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

• http://www2.eecs.berkeley.edu/Pubs/TechRpts/2016/EECS-2016-159.pdf

• 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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