            .pptx
Lecture 2 .pptx
 
Popular-NO1 Kala Jadu Expert Specialist In Germany Kala Jadu Expert Specialis...
Popular-NO1 Kala Jadu Expert Specialist In Germany Kala Jadu Expert Specialis...Popular-NO1 Kala Jadu Expert Specialist In Germany Kala Jadu Expert Specialis...
Popular-NO1 Kala Jadu Expert Specialist In Germany Kala Jadu Expert Specialis...
 
Summer training report on BUILDING CONSTRUCTION for DIPLOMA Students.pdf
Summer training report on BUILDING CONSTRUCTION for DIPLOMA Students.pdfSummer training report on BUILDING CONSTRUCTION for DIPLOMA Students.pdf
Summer training report on BUILDING CONSTRUCTION for DIPLOMA Students.pdf
 
specification estimation and valuation of a building
specification estimation and valuation of a buildingspecification estimation and valuation of a building
specification estimation and valuation of a building
 
The relationship between iot and communication technology
The relationship between iot and communication technologyThe relationship between iot and communication technology
The relationship between iot and communication technology
 
Graphics Primitives and CG Display Devices
Graphics Primitives and CG Display DevicesGraphics Primitives and CG Display Devices
Graphics Primitives and CG Display Devices
 
me3493 manufacturing technology unit 1 Part A
me3493 manufacturing technology unit 1 Part Ame3493 manufacturing technology unit 1 Part A
me3493 manufacturing technology unit 1 Part A
 
Litature Review: Research Paper work for Engineering
Litature Review: Research Paper work for EngineeringLitature Review: Research Paper work for Engineering
Litature Review: Research Paper work for Engineering
 
Guardians and Glitches: Navigating the Duality of Gen AI in AppSec
Guardians and Glitches: Navigating the Duality of Gen AI in AppSecGuardians and Glitches: Navigating the Duality of Gen AI in AppSec
Guardians and Glitches: Navigating the Duality of Gen AI in AppSec
 
A Seminar on Electric Vehicle Software Simulation
A Seminar on Electric Vehicle Software SimulationA Seminar on Electric Vehicle Software Simulation
A Seminar on Electric Vehicle Software Simulation
 
solar wireless electric vechicle charging system
solar wireless electric vechicle charging systemsolar wireless electric vechicle charging system
solar wireless electric vechicle charging system
 
Quasi-Stochastic Approximation: Algorithm Design Principles with Applications...
Quasi-Stochastic Approximation: Algorithm Design Principles with Applications...Quasi-Stochastic Approximation: Algorithm Design Principles with Applications...
Quasi-Stochastic Approximation: Algorithm Design Principles with Applications...
 
Design Analysis of Alogorithm 1 ppt 2024.pptx
Design Analysis of Alogorithm 1 ppt 2024.pptxDesign Analysis of Alogorithm 1 ppt 2024.pptx
Design Analysis of Alogorithm 1 ppt 2024.pptx
 
Introduction to Machine Learning Unit-3 for II MECH
Introduction to Machine Learning Unit-3 for II MECHIntroduction to Machine Learning Unit-3 for II MECH
Introduction to Machine Learning Unit-3 for II MECH
 

Toward Semantic Data Stream - Technologies and Applications

  • 1. Toward Semantic Data Stream Technologies and Applications Raja CHIKY raja.chiky@isep.fr 2013-2014 16/01/20141
  • 2. About me }  Associate professor in Computer Science – LISITE-RDI }  Research interest: Data stream mining, scalability and resource optimization in cloud computing, recommender systems }  Research field: Large scale data management 1. Real-time and distributed processing of various data sources 2. Use semantic technologies to add a semantic layer 3. Distributive and collaborative data mining 4. Optimizing resources in large scale systems Heterogeneous  and   dynamic  data  streams   Heterogeneous  and   sta1c  data   sensors 5. Modeling and validation of complex systems 16/01/20142
  • 3. Outline }  What is a data stream ? }  Data stream management systems }  New applications }  Semantic Data Stream }  Conclusion 16/01/20143
  • 4. What is a data stream? }  Golab & Oszu (2003): “A data stream is a real-time, continuous, ordered (implicitly by arrival time or explicitly by timestamp) sequence of items. It is impossible to control the order in which items arrive, nor is it feasible to locally store a stream in its entirety.” }  Massive volumes of structured data, items arrive at a high rate. 16/01/20144
  • 5. Data Stream Management Systems DBMS DSMS Data model Permanent updatable relations Streams and permanent updatable relations Storage Data is stored on disk Permanent relations are stored on disk Streams are processed on the fly Query SQL language Creating structures Inserting/updating/deleting data Retrieving data (one-time query) SQL-like query language Standard SQL on permanent relations Extended SQL on streams with windowing Continuous queries Performance Large volumes of data Optimization of computer resources to deal with Several streams Several queries Ability to face variations in arrival rates without crash 16/01/20145
  • 7. World of data streams Website logs Network monitoring Financial services eCommerce Traffic control Power consumption Weather forecasting 16/01/20147
  • 8. New applications : 360-degree view of the customer Why?   What?   How?   When/ Where?   Who?   Opera1onal   data   Behavioral   data   Descrip1ve   data   Interac1on   data   Contextual   data   16/01/20148
  • 9. New applications : 360-degree view of the customer (Contd.) Descrip1ve   data   •  Profile   •  Contractual   data   Interac1on   data   •  Mul1-­‐chanel   •  Email/chat/ call  center   Opera1onal   data   •  Orders     •  Transac1ons   •  Payment   history   Behavioral   data   •  Web  clicks   •  Usage   history   •  Social  media   •  Opinions   Contextual   data   •  Geographical   data   •  Devices   Internal  and  External  sources   16/01/20149
  • 10. New applications: Social networks }  Twitter:A massive Data stream }  Free social networking and micro-blogging service }  3 billion requests per day }  Sentiment Analysis }  Tracking trending hashtags }  Detect breaking news }  Analyze Marketing campaigns Tag1 Tag2 Tag2Tag1 Time 16/01/201410
  • 11. }  E.g., Twitter + MetaCarta [source: http://blog.blprnt.com/blog/blprnt/just-landed-processing-twitter-metacarta-hidden-data ] 16/01/201412 Combination of stream and static data
  • 12. }  BOTTARI: An augmented reality application for personalized recommendation of restaurants in Seoul What have you seen? 16/01/201413 E. Della valle’s talk , LarKC
  • 13. Too much data streams 16/01/201414
  • 14. Semantic Web, Linked Data }  Semantic Web }  Collaborative movement to promote common data formats on the World Wide Web }  Inclusion of semantic content in web pages }  From unstructured and semi-structured documents to a « Web of data » }  Linked Data }  Best practices to represent, publish, link data on the Semantic Web }  Linked Data Cloud: collection of datasets that have been published in Linked data format 16/01/201415
  • 16. Semantic Web technologies for data stream }  Annotate stream data with semantic metadata }  Apply Linked Data principles to publish streaming data }  Interlink streaming data with existing datasets }  Integrate data stream processing + reasoning }  Objectives : interoperability, automation, enrichment 16/01/201417
  • 18. Introduction to RDF }  Statement: "The author of http://www.w3schools.com/rdf is Jan Egil Refsnes” }  The subject of the statement above is: http:// www.w3schools.com/rdf }  The predicate is: author }  The object is: Jan Egil Refsnes 19 Subject Object predicate Predicate 16/01/2014
  • 19. RDF Stream }  RDF Stream is defined as an ordered sequence of pair }  Each pair consists of a RDF triplet and a timestamp }  Example (< :traveller1 :justLanded :placeA >, T1) (< :traveller2 :justLanded :placeB >, T1) (< :traveller3 :justLanded :placeA >, T2) (< :traveller1 :justLanded :placeC >, T3) 16/01/201420
  • 20. RDF Stream – Several definitions }  Several possibilities: }  An RDF stream is an infinite sequence of timestamped events (triples or graphs), … <<s,p,o>i,ti > <<s,p,o>i+1,ti+1 > <<s,p,o>i+2,ti+2 > … }  An RDF stream is an infinite sequence of triple occurrences <<s,p,o>,tα,tω> where <s,p,o> is an RDF triple and tα and tω are the start and end of the interval 16/01/201421
  • 21. Approaches In RDF Stream models (timestamps, events, time intervals, triple- based, graph- based …) 16/01/201422
  • 22. Example: SRBench ASK WHERE { STREAM <http://www.cwi.nl/SRBench/observations> [RANGE 10800s SLIDE 600s] {?observation om-owl:procedure ?sensor ; om-owl:observedProperty weather:WindSpeed ; om-owl:result [ om-owl:floatValue ?value ] .} } GROUP BY ?sensor HAVING ( AVG(?value) >= "74"^^xsd:float ) Q: Detect if a hurricane has been observed “A hurricane has a sustained wind (for more than 3 hours) of at least 33 metres per second or 74 miles per hour (119 km/h).” ASK FROM STREAM <http://www.cwi.nl/SRBench/observations> [RANGE 3h STEP 10m] WHERE {   ?observation om-owl:procedure ?sensor ; om-owl:observedProperty weather:WindSpeed ; om-owl:result [ om-owl:floatValue ?value ] . } GROUP BY ?sensor HAVING ( AVG(?value) >= "74"^^xsd:float ) 16/01/201423
  • 23. W3C RDF Stream Processing Community }  http://www.w3.org/community/rsp/ 16/01/201424
  • 24. W3C RSP Community Group mission “The mission of the RDF Stream Processing Community Group (RSP) is to define a common model for producing, transmitting and continuously querying RDF Streams. This includes extensions to both RDF and SPARQL for representing streaming data, as well as their semantics. Moreover this work envisions an ecosystem of streaming and static RDF data sources whose data can be combined through standard models, languages and protocols (...)” 16/01/201425
  • 25. Conclusion: Research Challenges }  Benchmarking: LSBench, SRBench }  Data Quality: Dealing with incomplete and noisy data }  Reasoning on streams: formal semantics, efficiency, scalability }  Distributed and parallel processing: data stream sources are distributed in nature }  Scalability: approximation, load shedding, etc. 16/01/201426
  • 26. Thanks for your attention raja.chiky@isep.fr 16/01/201427