Bio-jETI is a platform for the intuitive graphical design and execution of bioinformatics workows composed from heterogeneous remote services. In this paper we use a simple phylogenetic analysis process to show how fo...
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Bio-jETI is a platform for the intuitive graphical design and execution of bioinformatics workows composed from heterogeneous remote services. In this paper we use a simple phylogenetic analysis process to show how formal approaches like model checking and process synthesis can be applied to further support the workow development in Bio-jETI. To unfold their full potential these methods need a comprehensive knowledge base about the domain, containing semantic information about the single services as well as ontological classifications of the used terms. We outline how to systematically integrate these semantic web concepts into our framework and discuss the implications on checking and synthesis.
Agility is a must, in particular for business applications. Complex systems and processes must be continuously updated in order to meet the ever changing market conditions. Continuous Model Driven Engineering is based...
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Agility is a must, in particular for business applications. Complex systems and processes must be continuously updated in order to meet the ever changing market conditions. Continuous Model Driven Engineering is based on our eXtreme Model-Driven Design (XMDD) framework, which has been designed to continuously involve the customer/application expert throughout the whole systems' life cycle including software maintenance and evolution. Conceptually it is based on the One Thing Approach (OTA), which combines the simplicity of the waterfall development paradigm with a maximum of agility. The key to OTA is to view the whole development process simply as a complex hierarchical and interactive decision process, where each stakeholder, including the application expert, is allowed to continuously place his/her decisions in term of constraints. Thus semantically, at any time, the state of the development or evolution process can simply be regarded as the current set of constraints, and each development or evolution step can be regarded simply as a transformation of this very constraint set. This approach, conceptually, allows one 1) to monitor globally and at any time the consistency of the development or evolution process simply via constraint checking, and 2) to impose a kind of decision hierarchy by mapping areas of competencies to roles of individuals, in order to identify required actions in case of constraint violation. The essence and power of this approach, which is technically supported by the jABC development and execution framework, will be illustrated along a number of real life application.
Bio-jETI is a framework for model-based, graphical design, execution and management of bioinformatics analysis processes. Formal methodology like automatic service composition extends the framework and, in particular,...
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Bio-jETI is a framework for model-based, graphical design, execution and management of bioinformatics analysis processes. Formal methodology like automatic service composition extends the framework and, in particular, allows for semantically aware workow development. In this study we apply the workow synthesis methodology to the EMBOSS suite of sequence analysis tools. As neither the tool suite itself nor its various interfaces provide ready-to-use semantic annotations, we set up a domain model that uses a high-level, semantically meaningful type nomenclature to describe the input/output behavior of the single EMBOSS tools. Based on this domain model, we demonstrate how working with the large, heterogeneous, and hence manually intractable EMBOSS collection is simplified by our service composition methodology.
A hidden Markov model (HMM) is a statistical model in which the system being modeled is assumed to be a Markov process with hidden states. This model has been widely used in speech recognition and biological sequence ...
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A hidden Markov model (HMM) is a statistical model in which the system being modeled is assumed to be a Markov process with hidden states. This model has been widely used in speech recognition and biological sequence analysis. Viterbi algorithm has been proposed to compute the most probable value of these hidden states in regards to an observed data sequence. Constrained HMM extends this framework by adding some constraints on a HMM process run. In this paper, we propose to introduce constrained HMMs into Constraint programming. We propose new version of the Viterbi algorithm for this new framework. Several constraint techniques are used to reduce the search of the most probable value of hidden states of a constrained HMM. An implementation based on PRISM, a logic programming language for statistical modeling, is presented.
This paper introduces Stochastic Definite Clause Grammars, a stochastic variant of the wellknown Definite Clause Grammars. The grammar formalism supports parameter learning from annotated or unannotated corpora and pr...
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This paper introduces Stochastic Definite Clause Grammars, a stochastic variant of the wellknown Definite Clause Grammars. The grammar formalism supports parameter learning from annotated or unannotated corpora and provides a mechanism for parse selection by means of statistical inference. Unlike probabilistic contextfree grammars, it is a context-sensitive grammar formalism and it has the ability to model cross-serial dependencies in natural language. SDCG also provides some syntax extensions which makes it possible to write more compact grammars and makes it straight-forward to add lexicalization schemes to a grammar.
This project aims to investigate biologically inspired, logic-statistic models with constraints. The complexity and expressiveness of models with different kinds of constraints will be examined and algorithms to effic...
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Early in the dark days of World War II, President Roosevelt asked the Navy how it would provide the thousands of ships necessary for the numerous amphibious assault landings that were being planned. At a subsequent hi...
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Early in the dark days of World War II, President Roosevelt asked the Navy how it would provide the thousands of ships necessary for the numerous amphibious assault landings that were being planned. At a subsequent high-level meeting, where John Neidermair, the Technical Director of the Preliminary Design Division at BUSHIPS (Bureau of Ships-a predecessor organization to the Naval Sea systems Command-NAVSEA), created a concept design sketch of the now famous LSTs. It was this same BUSHIPS Ship Design organization that designed the US Navy Fleet which defeated the Japanese and German navies. And it was the BUSHIPS successor organization, NAVSEA, which designed the 600-ship Fleet during the President Reagan build-up of the 1980s and early 1990s. This early stage ship design capability to translate the operators' needs into technically feasible ship concepts and designs is still a core responsibility of NAVSEA. However, NAVSEA is now undertaking the grand challenge of rebuilding the Navy's ship design capabilities which were dramatically downsized during the 1990s. The Human Capital Strategy for Ship Design Acquisition Workforce Improvement is a proven road map for reconstituting the Navy's ship design capabilities and reinvigorating the naval ship design community. The Office of Naval Research (ONR) and NAVSEA made significant progress in this direction by establishing the Navy's Center for Innovation in Ship Design (CISD). CISD is accelerating the career development of ship design leaders, and is paving the way for fully implementing a Human Capital Strategy for Ship Design Acquisition Workforce Improvement.
Maintainability, extendibility and reusability of components in the design of robot control architectures is a major challenge. Parallel kinematic robots feature a wide variety of structures and applications. They are...
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In this paper, we present the LearnLib, a library of tools for automata learning, which is explicitly designed for the systematic experimental analysis of the profile of available learning algorithms and corresponding...
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