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Data-Driven Cyber-Physical Systems via Real-Time Stream Analytics and Machine Learning
Ilge Akkaya

Citation
Ilge Akkaya. "Data-Driven Cyber-Physical Systems via Real-Time Stream Analytics and Machine Learning". Technical report, EEECS Dept., University of California, Berkeley, October, 2016.

Abstract
Emerging distributed cyber-physical systems (CPSs) integrate a wide range of heterogeneous components that need to be orchestrated in a dynamic environment. While model-based techniques are commonly used in CPS design, they become inadequate in capturing the complexity as systems become larger and extremely dynamic. The adaptive nature of the systems makes data-driven approaches highly desirable, if not necessary. Traditionally, data-driven systems utilize large volumes of static data sets to extract models and predictions of physical processes. However, in emerging CPS, networked sensors provide continually streaming data, creating an essentially infinite source of information. Processing data in batches is no longer a viable option: streams are most valuable when processed on-line, allowing actionable information to be gathered just as the data becomes available. This fundamental shift from big data to infinite data, while having great potential to enable smarter systems, also poses unique challenges. Computation models that capture the integration of streaming data into CPS design become a key requirement for systems to learn, adapt, and evolve in real-time. This thesis explores methodologies for developing data-driven CPSs that integrate model-based design and real-time stream analytics in a modular way. The key modeling framework to be introduced is the aspect-oriented modeling (AOM) paradigm, which leverages the principle of separation-of-concerns in actor-oriented design. Aspects are useful for representing cross-cutting concerns in complex system architectures, as first introduced by the aspect-oriented programming paradigm in object-oriented design. AOM applies this idea to actor-oriented design, creating aspects that enable representation of modular concerns in a complex system model. In data-driven CPS, the introduced aspects can be leveraged to process streaming data, extract actionable information, and incorporate these into the system workflow in a way that preserves model semantics and modularity. To address information extraction from streaming data, we propose the use of aspects that implement Dynamic Bayesian Network based algorithms for machine learning and optimization. Specifically, we introduce an actor-oriented toolkit that enables dynamics and sensing models to be composed with inference, Bayesian learning, and optimization algorithms, and present comprehensive case studies on cooperative mobile robot control. We additionally study the use of streaming data for control of dynamic networked CPS in the context of home automation, and present an overview of the use cases of aspects in actor-oriented CPS development.

Electronic downloads

Citation formats  
  • HTML
    Ilge Akkaya. <a
    href="http://chess.eecs.berkeley.edu/pubs/1185.html"
    ><i>Data-Driven Cyber-Physical Systems via
    Real-Time Stream Analytics and Machine
    Learning</i></a>, Technical report,  EEECS 
    Dept., University of California, Berkeley, October, 2016.
  • Plain text
    Ilge Akkaya. "Data-Driven Cyber-Physical Systems via
    Real-Time Stream Analytics and Machine Learning".
    Technical report,  EEECS  Dept., University of California,
    Berkeley, October, 2016.
  • BibTeX
    @techreport{Akkaya16_DataDrivenCyberPhysicalSystemsViaRealTimeStreamAnalytics,
        author = {Ilge Akkaya},
        title = {Data-Driven Cyber-Physical Systems via Real-Time
                  Stream Analytics and Machine Learning},
        institution = {EEECS  Dept., University of California, Berkeley},
        month = {October},
        year = {2016},
        abstract = {Emerging distributed cyber-physical systems (CPSs)
                  integrate a wide range of heterogeneous components
                  that need to be orchestrated in a dynamic
                  environment. While model-based techniques are
                  commonly used in CPS design, they become
                  inadequate in capturing the complexity as systems
                  become larger and extremely dynamic. The adaptive
                  nature of the systems makes data-driven approaches
                  highly desirable, if not necessary. Traditionally,
                  data-driven systems utilize large volumes of
                  static data sets to extract models and predictions
                  of physical processes. However, in emerging CPS,
                  networked sensors provide continually streaming
                  data, creating an essentially infinite source of
                  information. Processing data in batches is no
                  longer a viable option: streams are most valuable
                  when processed on-line, allowing actionable
                  information to be gathered just as the data
                  becomes available. This fundamental shift from big
                  data to infinite data, while having great
                  potential to enable smarter systems, also poses
                  unique challenges. Computation models that capture
                  the integration of streaming data into CPS design
                  become a key requirement for systems to learn,
                  adapt, and evolve in real-time. This thesis
                  explores methodologies for developing data-driven
                  CPSs that integrate model-based design and
                  real-time stream analytics in a modular way. The
                  key modeling framework to be introduced is the
                  aspect-oriented modeling (AOM) paradigm, which
                  leverages the principle of separation-of-concerns
                  in actor-oriented design. Aspects are useful for
                  representing cross-cutting concerns in complex
                  system architectures, as first introduced by the
                  aspect-oriented programming paradigm in
                  object-oriented design. AOM applies this idea to
                  actor-oriented design, creating aspects that
                  enable representation of modular concerns in a
                  complex system model. In data-driven CPS, the
                  introduced aspects can be leveraged to process
                  streaming data, extract actionable information,
                  and incorporate these into the system workflow in
                  a way that preserves model semantics and
                  modularity. To address information extraction from
                  streaming data, we propose the use of aspects that
                  implement Dynamic Bayesian Network based
                  algorithms for machine learning and optimization.
                  Specifically, we introduce an actor-oriented
                  toolkit that enables dynamics and sensing models
                  to be composed with inference, Bayesian learning,
                  and optimization algorithms, and present
                  comprehensive case studies on cooperative mobile
                  robot control. We additionally study the use of
                  streaming data for control of dynamic networked
                  CPS in the context of home automation, and present
                  an overview of the use cases of aspects in
                  actor-oriented CPS development.},
        URL = {http://chess.eecs.berkeley.edu/pubs/1185.html}
    }
    

Posted by Mary Stewart on 17 Nov 2016.
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