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Dusan Jakovetic
University of Novi Sad, Faculty of Sciences, Serbia
Virtual BenchLearning Webinar
July 8, 2020
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 780787
Industrial-Driven Big Data as
a Self-Service Solution
Brief introduction to I-BiDaaS
I-BiDaaS pipeline, architecture & technologies
I-BiDaaS & BDVA reference model
Benchmarking landscape and opportunities @ I-BiDaaS
Outline
2
Identity card
http://www.ibidaas.eu/ @Ibidaas https://www.linkedin.com/in/i-bidaas/
3
Consortium
1. FOUNDATION FOR RESEARCH AND TECHNOLOGY HELLAS (FORTH)
2. BARCELONA SUPERCOMPUTING CENTER - CENTRO NACIONAL DE
SUPERCOMPUTACION (BSC)
3. IBM ISRAEL - SCIENCE AND TECHNOLOGY LTD (IBM)
4. CENTRO RICERCHE FIAT SCPA (CRF)
5. SOFTWARE AG (SAG)
6. CAIXABANK, S.A (CAIXA)
7. THE UNIVERSITY OF MANCHESTER (UNIMAN)
8. ECOLE NATIONALE DES PONTS ET CHAUSSEES (ENPC)
9. ATOS SPAIN SA (ATOS)
10. AEGIS IT RESEARCH LTD (AEGIS)
11. INFORMATION TECHNOLOGY FOR MARKET LEADERSHIP (ITML)
12. UNIVERSITY OF NOVI SAD FACULTY OF SCIENCES SERBIA (UNSPMF)
13. TELEFONICA INVESTIGACION Y DESARROLLO SA (TID)
4
A complete and safe environment for
methodological big data experimentation
Tool and services to increase the quality of
data analytics
A Big Data as a Self-Service solution
that helps in breaking silos and boosts
EU's data-driven economy
Tools and services for fast ingestion and
consolidation of both realistic and fabricated
data
Tools and services for the management of
heterogeneous infrastructures
Increases impact in research community and
contributes to industrial innovation capacity
5
Key messages
• Expert mode
Analyze your DataUsers
• Import your data
• Self-service mode
Data
• Fabricate Data
• Stream & Batch Analytics
• Expert: Upload your code
• Self-service: Select an
algorithm from the pool
Results
• Visualize the results
• Share models
Do it yourself
In a flexible
manner
Break data silos Safe environment Interact with Big Data
technologies
Increase speed of data
analysis
Cope with the rate of data
asset growth
Intra- and inter-
domain data-flow
Benefits of using I-BiDaaS
• Co-develop mode
I-BiDaaS pipeline
• Co-develop: custom end-to-
end application
• Tokenize data
6
• Expert mode
Analyze your DataUsers
• Import your data
• Self-service mode
Data
• Fabricate Data
• Stream & Batch Analytics
• Expert: Upload your code
• Self-service: Select an
algorithm from the pool
Results
• Visualize the results
• Share models
Do it yourself
In a flexible
manner
Break data silos Safe environment Interact with Big Data
technologies
Increase speed of data
analysis
Cope with the rate of data
asset growth
Intra- and inter-
domain data-flow
Benefits of using I-BiDaaS
• Co-develop mode
• Co-develop: custom end-to-
end application
• Tokenize data
7
Flexible solution
I-BiDaaS pipeline
• Expert mode
Analyze your DataUsers
• Import your data
• Self-service mode
Data
• Fabricate Data
• Stream & Batch Analytics
• Expert: Upload your code
• Self-service: Select an
algorithm from the pool
Results
• Visualize the results
• Share models
Do it yourself
In a flexible
manner
Break data silos Safe environment Interact with Big Data
technologies
Increase speed of data
analysis
Cope with the rate of data
asset growth
Intra- and inter-
domain data-flow
Benefits of using I-BiDaaS
• Co-develop mode
• Co-develop: custom end-to-
end application
• Tokenize data
Data sharing
& breaking silos
8
I-BiDaaS pipeline
• Expert mode
Analyze your DataUsers
• Import your data
• Self-service mode
Data
• Fabricate Data
• Stream & Batch Analytics
• Expert: Upload your code
• Self-service: Select an
algorithm from the pool
Results
• Visualize the results
• Share models
Do it yourself
In a flexible
manner
Break data silos Safe environment Interact with Big Data
technologies
Increase speed of data
analysis
Cope with the rate of data
asset growth
Intra- and inter-
domain data-flow
Benefits of using I-BiDaaS
• Co-develop mode
• Co-develop: custom end-to-
end application
• Tokenize data
9
I-BiDaaS pipeline
• Expert mode
Analyze your DataUsers
• Import your data
• Self-service mode
Data
• Fabricate Data
• Stream & Batch Analytics
• Expert: Upload your code
• Self-service: Select an
algorithm from the pool
Results
• Visualize the results
• Share models
Do it yourself
In a flexible
manner
Break data silos Safe environment Interact with Big Data
technologies
Increase speed of data
analysis
Cope with the rate of data
asset growth
Intra- and inter-
domain data-flow
Benefits of using I-BiDaaS
• Co-develop mode
• Co-develop: custom end-to-
end application
• Tokenize data
10
I-BiDaaS pipeline
The I-BiDaaS solution:
Architecture & technologies
Medium to long term business decisions
Data
Fabrication
Platform
(IBM)
Refined
specifications
for data
fabrication
GPU-accelerated Analytics
(FORTH)
Apama Complex Event
Processing (SAG)
Streaming Analytics
Batch Processing
Advanced ML (UNSPMF)
COMPsProgramming
Model(BSC)
Query Partitioning
Infrastructurelayer: Private cloud; Commodity cluster; GPUs
Pre-defined
Queries
SQL-like
interface
Domain
Language
Programming
API
User Interface
Resource managementand orchestration (ATOS)
Advanced
Visualis.
Advanced IT services for
Big Data processing tasks;
Open source pool of ML
algorithms
Data ingestion
Programming
Interface /
Sequential
Programming
(AEGIS+SAG)
(AEGIS)
COMPsRuntime
(BSC)
Distributed
large scale
layer
Application layer
UniversalMessaging(SAG)
TestData
Fabrication(IBM)
Meta-
data;
Data
descri-
ption
Hecuba tools
(BSC)
Short term decisions
real time alerts
Model structure improvements
Learnedpatterns correlations
11
WP2:
Data, user interface, visualization
Technologies:
• IBM TDF
• SAG UM
• AEGIS AVT
Medium to long term business decisions
Data
Fabrication
Platform
(IBM)
Refined
specifications
for data
fabrication
GPU-accelerated Analytics
(FORTH)
Apama Complex Event
Processing (SAG)
Streaming Analytics
Batch Processing
Advanced ML (UNSPMF)
COMPs Programming
Model (BSC)
Query Partitioning
Infrastructure layer: Private cloud; Commodity cluster; GPUs
Pre-defined
Queries
SQL-like
interface
Domain
Language
Programming
API
User Interface
Resource management and orchestration (ATOS)
Advanced
Visualis.
Advanced IT services for
Big Data processing tasks;
Open source pool of ML
algorithms
Programming
Interface /
Sequential
Programming
(AEGIS+SAG)
(AEGIS)
COMPs Runtime
(BSC)
Distributed
large scale
layer
Application layer
UniversalMessaging(SAG)
DataFabrication
Platform(IBM)
Meta-
data;
Data
descri-
ption
Hecuba tools
(BSC)
Short term decisions
real time alerts
Model structure improvements
Learned patterns correlations
12
http://ibidaas.eu/tools
13
Medium to long term business decisions
Data
Fabrication
Platform
(IBM)
Refined
specifications
for data
fabrication
GPU-accelerated Analytics
(FORTH)
Apama Complex Event
Processing (SAG)
Streaming Analytics
Batch Processing
Advanced ML (UNSPMF)
COMPs Programming
Model (BSC)
Query Partitioning
Infrastructure layer: Private cloud; Commodity cluster; GPUs
Pre-defined
Queries
SQL-like
interface
Domain
Language
Programming
API
User Interface
Resource management and orchestration (ATOS)
Advanced
Visualis.
Advanced IT services for
Big Data processing tasks;
Open source pool of ML
algorithms
Programming
Interface /
Sequential
Programming
(AEGIS+SAG)
(AEGIS)
COMPs Runtime
(BSC)
Distributed
large scale
layer
Application layer
UniversalMessaging(SAG)
DataFabrication
Platform(IBM)
Meta-
data;
Data
descri-
ption
Hecuba tools
(BSC)
Short term decisions
real time alerts
Model structure improvements
Learned patterns correlations
WP3:
Batch analytics
Technologies:
• BSC COMPSs
• BSC Hecuba
• BSC Qbeast
• Advanced ML (UNSPMF)
13
http://ibidaas.eu/tools
14
Medium to long term business decisions
Data
Fabrication
Platform
(IBM)
Refined
specifications
for data
fabrication
GPU-accelerated Analytics
(FORTH)
Apama Complex Event
Processing (SAG)
Streaming Analytics
Batch Processing
Advanced ML (UNSPMF)
COMPs Programming
Model (BSC)
Query Partitioning
Infrastructure layer: Private cloud; Commodity cluster; GPUs
Pre-defined
Queries
SQL-like
interface
Domain
Language
Programming
API
User Interface
Resource management and orchestration (ATOS)
Advanced
Visualis.
Advanced IT services for
Big Data processing tasks;
Open source pool of ML
algorithms
Programming
Interface /
Sequential
Programming
(AEGIS+SAG)
(AEGIS)
COMPs Runtime
(BSC)
Distributed
large scale
layer
Application layer
UniversalMessaging(SAG)
DataFabrication
Platform(IBM)
Meta-
data;
Data
descri-
ption
Hecuba tools
(BSC)
Short term decisions
real time alerts
Model structure improvements
Learned patterns correlations
WP4:
Streaming analytics
Technologies:
• SAG Apama CEP
• FORTH GPU-accel. analytics
14
http://ibidaas.eu/tools
15
Medium to long term business decisions
Data
Fabrication
Platform
(IBM)
Refined
specifications
for data
fabrication
GPU-accelerated Analytics
(FORTH)
Apama Complex Event
Processing (SAG)
Streaming Analytics
Batch Processing
Advanced ML (UNSPMF)
COMPs Programming
Model (BSC)
Query Partitioning
Infrastructure layer: Private cloud; Commodity cluster; GPUs
Pre-defined
Queries
SQL-like
interface
Domain
Language
Programming
API
User Interface
Resource management and orchestration (ATOS)
Advanced
Visualis.
Advanced IT services for
Big Data processing tasks;
Open source pool of ML
algorithms
Programming
Interface /
Sequential
Programming
(AEGIS+SAG)
(AEGIS)
COMPs Runtime
(BSC)
Distributed
large scale
layer
Application layer
UniversalMessaging(SAG)
DataFabrication
Platform(IBM)
Meta-
data;
Data
descri-
ption
Hecuba tools
(BSC)
Short term decisions
real time alerts
Model structure improvements
Learned patterns correlations
WP5:
Resource mgmt & integration
Technologies:
• ATOS Resource mgmt
• ITML integration services
15
http://ibidaas.eu/tools
16
BDVA Reference model
BDV SRIA: European Big Data Value Strategic Research and Innovation Agenda
17
BDVA reference model horizontal concerns
& I-BiDaaS
BDV reference model horizontal concern I-BiDaaS module or platform as a whole
Data visualization and user interaction
Advanced visualization module (AEGIS+SAG);
User interface (AEGIS)
Data analytics
Batch processing module (UNSPMF+BSC);
Streaming analytics module (SAG+FORTH)
Data processing architectures expected
advances according to BDVA SRIA
I-BiDaaS platform
Data protection
FORTH commodity cluster privacy preservation through
commodity hardware (Intel SGX); TDF (IBM) for generation of
realistic synthetic data when real data cannot be uploaded to
cloud or similar systems
Data management
COMPSs runtime (BSC); ATOS resource management and
orchestration module
The Cloud and HPC
(efficient usage of Cloud) ATOS resource management and
orchestration module
Brief introduction to I-BiDaaS
I-BiDaaS pipeline, architecture & technologies
I-BiDaaS & BDVA reference model
Benchmarking landscape and opportunities @ I-BiDaaS
Outline
18
19
Benchmarking: Technology level
I-BiDaaS partner Technology name Big Data pipeline element Current benchmarks
FORTH GPU accelerator technology Data pre-processing, Streaming
Analytics
Custom benchmark (throughput, latency)
BSC COMPSs Sequential programming model for
distributed architectures
Applications (Own use cases)
BSC Hecuba Data management framework with
easy interface
Applications (Own use cases)
BSC Qbeast Multidimensional indexing and
storage
TCP-H
IBM Test Data Fabrication Synthetic test data fabrication Several open source + commercial products (e.g., Grid
tools of CA) / No known benchmarks yet
SAG Apama Streaming Analytics Platform Streaming Analytics Custom benchmark (throughput)
SAG Universal Messaging Message Broker Custom benchmark (throughput)
SAG WebMethods Integration Platform Integration N/A
SAG MashZone Visualization N/A
AEGIS Advanced visualization and monitoring Visualization and interface N/A
UNSPMF Pool of ML algorithms in
COMPSs/Python
Batch analytics Respective MPI implementation; Sklearn
ATOS Resource management and orchestration
module
Resource management N/A
Business Objectives Data Sets Data Size Processing Type Type of Analysis
Telecoms
⁻ improve and optimize
current operations
⁻ Anonymized mobility data
(structured)
⁻ Anonymized call center data
(unstructured)
TB ⁻ batch & streaming ⁻ predictive
⁻ descriptive /
diagnostic
Finance
⁻ improve decision
making
⁻ improve efficiency of Big
Data solutions
⁻ Tokenized online banking
control data (structured)
⁻ Tokenized bank transfer data
(structured)
⁻ Tokenized IP address data
(structured)
PB ⁻ batch
⁻ batch & streaming
⁻ descriptive /
diagnostic
Manufacturing
⁻ improve and optimise
current operations
⁻ improve the quality of
the process and product
⁻ Anonymized SCADA/MES data
(structured)
⁻ Anonymized Aluminum Die-
casting (structured)
GB ⁻ batch
⁻ batch & streaming
⁻ predictive
⁻ diagnostic
Benchmarking: Business, data & analytics level
20
I-BiDaaS
Partner
Use Case Most relevant business KPIs
TID Accurate location prediction with high traffic and visibility - Acquisition of insights on the dynamics of
cellular sectors
- Processing costs (cost reduction)
- Customer satisfaction
TID Optimization of placement of telecommunication equipment
TID Quality of service in Call Centers
CAIXA Enhanced control on online banking - Cost reduction
- Data accessibility
- Time efficiency
- End-to-end execution time (from data request
to data provision)
CAIXA Advanced analysis of bank transfer payment in financial terminal
CAIXA Analysis of relationships through IP addresses
CRF Production process of aluminium die-casting - 3 Product quality levels (High. Medium, Low)
- Overall Equipment Effectiveness (OEE),
- Maintenance cost
- Cost reduction
CRF Maintenance and monitoring of production assets
Benchmarking: Business level
21

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Virtual BenchLearning - I-BiDaaS - Industrial-Driven Big Data as a Self-Service Solution

  • 1. Dusan Jakovetic University of Novi Sad, Faculty of Sciences, Serbia Virtual BenchLearning Webinar July 8, 2020 This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 780787 Industrial-Driven Big Data as a Self-Service Solution
  • 2. Brief introduction to I-BiDaaS I-BiDaaS pipeline, architecture & technologies I-BiDaaS & BDVA reference model Benchmarking landscape and opportunities @ I-BiDaaS Outline 2
  • 3. Identity card http://www.ibidaas.eu/ @Ibidaas https://www.linkedin.com/in/i-bidaas/ 3
  • 4. Consortium 1. FOUNDATION FOR RESEARCH AND TECHNOLOGY HELLAS (FORTH) 2. BARCELONA SUPERCOMPUTING CENTER - CENTRO NACIONAL DE SUPERCOMPUTACION (BSC) 3. IBM ISRAEL - SCIENCE AND TECHNOLOGY LTD (IBM) 4. CENTRO RICERCHE FIAT SCPA (CRF) 5. SOFTWARE AG (SAG) 6. CAIXABANK, S.A (CAIXA) 7. THE UNIVERSITY OF MANCHESTER (UNIMAN) 8. ECOLE NATIONALE DES PONTS ET CHAUSSEES (ENPC) 9. ATOS SPAIN SA (ATOS) 10. AEGIS IT RESEARCH LTD (AEGIS) 11. INFORMATION TECHNOLOGY FOR MARKET LEADERSHIP (ITML) 12. UNIVERSITY OF NOVI SAD FACULTY OF SCIENCES SERBIA (UNSPMF) 13. TELEFONICA INVESTIGACION Y DESARROLLO SA (TID) 4
  • 5. A complete and safe environment for methodological big data experimentation Tool and services to increase the quality of data analytics A Big Data as a Self-Service solution that helps in breaking silos and boosts EU's data-driven economy Tools and services for fast ingestion and consolidation of both realistic and fabricated data Tools and services for the management of heterogeneous infrastructures Increases impact in research community and contributes to industrial innovation capacity 5 Key messages
  • 6. • Expert mode Analyze your DataUsers • Import your data • Self-service mode Data • Fabricate Data • Stream & Batch Analytics • Expert: Upload your code • Self-service: Select an algorithm from the pool Results • Visualize the results • Share models Do it yourself In a flexible manner Break data silos Safe environment Interact with Big Data technologies Increase speed of data analysis Cope with the rate of data asset growth Intra- and inter- domain data-flow Benefits of using I-BiDaaS • Co-develop mode I-BiDaaS pipeline • Co-develop: custom end-to- end application • Tokenize data 6
  • 7. • Expert mode Analyze your DataUsers • Import your data • Self-service mode Data • Fabricate Data • Stream & Batch Analytics • Expert: Upload your code • Self-service: Select an algorithm from the pool Results • Visualize the results • Share models Do it yourself In a flexible manner Break data silos Safe environment Interact with Big Data technologies Increase speed of data analysis Cope with the rate of data asset growth Intra- and inter- domain data-flow Benefits of using I-BiDaaS • Co-develop mode • Co-develop: custom end-to- end application • Tokenize data 7 Flexible solution I-BiDaaS pipeline
  • 8. • Expert mode Analyze your DataUsers • Import your data • Self-service mode Data • Fabricate Data • Stream & Batch Analytics • Expert: Upload your code • Self-service: Select an algorithm from the pool Results • Visualize the results • Share models Do it yourself In a flexible manner Break data silos Safe environment Interact with Big Data technologies Increase speed of data analysis Cope with the rate of data asset growth Intra- and inter- domain data-flow Benefits of using I-BiDaaS • Co-develop mode • Co-develop: custom end-to- end application • Tokenize data Data sharing & breaking silos 8 I-BiDaaS pipeline
  • 9. • Expert mode Analyze your DataUsers • Import your data • Self-service mode Data • Fabricate Data • Stream & Batch Analytics • Expert: Upload your code • Self-service: Select an algorithm from the pool Results • Visualize the results • Share models Do it yourself In a flexible manner Break data silos Safe environment Interact with Big Data technologies Increase speed of data analysis Cope with the rate of data asset growth Intra- and inter- domain data-flow Benefits of using I-BiDaaS • Co-develop mode • Co-develop: custom end-to- end application • Tokenize data 9 I-BiDaaS pipeline
  • 10. • Expert mode Analyze your DataUsers • Import your data • Self-service mode Data • Fabricate Data • Stream & Batch Analytics • Expert: Upload your code • Self-service: Select an algorithm from the pool Results • Visualize the results • Share models Do it yourself In a flexible manner Break data silos Safe environment Interact with Big Data technologies Increase speed of data analysis Cope with the rate of data asset growth Intra- and inter- domain data-flow Benefits of using I-BiDaaS • Co-develop mode • Co-develop: custom end-to- end application • Tokenize data 10 I-BiDaaS pipeline
  • 11. The I-BiDaaS solution: Architecture & technologies Medium to long term business decisions Data Fabrication Platform (IBM) Refined specifications for data fabrication GPU-accelerated Analytics (FORTH) Apama Complex Event Processing (SAG) Streaming Analytics Batch Processing Advanced ML (UNSPMF) COMPsProgramming Model(BSC) Query Partitioning Infrastructurelayer: Private cloud; Commodity cluster; GPUs Pre-defined Queries SQL-like interface Domain Language Programming API User Interface Resource managementand orchestration (ATOS) Advanced Visualis. Advanced IT services for Big Data processing tasks; Open source pool of ML algorithms Data ingestion Programming Interface / Sequential Programming (AEGIS+SAG) (AEGIS) COMPsRuntime (BSC) Distributed large scale layer Application layer UniversalMessaging(SAG) TestData Fabrication(IBM) Meta- data; Data descri- ption Hecuba tools (BSC) Short term decisions real time alerts Model structure improvements Learnedpatterns correlations 11
  • 12. WP2: Data, user interface, visualization Technologies: • IBM TDF • SAG UM • AEGIS AVT Medium to long term business decisions Data Fabrication Platform (IBM) Refined specifications for data fabrication GPU-accelerated Analytics (FORTH) Apama Complex Event Processing (SAG) Streaming Analytics Batch Processing Advanced ML (UNSPMF) COMPs Programming Model (BSC) Query Partitioning Infrastructure layer: Private cloud; Commodity cluster; GPUs Pre-defined Queries SQL-like interface Domain Language Programming API User Interface Resource management and orchestration (ATOS) Advanced Visualis. Advanced IT services for Big Data processing tasks; Open source pool of ML algorithms Programming Interface / Sequential Programming (AEGIS+SAG) (AEGIS) COMPs Runtime (BSC) Distributed large scale layer Application layer UniversalMessaging(SAG) DataFabrication Platform(IBM) Meta- data; Data descri- ption Hecuba tools (BSC) Short term decisions real time alerts Model structure improvements Learned patterns correlations 12 http://ibidaas.eu/tools
  • 13. 13 Medium to long term business decisions Data Fabrication Platform (IBM) Refined specifications for data fabrication GPU-accelerated Analytics (FORTH) Apama Complex Event Processing (SAG) Streaming Analytics Batch Processing Advanced ML (UNSPMF) COMPs Programming Model (BSC) Query Partitioning Infrastructure layer: Private cloud; Commodity cluster; GPUs Pre-defined Queries SQL-like interface Domain Language Programming API User Interface Resource management and orchestration (ATOS) Advanced Visualis. Advanced IT services for Big Data processing tasks; Open source pool of ML algorithms Programming Interface / Sequential Programming (AEGIS+SAG) (AEGIS) COMPs Runtime (BSC) Distributed large scale layer Application layer UniversalMessaging(SAG) DataFabrication Platform(IBM) Meta- data; Data descri- ption Hecuba tools (BSC) Short term decisions real time alerts Model structure improvements Learned patterns correlations WP3: Batch analytics Technologies: • BSC COMPSs • BSC Hecuba • BSC Qbeast • Advanced ML (UNSPMF) 13 http://ibidaas.eu/tools
  • 14. 14 Medium to long term business decisions Data Fabrication Platform (IBM) Refined specifications for data fabrication GPU-accelerated Analytics (FORTH) Apama Complex Event Processing (SAG) Streaming Analytics Batch Processing Advanced ML (UNSPMF) COMPs Programming Model (BSC) Query Partitioning Infrastructure layer: Private cloud; Commodity cluster; GPUs Pre-defined Queries SQL-like interface Domain Language Programming API User Interface Resource management and orchestration (ATOS) Advanced Visualis. Advanced IT services for Big Data processing tasks; Open source pool of ML algorithms Programming Interface / Sequential Programming (AEGIS+SAG) (AEGIS) COMPs Runtime (BSC) Distributed large scale layer Application layer UniversalMessaging(SAG) DataFabrication Platform(IBM) Meta- data; Data descri- ption Hecuba tools (BSC) Short term decisions real time alerts Model structure improvements Learned patterns correlations WP4: Streaming analytics Technologies: • SAG Apama CEP • FORTH GPU-accel. analytics 14 http://ibidaas.eu/tools
  • 15. 15 Medium to long term business decisions Data Fabrication Platform (IBM) Refined specifications for data fabrication GPU-accelerated Analytics (FORTH) Apama Complex Event Processing (SAG) Streaming Analytics Batch Processing Advanced ML (UNSPMF) COMPs Programming Model (BSC) Query Partitioning Infrastructure layer: Private cloud; Commodity cluster; GPUs Pre-defined Queries SQL-like interface Domain Language Programming API User Interface Resource management and orchestration (ATOS) Advanced Visualis. Advanced IT services for Big Data processing tasks; Open source pool of ML algorithms Programming Interface / Sequential Programming (AEGIS+SAG) (AEGIS) COMPs Runtime (BSC) Distributed large scale layer Application layer UniversalMessaging(SAG) DataFabrication Platform(IBM) Meta- data; Data descri- ption Hecuba tools (BSC) Short term decisions real time alerts Model structure improvements Learned patterns correlations WP5: Resource mgmt & integration Technologies: • ATOS Resource mgmt • ITML integration services 15 http://ibidaas.eu/tools
  • 16. 16 BDVA Reference model BDV SRIA: European Big Data Value Strategic Research and Innovation Agenda
  • 17. 17 BDVA reference model horizontal concerns & I-BiDaaS BDV reference model horizontal concern I-BiDaaS module or platform as a whole Data visualization and user interaction Advanced visualization module (AEGIS+SAG); User interface (AEGIS) Data analytics Batch processing module (UNSPMF+BSC); Streaming analytics module (SAG+FORTH) Data processing architectures expected advances according to BDVA SRIA I-BiDaaS platform Data protection FORTH commodity cluster privacy preservation through commodity hardware (Intel SGX); TDF (IBM) for generation of realistic synthetic data when real data cannot be uploaded to cloud or similar systems Data management COMPSs runtime (BSC); ATOS resource management and orchestration module The Cloud and HPC (efficient usage of Cloud) ATOS resource management and orchestration module
  • 18. Brief introduction to I-BiDaaS I-BiDaaS pipeline, architecture & technologies I-BiDaaS & BDVA reference model Benchmarking landscape and opportunities @ I-BiDaaS Outline 18
  • 19. 19 Benchmarking: Technology level I-BiDaaS partner Technology name Big Data pipeline element Current benchmarks FORTH GPU accelerator technology Data pre-processing, Streaming Analytics Custom benchmark (throughput, latency) BSC COMPSs Sequential programming model for distributed architectures Applications (Own use cases) BSC Hecuba Data management framework with easy interface Applications (Own use cases) BSC Qbeast Multidimensional indexing and storage TCP-H IBM Test Data Fabrication Synthetic test data fabrication Several open source + commercial products (e.g., Grid tools of CA) / No known benchmarks yet SAG Apama Streaming Analytics Platform Streaming Analytics Custom benchmark (throughput) SAG Universal Messaging Message Broker Custom benchmark (throughput) SAG WebMethods Integration Platform Integration N/A SAG MashZone Visualization N/A AEGIS Advanced visualization and monitoring Visualization and interface N/A UNSPMF Pool of ML algorithms in COMPSs/Python Batch analytics Respective MPI implementation; Sklearn ATOS Resource management and orchestration module Resource management N/A
  • 20. Business Objectives Data Sets Data Size Processing Type Type of Analysis Telecoms ⁻ improve and optimize current operations ⁻ Anonymized mobility data (structured) ⁻ Anonymized call center data (unstructured) TB ⁻ batch & streaming ⁻ predictive ⁻ descriptive / diagnostic Finance ⁻ improve decision making ⁻ improve efficiency of Big Data solutions ⁻ Tokenized online banking control data (structured) ⁻ Tokenized bank transfer data (structured) ⁻ Tokenized IP address data (structured) PB ⁻ batch ⁻ batch & streaming ⁻ descriptive / diagnostic Manufacturing ⁻ improve and optimise current operations ⁻ improve the quality of the process and product ⁻ Anonymized SCADA/MES data (structured) ⁻ Anonymized Aluminum Die- casting (structured) GB ⁻ batch ⁻ batch & streaming ⁻ predictive ⁻ diagnostic Benchmarking: Business, data & analytics level 20
  • 21. I-BiDaaS Partner Use Case Most relevant business KPIs TID Accurate location prediction with high traffic and visibility - Acquisition of insights on the dynamics of cellular sectors - Processing costs (cost reduction) - Customer satisfaction TID Optimization of placement of telecommunication equipment TID Quality of service in Call Centers CAIXA Enhanced control on online banking - Cost reduction - Data accessibility - Time efficiency - End-to-end execution time (from data request to data provision) CAIXA Advanced analysis of bank transfer payment in financial terminal CAIXA Analysis of relationships through IP addresses CRF Production process of aluminium die-casting - 3 Product quality levels (High. Medium, Low) - Overall Equipment Effectiveness (OEE), - Maintenance cost - Cost reduction CRF Maintenance and monitoring of production assets Benchmarking: Business level 21

Notas del editor

  1. The full name of the I-BiDaaS project is Industrial-driven big data as a self service solution. I-BiDaaS is a European Project, RIA (Research and Innovation Action) that means focused on research but as applied as possible. The duration is 36 months. We started in the beginning of 2018 and we have just completed the 1st year. The I-BiDaaS consortium has 13 partners. A mixture of Universities, Research Centers, SMEs, Industry.
  2. The I-BiDaaS consortium was constructed in such a way as to put together a group of organizations with high complementarity in terms of technical competence, organizational, business and market experience. All the partner share a track record of proven leadership, technical expertise, extensive experience and critical skills in all the key areas required for the project to achieve its objectives. With respect to the ability to carry out the proposed work, all partners in I-BiDaaS have participated in successful EU projects in the past.