Download Bigdata@BTH – Challenges and applications BigData@BTH

2015-04-24
Bigdata@BTH –
Challenges and applications
Håkan Grahn,
Blekinge Institute of Technology
Parisa Yousefi,
Ericsson and Blekinge Institute of Technology
BigData@BTH
•  Research profile financed by the Knowledge
foundation
–  36 msek (KKS) + 15 msek (BTH) + >40 msek
(companies)
–  Sep. 2014 to Dec. 2020
–  11 companies
–  4 departments at BTH
•  Focus on machine learning and data mining,
and efficient implementation of such algorithms
on multicore and cloud system
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Research focus
How shall we design future
scalable systems for big data analytics
in order to achieve a good balance between
performance and resource efficiency
as well as business value?
Research themes Core academic competence in all themes
Theme A:
Big data analytics for decision support
- Business intelligence
- Multi-criteria decision-making
- Descriptive/predictive big data analytics
Theme B:
Big data analytics for image processing
- Image classification
- Image restoration
- Pattern recognition
Theme C: Core technologies
- Data mining and knowledge discovery
- Discovery science
- Machine learning
- Real-time analytics
Theme D: Foundations and enabling technologies
- Multicore and cloud
- Data communication and networks
- Heterogeneous systems - Real-time and scheduling
- Storage systems
- Software architecture and implementation
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Balanced mix of
industry partners
Arkiv Digital AD
Theme B:
Big data analytics for image processing
- Image classification
- Image restoration
- Pattern recognition
Sony
Wireless Maingate Nordic
Noda Intelligent Systems
Ericsson
Telenor
Contribe
Scorett Footware
MMI
Indigo IPEX
Theme A:
Big data analytics for decision support
- Business intelligence
- Multi-criteria decision-making
- Descriptive/predictive big data
analytics
Compuverde
Theme C: Core technologies
- Data mining and knowledge discovery
- Discovery science
- Machine learning
- Real-time analytics
Theme D: Foundations and enabling technologies
- Multicore and cloud
- Data communication and networks
- Heterogeneous systems - Real-time and scheduling
- Storage systems
- Software architecture and implementation
Uniqueness and
competitive edge
Concrete challenges!!
Theme C: Core technologies
- Data mining and knowledge discovery
- Discovery science
- Machine learning
- Real-time analytics
Unique combination!!
Large-scale image processing
and classification
Theme B:
Big data analytics for image processing
- Image classification
- Image restoration
- Pattern recognition
Camera devices
Telecommunication systems
Large distributed systems
Health care domain
Theme A:
Big data analytics for decision support
- Business intelligence
- Multi-criteria decision-making
- Descriptive/predictive big data
analytics
Theme D: Foundations and enabling technologies
- Multicore and cloud
- Data communication and networks
- Heterogeneous systems - Real-time and scheduling
- Storage systems
- Software architecture and implementation
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Industrial
challenges
Results,
knowledge,
products, …
Concrete
projects
Industrial challenges
drive the research agenda
•  IC1: Real-time and large-scale quality assessment of
images
•  IC2: Demand-based hospital staff planning
•  IC3: Customer profiling for personalized strategies &
marketing
•  IC4: Fraud and anomaly detection in large-scale data
sets
•  IC5: Automation and orchestration of cloud-based test
environments
•  IC6: Collection and selection of data for real-time
analysis
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Industrial
challenges
Results,
knowledge,
products, …
IC1 IC2 Concrete
projects
IC3 IC4 IC5 IC6 P1, Theme A X X P2, Theme A X X X P3, Theme B X X P4, Theme C X X X X X P5, Theme C X X X P6, Theme D X X X P7, Theme D X X IC3 IC4 IC5 IC6 IC1: Real-time and large-scale
quality assessment of images
IC1 IC2 P1, Theme A X X P2, Theme A X X X P3, Theme B X X P4, Theme C X X X X X P5, Theme C X X X P6, Theme D X X X P7, Theme D X X 5
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IC1: Real-time and large-scale
quality assessment of images
IC1 IC2 IC3 IC4 IC5 IC6 P1, Theme A X X P2, Theme A X X X P3, Theme B X X P4, Theme C X X X X X P5, Theme C X X X P6, Theme D X X X P7, Theme D X X P3 (B): Efficient media analysis and processing
P4 (C): Efficient ensemble methods for challenging domains
Subprojects –
Addressing the challenges
•  P1 (A): Decision support systems for resource estimation and
allocation
•  P2 (A): Decision support systems for anomaly detection and
visualization
•  P3 (B): Efficient media analysis and processing
•  P4 (C): Efficient ensemble methods for challenging domains
•  P5 (C): Classification and regression in large data streams
•  P6 (D): Data collection and selection in large distributed
environments
•  P7 (D): Resource-efficient automatic orchestration of resources
in cloud systems for big data analytics
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Possible applications in
transport and logistics
•  Distributed data collection, filtering, and
storage, e.g., traffic information
•  Planning and scheduling, e.g., resource
planning, train schedules, maintenance
–  FLOAT - FLexibel Omplanering Av Tåglägen i drift
–  KAJT – Kapacitet i JärnvägsTrafiken
•  Anomaly detection, e.g., strange or unusual
behavior
•  Revenue management, e.g., revenue leakage,
run-away costs
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