2,876 research outputs found
Managing uncertainty in integrated environmental modelling:the UncertWeb framework
Web-based distributed modelling architectures are gaining increasing recognition as potentially useful tools to build holistic environmental models, combining individual components in complex workflows. However, existing web-based modelling frameworks currently offer no support for managing uncertainty. On the other hand, the rich array of modelling frameworks and simulation tools which support uncertainty propagation in complex and chained models typically lack the benefits of web based solutions such as ready publication, discoverability and easy access. In this article we describe the developments within the UncertWeb project which are designed to provide uncertainty support in the context of the proposed ‘Model Web’. We give an overview of uncertainty in modelling, review uncertainty management in existing modelling frameworks and consider the semantic and interoperability issues raised by integrated modelling. We describe the scope and architecture required to support uncertainty management as developed in UncertWeb. This includes tools which support elicitation, aggregation/disaggregation, visualisation and uncertainty/sensitivity analysis. We conclude by highlighting areas that require further research and development in UncertWeb, such as model calibration and inference within complex environmental models
Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective
Machine Learning models are being deployed as parts of real-world systems
with the upsurge of interest in artificial intelligence. The design,
implementation, and maintenance of such systems are challenged by real-world
environments that produce larger amounts of heterogeneous data and users
requiring increasingly faster responses with efficient resource consumption.
These requirements push prevalent software architectures to the limit when
deploying ML-based systems. Data-oriented Architecture (DOA) is an emerging
concept that equips systems better for integrating ML models. DOA extends
current architectures to create data-driven, loosely coupled, decentralised,
open systems. Even though papers on deployed ML-based systems do not mention
DOA, their authors made design decisions that implicitly follow DOA. The
reasons why, how, and the extent to which DOA is adopted in these systems are
unclear. Implicit design decisions limit the practitioners' knowledge of DOA to
design ML-based systems in the real world. This paper answers these questions
by surveying real-world deployments of ML-based systems. The survey shows the
design decisions of the systems and the requirements these satisfy. Based on
the survey findings, we also formulate practical advice to facilitate the
deployment of ML-based systems. Finally, we outline open challenges to
deploying DOA-based systems that integrate ML models.Comment: Under revie
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