The Experts below are selected from a list of 98901 Experts worldwide ranked by ideXlab platform
R Nikoukhah - One of the best experts on this subject based on the ideXlab platform.
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robust state space filtering under Incremental Model perturbations subject to a relative entropy tolerance
IEEE Transactions on Automatic Control, 2013Co-Authors: Bernard C Levy, R NikoukhahAbstract:This paper considers robust filtering for a nominal Gaussian state-space Model, when a relative entropy tolerance is applied to each time increment of a dynamical Model. The problem is formulated as a dynamic minimax game where the maximizer adopts a myopic strategy. This game is shown to admit a saddle point whose structure is characterized by applying and extending results presented earlier in “Robust least-squares estimation with a relative entropy constraint” (B. C. Levy and R. Nikoukhah, IEEE Trans. Inf. Theory, vol. 50, no. 1, 89-104, Jan. 2004) for static least-squares estimation. The resulting minimax filter takes the form of a risk-sensitive filter with a time varying risk sensitivity parameter, which depends on the tolerance bound applied to the Model dynamics and observations at the corresponding time index. The least-favorable Model is constructed and used to evaluate the performance of alternative filters. Simulations comparing the proposed risk-sensitive filter to a standard Kalman filter show a significant performance advantage when applied to the least-favorable Model, and only a small performance loss for the nominal Model.
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robust state space filtering under Incremental Model perturbations subject to a relative entropy tolerance
arXiv: Optimization and Control, 2010Co-Authors: Bernard C Levy, R NikoukhahAbstract:This paper considers robust filtering for a nominal Gaussian state-space Model, when a relative entropy tolerance is applied to each time increment of a dynamical Model. The problem is formulated as a dynamic minimax game where the maximizer adopts a myopic strategy. This game is shown to admit a saddle point whose structure is characterized by applying and extending results presented earlier in [1] for static least-squares estimation. The resulting minimax filter takes the form of a risk-sensitive filter with a time varying risk sensitivity parameter, which depends on the tolerance bound applied to the Model dynamics and observations at the corresponding time index. The least-favorable Model is constructed and used to evaluate the performance of alternative filters. Simulations comparing the proposed risk-sensitive filter to a standard Kalman filter show a significant performance advantage when applied to the least-favorable Model, and only a small performance loss for the nominal Model.
Koichiro Yamauchi - One of the best experts on this subject based on the ideXlab platform.
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Incremental Model selection and ensemble prediction under virtual concept drifting environments
Evolving Systems, 2011Co-Authors: Koichiro YamauchiAbstract:Model selection for machine learning systems is one of the most important issues to be addressed for obtaining greater generalization capabilities. This paper proposes a strategy to achieve Model selection Incrementally under virtual concept drifting environments, where the distribution of learning samples varies over time. To carry out Incremental Model selection, the system generally uses all the learning samples that have been observed until now. Under virtual concept drifting environments, however, the distribution of the observed samples is considerably different from the distribution of cumulative dataset so that Model selection is usually unsuccessful. To overcome this problem, the author had earlier proposed the weighted objective function and Model-selection criterion based on the predictive input density of the learning samples. Although the previous method described in the author’s previous study shows good performances to some datasets, it occasionally fails to yield appropriate learning results because of the failure in the prediction of the actual input density. To reduce the adverse effect, the method proposed in this paper improves on the previously described method to yield the desired outputs using an ensemble of the constructed radial basis function neural networks (RBFNNs).
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PRICAI - Incremental Model selection and ensemble prediction under virtual concept drifting environments
PRICAI 2010: Trends in Artificial Intelligence, 2010Co-Authors: Koichiro YamauchiAbstract:Model selection for machine learning systems is one of the most important issues to be addressed for obtaining greater generalization capabilities. This paper proposes a strategy to achieve Model selection Incrementally under virtual concept drifting environments, where the distribution of learning samples varies over time. To carry out Incremental Model selection, the system generally uses all the learning samples that have been observed until now. Under virtual concept drifting environments, however, the distribution of the observed samples is considerably different from that under real concept drifting environments so that Model selection is usually unsuccessful. To overcome this problem, the author had earlier proposed the weighted objective function and Model-selection criterion based on the predictive input density of the learning samples. Although the previous method described in the author's previous study shows good performances to some datasets, it occasionally fails to yield appropriate learning results because of the failure in the prediction of the actual input density. To overcome this drawback, the method proposed in this paper improves on the previously described method to yield the desired outputs using an ensemble of the constructed radial basis function neural networks (RBFNNs). Experimental results indicate that the improved method yields a stable performance.
Holger Giese - One of the best experts on this subject based on the ideXlab platform.
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Incremental Model Synchronization for Ecient
2010Co-Authors: Run-time Monitoring, Stephan Hildebrandt, Thomas Vogel, Stefan Neumann, Holger Giese, Basil BeckerAbstract:The Model-driven engineering community has developed ex- pressive Model transformation techniques based on metaModels, which ease the specication of translations between dierent Model types. Thus, it is attractive to also apply these techniques for autonomic and self- adaptive systems at run-time to enable a comprehensive monitoring of their architectures while reducing development eorts. This requires spe- cial solutions for Model transformation techniques as they are applied at run-time instead of their traditional usage at development time. In this paper we present an approach to ease the development of architec- tural monitoring based on Incremental Model synchronization with triple graph grammars. We show that the provided Incremental synchroniza- tion between a running system and Models for dierent self-management capabilities provides a signicantly better compromise between perfor- mance and development costs than manually developed solutions.
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ModelS Workshops - Incremental Model synchronization for efficient run-time monitoring
Models in Software Engineering, 2010Co-Authors: Thomas Vogel, Stephan Hildebrandt, Stefan Neumann, Holger Giese, Basil BeckerAbstract:The Model-driven engineering community has developed expressive Model transformation techniques based on metaModels, which ease the specification of translations between different Model types. Thus, it is attractive to also apply these techniques for autonomic and self-adaptive systems at run-time to enable a comprehensive monitoring of their architectures while reducing development efforts. This requires special solutions for Model transformation techniques as they are applied at run-time instead of their traditional usage at development time. In this paper we present an approach to ease the development of architectural monitoring based on Incremental Model synchronization with triple graph grammars. We show that the provided Incremental synchronization between a running system and Models for different self-management capabilities provides a significantly better compromise between performance and development costs than manually developed solutions.
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From Model transformation to Incremental bidirectional Model synchronization
Software and Systems Modeling, 2009Co-Authors: Holger Giese, Robert WagnerAbstract:The Model-driven software development paradigm requires that appropriate Model transformations are applicable in different stages of the development process. The transformations have to consistently propagate changes between the different involved Models and thus ensure a proper Model synchronization. However, most approaches today do not fully support the requirements for Model synchronization and focus only on classical one-way batch-oriented transformations. In this paper, we present our approach for an Incremental Model transformation which supports Model synchronization. Our approach employs the visual, formal, and bidirectional transformation technique of triple graph grammars. Using this declarative specification formalism, we focus on the efficient execution of the transformation rules and how to achieve an Incremental Model transformation for synchronization purposes. We present an evaluation of our approach and demonstrate that due to the speedup for the Incremental processing in the average case even larger Models can be tackled.
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Incremental Model synchronization for efficient run-time monitoring
CEUR Workshop Proceedings, 2009Co-Authors: Thomas Vogel, Stephan Hildebrandt, Stefan Neumann, Holger Giese, Basil BeckerAbstract:The Model-driven engineering community has developed expressive Model transformation techniques based on metaModels, which ease the specification of translations between different Model types. Thus, it is attractive to also apply these techniques for autonomic and self- adaptive systems at run-time to enable a comprehensive monitoring of their architectures while reducing development efforts. This requires special solutions for Model transformation techniques as they are applied at run-time instead of their traditional usage at development time. In this paper we present an approach to ease the development of architectural monitoring based on Incremental Model synchronization with triple graph grammars. We show that the provided Incremental synchronization between a running system and Models for different self-management capabilities provides a significantly better compromise between performance and development costs than manually developed solutions.
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Incremental Model synchronization with triple graph grammars
Lecture Notes in Computer Science, 2006Co-Authors: Holger Giese, Robert WagnerAbstract:The advent of Model-driven software development has put Model transformations into focus. In practice, Model transformations are expected to be applicable in different stages of a development process and help to consistently propagate changes between the different involved Models which we refer to as Model synchronization. However, most approaches do not fully support the requirements for Model synchronization today and focus only on classical one-way batch-oriented transformations. In this paper, we present our approach for an Incremental Model transformation which supports Model synchronization. Our approach employs the visual, formal, and bidirectional transformation technique of triple graph grammars. Using this declarative specification formalism, we focus on the efficient execution of the transformation rules and present our approach to achieve an Incremental Model transformation for synchronization purposes. We present an evaluation of our approach and demonstrate that due to the speedup for the Incremental processing in the average case even larger Models can be tackled.
Daniel Varro - One of the best experts on this subject based on the ideXlab platform.
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Foundations for Streaming Model Transformations by Complex Event Processing
Software & Systems Modeling, 2018Co-Authors: István Dávid, Istvan Rath, Daniel VarroAbstract:Streaming Model transformations represent a novel class of transformations to manipulate Models whose elements are continuously produced or modified in high volume and with rapid rate of change. Executing streaming transformations requires efficient techniques to recognize activated transformation rules over a live Model and a potentially infinite stream of events. In this paper, we propose foundations of streaming Model transformations by innovatively integrating Incremental Model query, complex event processing (CEP) and reactive (event-driven) transformation techniques. Complex event processing allows to identify relevant patterns and sequences of events over an event stream. Our approach enables event streams to include Model change events which are automatically and continuously populated by Incremental Model queries. Furthermore, a reactive rule engine carries out transformations on identified complex event patterns. We provide an integrated domain-specific language with precise semantics for capturing complex event patterns and streaming transformations together with an execution engine, all of which is now part of the Viatra reactive transformation framework. We demonstrate the feasibility of our approach with two case studies: one in an advanced Model engineering workflow; and one in the context of on-the-fly gesture recognition.
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Road to a reactive and Incremental Model transformation platform: three generations of the VIATRA framework
Software & Systems Modeling, 2016Co-Authors: Daniel Varro, Istvan Rath, Gabor Bergmann, Ábel Hegedüs, Ákos Horváth, Zoltán UjhelyiAbstract:The current release of VIATRA provides open-source tool support for an event-driven, reactive Model transformation engine built on top of highly scalable Incremental graph queries for Models with millions of elements and advanced features such as rule-based design space exploration complex event processing or Model obfuscation. However, the history of the VIATRA Model transformation framework dates back to over 16 years. Starting as an early academic research prototype as part of the M.Sc project of the the first author it first evolved into a Prolog-based engine followed by a family of open-source projects which by now matured into a component integrated into various industrial and open-source tools and deployed over multiple technologies. This invited paper briefly overviews the evolution of the VIATRA/IncQuery family by highlighting key features and illustrating main transformation concepts along an open case study influenced by an industrial project.
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the ttc 2015 train benchmark case for Incremental Model validation
8th Transformation Tool Contest TTC 2015 - A part of the Software Technologies: Applications and Foundations STAF 2015, 2015Co-Authors: Gabor Szarnyas, Oszkar Semerath, Istvan Rath, Daniel VarroAbstract:In Model-driven development of safety-critical systems (like automotive, avionics or railways), wellformedness of Models is repeatedly validated in order to detect design flaws as early as possible. Validation rules are often implemented by a large amount of imperative Model traversal code which makes those rule implementations complicated and hard to maintain. Additionally as Models are rapidly increasing in size and complexity, efficient execution of these operations is challenging for the currently available toolchains. However, checking well-formedness constraints can be interpreted as evaluation of Model queries, and the operations as Model transformations, where the validation task can be specified in a concise way, and executed efficiently. This paper presents a benchmark case and an evaluation framework to systematically assess the scalability of validating and revalidating well-formedness constraints over large Models. The benchmark case defines a typical well-formedness validation scenario in the railway domain including the metaModel, an instance Model generator, and a set of well-formedness constraints captured by queries and repair operations (imitating the work of systems engineers by Model transformations). The benchmark case focuses on the execution time of the query evaluations with a special emphasis on reevaluations, as well as simple repair transformations.
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TTC@STAF - The TTC 2015 Train Benchmark Case for Incremental Model Validation
2015Co-Authors: Gabor Szarnyas, Oszkar Semerath, Istvan Rath, Daniel VarroAbstract:In Model-driven development of safety-critical systems (like automotive, avionics or railways), wellformedness of Models is repeatedly validated in order to detect design flaws as early as possible. Validation rules are often implemented by a large amount of imperative Model traversal code which makes those rule implementations complicated and hard to maintain. Additionally as Models are rapidly increasing in size and complexity, efficient execution of these operations is challenging for the currently available toolchains. However, checking well-formedness constraints can be interpreted as evaluation of Model queries, and the operations as Model transformations, where the validation task can be specified in a concise way, and executed efficiently. This paper presents a benchmark case and an evaluation framework to systematically assess the scalability of validating and revalidating well-formedness constraints over large Models. The benchmark case defines a typical well-formedness validation scenario in the railway domain including the metaModel, an instance Model generator, and a set of well-formedness constraints captured by queries and repair operations (imitating the work of systems engineers by Model transformations). The benchmark case focuses on the execution time of the query evaluations with a special emphasis on reevaluations, as well as simple repair transformations.
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incquery d a distributed Incremental Model query framework in the cloud
Model Driven Engineering Languages and Systems, 2014Co-Authors: Gabor Szarnyas, Istvan Rath, Daniel Varro, Benedek Izso, Denes Harmath, Gabor BergmannAbstract:Queries are the foundations of data intensive applications. In Model-driven software engineering (MDE), Model queries are core technologies of tools and transformations. As software Models are rapidly increasing in size and complexity, traditional tools exhibit scalability issues that decrease productivity and increase costs [17]. While scalability is a hot topic in the database community and recent NoSQL efforts have partially addressed many shortcomings, this happened at the cost of sacrificing the ad-hoc query capabilities of SQL. Unfortunately, this is a critical problem for MDE applications due to their inherent workload complexity. In this paper, we aim to address both the scalability and ad-hoc querying challenges by adapting Incremental graph search techniques – known from the EMF-IncQuery framework – to a distributed cloud infrastructure. We propose a novel architecture for distributed and Incremental queries, and conduct experiments to demonstrate that IncQuery-D, our prototype system, can scale up from a single workstation to a cluster that can handle very large Models and complex Incremental queries efficiently.
Bernard C Levy - One of the best experts on this subject based on the ideXlab platform.
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robust state space filtering under Incremental Model perturbations subject to a relative entropy tolerance
IEEE Transactions on Automatic Control, 2013Co-Authors: Bernard C Levy, R NikoukhahAbstract:This paper considers robust filtering for a nominal Gaussian state-space Model, when a relative entropy tolerance is applied to each time increment of a dynamical Model. The problem is formulated as a dynamic minimax game where the maximizer adopts a myopic strategy. This game is shown to admit a saddle point whose structure is characterized by applying and extending results presented earlier in “Robust least-squares estimation with a relative entropy constraint” (B. C. Levy and R. Nikoukhah, IEEE Trans. Inf. Theory, vol. 50, no. 1, 89-104, Jan. 2004) for static least-squares estimation. The resulting minimax filter takes the form of a risk-sensitive filter with a time varying risk sensitivity parameter, which depends on the tolerance bound applied to the Model dynamics and observations at the corresponding time index. The least-favorable Model is constructed and used to evaluate the performance of alternative filters. Simulations comparing the proposed risk-sensitive filter to a standard Kalman filter show a significant performance advantage when applied to the least-favorable Model, and only a small performance loss for the nominal Model.
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robust state space filtering under Incremental Model perturbations subject to a relative entropy tolerance
arXiv: Optimization and Control, 2010Co-Authors: Bernard C Levy, R NikoukhahAbstract:This paper considers robust filtering for a nominal Gaussian state-space Model, when a relative entropy tolerance is applied to each time increment of a dynamical Model. The problem is formulated as a dynamic minimax game where the maximizer adopts a myopic strategy. This game is shown to admit a saddle point whose structure is characterized by applying and extending results presented earlier in [1] for static least-squares estimation. The resulting minimax filter takes the form of a risk-sensitive filter with a time varying risk sensitivity parameter, which depends on the tolerance bound applied to the Model dynamics and observations at the corresponding time index. The least-favorable Model is constructed and used to evaluate the performance of alternative filters. Simulations comparing the proposed risk-sensitive filter to a standard Kalman filter show a significant performance advantage when applied to the least-favorable Model, and only a small performance loss for the nominal Model.
