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In-Time Aviation Safety Management Challenges and Research for an Evolving Aviation System (2018) / Chapter Skim
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4 System Analytics
Pages 36-43

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From page 36...
... Furthermore, the continually evolving NAS will require development of advanced anomaly and hazard detection algorithms that can operate on large volumes of historical operations data to characterize and predict emergent risks and to reprioritize known risks. In-time safety assessment for a large number of risk factors will require the development of computational architectures for data input and output devices, processing capabilities, and storage that can work with high-volume and high-speed streaming of data from multiple sources.
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From page 37...
... In-time algorithms will require large volumes of heterogeneous, multimodal data, and the ability to process them in in a timely fashion so that an IASMS can monitor ground and air operations and identify and characterize the current state of NAS. Data quality and completeness as well as data fusion will impose requirements on the data-driven state identification methods regarding the ability to process data from multiple sources of varying levels of uncertainty to determine their impact on the reliability of the assessment function as it detects elevated risk states.
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From page 38...
... Computational Architectures Challenge Summary Statement: Existing computational architectures lack the ability to handle large volumes of heterogeneous data and dynamic analytics workflows, both of which are necessary to detect elevated risk states, to detect and characterize emergent risks, and to update the IASMS risk assessment algorithms. Computational architectures typically focus on data sources, storage, computing mechanisms, and the presentation and delivery of results to the user.
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From page 39...
... The centralized SWIM architecture may need to be revamped to set up a shared, distributed computational architecture that includes networked repositories and computational systems to support online IASMS analytics operations that cover ground operations and the airspace. Another key element of this challenge is the need for computational architectures to support multiple data sources and consumers of various components of the data.
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From page 40...
... In addition, this research project faces significant uncertainties regarding the ability to acquire all of the data needed to monitor the NAS, to assess the system state, and to detect elevated risk states. This research project is urgent because in-time algorithms will form the core of the monitoring, detection, prediction, and mitigation tasks of the IASMS.7 Advances in supervised, semi-supervised, and unsupervised machine learning algorithms will be needed to reliably characterize known hazards and to discover new hazards, all while taking into account the multidimensional operational space of aircraft, their flight trajectories, human performance states, key inputs to human performance, and environmental factors.
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From page 41...
... Computational Architectures Research Project Summary Statement: Support the design of data repositories and computational architectures that support online detection of elevated risk states and offline analysis to detect and characterize emergent risks and to update the IASMS risk assessment algorithms. This research project will provide the core infrastructure that will provide the basis for the algorithms used for online and offline elements of an IASMS.
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From page 42...
... Research related to the data layer will address core functionalities, such as data organization, to enable information exchange and fusion and to ensure that all distributed storage devices can support common goals while facilitating fast access and retrieval of the data in both the streaming and stored models of operation. Research related to the computing layer will address the needs for data modeling and query, and it will develop tools that facilitate retrieval of structured and nonstructured data while also supporting advanced computational architectures, such as Spark11 and Storm12 and their future evolutions to 10  The four-layer architecture is a widely used, generic template used in big data applications.
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From page 43...
... This research project will also develop visual and configurable schemes for generating workflows that support the data analysis tool chain: acquisition, data cleaning and alignment, preprocessing, curation, analytics and mining, and generation of actionable information to support automated as well as human-in-the-loop decision making. This project would also investigate secure repositories and computational architectures that scale with the four V's (volume, variety, velocity, and veracity)
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Key Terms

  • big data
  • elevated risk
  • risk states
  • computational architectures
  • machine learning

  • heterogeneous data
  • learning algorithms
  • iasms risk
  • data applications
  • characterize emergent

  • ground operations
  • assessment algorithms
  • varying quality
  • iasms analytics
  • anomaly detection

  • challenge summary
  • previously unknown
  • manned aircraft
  • multiple sources
  • project summary

  • human performance
  • air traffic
  • data fusion
  • data repositories
  • data analytics

  • data quality
  • operational data
  • operations data
  • risk state
  • risk analysis


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