Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 29 for “"Model Management"”.
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PROCESS MODEL MANAGEMENT SYSTEM ON THE WWW
… design and implementation of a WWW-based Process Model Management System (WPMM) are described. The WPMM is an extensible system upon which we can build a WWW-based PSEE in the future. Specifically, the WPMM contains a Process Model Repository Subsystem that stores process models for projects and …
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THE USE OF ABSTRACTIONS IN MODEL MANAGEMENT.
The concept of a generalized model management System (GMMS) and its role in a decision support system are discussed. A paradigm for developing a GMMS which integrates artificial intelligence techniques with data management concepts is presented. The paradigm views a GMMS as a knowledge-based …
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Infrastructure for model management and model diagnosis
… including the large variety of available ML models, the time required to train the workflow, difficulty keeping track of workflows built during the modeling process, and the time required for debugging trained workflows. In this thesis, we are primarily interested in two problems with the …
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Traceability and model management with executable and dynamic hierarchical megamodels
Nowadays, model-driven engineering (MDE) promises to ease software development by decreasing the inherent complexity of classical software development. In order to deliver on this promise, MDE increases the level of abstraction and automation, through a consideration of domain-specific models …
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ModelDB : tools for machine learning model management and prediction storage
Building a machine learning model is often an iterative process. Data scientists train hundreds of models before finding a model that meets acceptable criteria. But tracking these models and remembering the insights obtained from them is an arduous task. In this thesis, we present two main systems …
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Applying machine learning to the design of decision support systems for intelligent manufacturing
… (DSS) with inductive learning capability for model management. Simulation is used as the primary environment for modeling manufacturing systems and their processes. We propose an adaptive DSS framework for incorporating machine learning into the real time scheduling of a Flexible Manufacturing …
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Visualizations for model tracking and predictions in machine learning
Building machine learning models is often an exploratory and iterative process. A data scientist frequently builds and trains hundreds of models with different parameters and feature sets in order to find one that meets the desired criteria. However, it can be difficult to keep track of all the …
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Business process model abstraction
Business process models are used within a range of organizational initiatives, where every stakeholder has a unique perspective on a process and demands the respective model. As a consequence, multiple process models capturing the very same business process coexist. Keeping such models in sync is a …
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An automated spatial decision support system for the relocation of Army Reserve Units
… of military readiness. A comparative decision model (based upon Multi-Attribute Utility Theory), augmented by a Geographic Information System (GIS), was designed and implemented in an automated Spatial Decision Support System (SDSS). This SDSS provides a flexible structure that can be …
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A recommender system for e-retail
… responsiveness, durability, scalability, model management, and extensibility. Second, we develop an RS that is capable of serving recommendations in an actual e-retail environment. The design of the RS is an attempt to embody the characteristics mentioned above. In addition, to show how …
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Feature-based design of solids with local composition control
… analyses are presented. The developed model is a multi-level and graph-based representation, thereby allowing for controls on the model validity and efficiency in model management. The representations underlying the composition design features are analytic in nature and therefore …
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Surrogate optimization with algebraic notes and applications within the electromagnetics context
… context, in particular, the magnetoquasistatic model of Maxwell’s theory is discussed. Moreover, relevant points regarding the magnetoquasistatic model’s numerical simulation and numerical optimization are examined. The key notion surrogate optimization is thoroughly elaborated which is …
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Investigation of Model-Based Systems Engineering Integration Challenges and Improvements
… testing, and implementation more difficult. Model Based Systems Engineering (MBSE) promises to help by improving system understanding across stakeholders, managing traceability, complexity, and capacity for design reuse, as well as reducing risk through earlier system validation and …
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Intelligent dialog agent modeling in human-centered artificial intelligence applications
… have been integral in which ways humans and AI models can interact across multiple domains such as healthcare, customer service, and education. With the recent advancements in deep learning and pre-trained language models (PLMs), dialog systems have shown impressive performance in various …
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Model specification and analysis for discrete event simulation
… in the development and utilization of simulation models requires the assistance of a Model Management System to automate much of the process. This work develops one aspect of such system: tools fundamental to the specification and analysis of discrete event models. A model specification language …
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A Study of Autonomous Agents in Decision Support Systems
… providing a means to integrate and sequence the models in the DSS. The architecture used to support this planning agent is described. This new kind of DSS enables not only the monitoring of goals, but also the maintenance of these goals through agent-generated plans. The conclusion summarizes the …
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Automated End-to-End Management of the Deep Learning Lifecycle
… relies on the development of deep learning models--an experimental, iterative process that produces tens to hundreds of models before arriving at a satisfactory result. While there has been a surge in the number of tools and frameworks that aim at facilitating deep learning, the process of …
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Uncertainty-Integrated Surrogate Modeling for Complex System Optimization
<p>Approximation models such as surrogate models provide a tractable substitute to expensive physical simulations and an effective solution to the potential lack of quantitative models of system behavior. These capabilities not only enable the efficient design of complex systems, but is also …
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Interpretation, Identification and Reuse of Models. Theory and algorithms with applications in predictive toxicology.
… developing methodologies that enable existing models to be effectively reused. Results of this thesis are presented in the framework of Quantitative Structural-Activity Relationship (QSAR) models, but their application is much more general. QSAR models relate chemical structures with their …
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Interpretation, Identification and Reuse of Models. Theory and algorithms with applications in predictive toxicology.
… developing methodologies that enable existing models to be effectively reused. Results of this thesis are presented in the framework of Quantitative Structural-Activity Relationship (QSAR) models, but their application is much more general. QSAR models relate chemical structures with their …
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