The Experts below are selected from a list of 1390533 Experts worldwide ranked by ideXlab platform

Jacky L Snoep - One of the best experts on this subject based on the ideXlab platform.

  • seek a systems biology data and Model Management platform
    BMC Systems Biology, 2015
    Co-Authors: Katherine Wolstencroft, Stuart Owen, Olga Krebs, Quyen Nguyen, Natalie J Stanford, Martin Golebiewski, Andreas Weidemann, Meik Bittkowski, David Shockley, Jacky L Snoep
    Abstract:

    Systems biology research typically involves the integration and analysis of heterogeneous data types in order to Model and predict biological processes. Researchers therefore require tools and resources to facilitate the sharing and integration of data, and for linking of data to systems biology Models. There are a large number of public repositories for storing biological data of a particular type, for example transcriptomics or proteomics, and there are several Model repositories. However, this silo-type storage of data and Models is not conducive to systems biology investigations. Interdependencies between multiple omics datasets and between datasets and Models are essential. Researchers require an environment that will allow the Management and sharing of heterogeneous data and Models in the context of the experiments which created them. The SEEK is a suite of tools to support the Management, sharing and exploration of data and Models in systems biology. The SEEK platform provides an access-controlled, web-based environment for scientists to share and exchange data and Models for day-to-day collaboration and for public dissemination. A plug-in architecture allows the linking of experiments, their protocols, data, Models and results in a configurable system that is available 'off the shelf'. Tools to run Model simulations, plot experimental data and assist with data annotation and standardisation combine to produce a collection of resources that support analysis as well as sharing. Underlying semantic web resources additionally extract and serve SEEK metadata in RDF (Resource Description Format). SEEK RDF enables rich semantic queries, both within SEEK and between related resources in the web of Linked Open Data. The SEEK platform has been adopted by many systems biology consortia across Europe. It is a data Management environment that has a low barrier of uptake and provides rich resources for collaboration. This paper provides an update on the functions and features of the SEEK software, and describes the use of the SEEK in the SysMO consortium (Systems biology for Micro-organisms), and the VLN (virtual Liver Network), two large systems biology initiatives with different research aims and different scientific communities.

  • seek a systems biology data and Model Management platform
    BMC Systems Biology, 2015
    Co-Authors: Katherine Wolstencroft, Stuart Owen, Olga Krebs, Quyen Nguyen, Natalie J Stanford, Martin Golebiewski, Andreas Weidemann, Meik Bittkowski, David Shockley, Jacky L Snoep
    Abstract:

    Background Systems biology research typically involves the integration and analysis of heterogeneous data types in order to Model and predict biological processes. Researchers therefore require tools and resources to facilitate the sharing and integration of data, and for linking of data to systems biology Models. There are a large number of public repositories for storing biological data of a particular type, for example transcriptomics or proteomics, and there are several Model repositories. However, this silo-type storage of data and Models is not conducive to systems biology investigations. Interdependencies between multiple omics datasets and between datasets and Models are essential. Researchers require an environment that will allow the Management and sharing of heterogeneous data and Models in the context of the experiments which created them.

Philip A Bernstein - One of the best experts on this subject based on the ideXlab platform.

  • Model Management 2.0: manipulating richer mappings
    2014
    Co-Authors: Philip A Bernstein, Sergey Melnik
    Abstract:

    Model Management is a generic approach to solving problems of data programmability where precisely engineered mappings are required. Applications include data warehousing, e-commerce, object-to-relational wrappers, enterprise information integration, database portals, and report generators. The goal is to develop a Model Management engine that can support tools for all of these applications. The engine supports operations to match schemas, compose mappings, diff schemas, merge schemas, translate schemas into different data Models, and generate data transformations from mappings. Much has been learned about Model Management since it was proposed seven years ago. This leads us to a revised vision that differs from the original in two main respects: the operations must handle more expressive mappings, and the runtime that executes mappings should be added as an important Model Management component. We review what has been learned from recent experience, explain the revised Model Management vision based on that experience, and identify the research problems that the revised vision opens up

  • Model Management Engine for Data Integration with Reverse-Engineering Support
    2008 IEEE 24th International Conference on Data Engineering, 2008
    Co-Authors: Michael N. Gubanov, Philip A Bernstein, Alexander Moshchuk
    Abstract:

    Model Management is a high-level programming language designed to efficiently manipulate schemas and mappings. It is comprised of robust operators that combined in short programs can solve complex metadata-oriented problems in a compact way. For instance, countless enterprise data integration scenarios can be easily expressed in this high-level language thus saving hundreds of development man-hours. Here we present the first Model Management engine that has reverse-engineering support for data integration, which is one of the most pressing metadata-oriented problems. It merges two schemas based on the mappings between them and allows user to correct the result keeping all the mappings in sync automatically. For user it is much more convenient than determining which mappings to correct in order to get desired result. In addition, the engine supports restructuring merging which is important when the sources are structured differently and cannot be mapped directly. While making schema merging fully automatic is not yet possible, our work simplifies and automates this process to make it practical in complex data integration scenarios.

  • Model Management and schema mappings theory and practice
    Very Large Data Bases, 2007
    Co-Authors: Philip A Bernstein
    Abstract:

    We present an overview of a tutorial on Model Management---an approach to solving data integration problems, such as data warehousing, e-commerce, object-to-relational mapping, schema evolution and enterprise information integration. Model Management defines a small set of operations for manipulating schemas and mappings, such as Match, Compose, Inverse, and Merge. The long-term goal is to build generic implementations of the operations that can be applied to a wide variety of data integration problems.

  • rondo a programming platform for generic Model Management
    International Conference on Management of Data, 2003
    Co-Authors: Sergey Melnik, Erhard Rahm, Philip A Bernstein
    Abstract:

    Model Management aims at reducing the amount of programming needed for the development of metadata-intensive applications. We present a first complete prototype of a generic Model Management system, in which high-level operators are used to manipulate Models and mappings between Models. We define the key conceptual structures: Models, morphisms, and selectors, and describe their use and implementation. We specify the semantics of the known Model-Management operators applied to these structures, suggest new ones, and develop new algorithms for implementing the individual operators. We examine the solutions for two Model-Management tasks that involve manipulations of relational schemas, XML schemas, and SQL views.

  • generic Model Management a database infrastructure for schema manipulation
    SWDB, 2003
    Co-Authors: Philip A Bernstein
    Abstract:

    This paper summarizes the Model Management approach to generic schema Management. The goal is to raise the level of abstraction of data manipulation for programmers of design tools and other Model-driven applications. We explain the main concepts of Model Management, report on some recent progress on Model matching algorithms, and sketch a category-theoretic approach to a formal semantics for Model Management.

Katherine Wolstencroft - One of the best experts on this subject based on the ideXlab platform.

  • seek a systems biology data and Model Management platform
    BMC Systems Biology, 2015
    Co-Authors: Katherine Wolstencroft, Stuart Owen, Olga Krebs, Quyen Nguyen, Natalie J Stanford, Martin Golebiewski, Andreas Weidemann, Meik Bittkowski, David Shockley, Jacky L Snoep
    Abstract:

    Systems biology research typically involves the integration and analysis of heterogeneous data types in order to Model and predict biological processes. Researchers therefore require tools and resources to facilitate the sharing and integration of data, and for linking of data to systems biology Models. There are a large number of public repositories for storing biological data of a particular type, for example transcriptomics or proteomics, and there are several Model repositories. However, this silo-type storage of data and Models is not conducive to systems biology investigations. Interdependencies between multiple omics datasets and between datasets and Models are essential. Researchers require an environment that will allow the Management and sharing of heterogeneous data and Models in the context of the experiments which created them. The SEEK is a suite of tools to support the Management, sharing and exploration of data and Models in systems biology. The SEEK platform provides an access-controlled, web-based environment for scientists to share and exchange data and Models for day-to-day collaboration and for public dissemination. A plug-in architecture allows the linking of experiments, their protocols, data, Models and results in a configurable system that is available 'off the shelf'. Tools to run Model simulations, plot experimental data and assist with data annotation and standardisation combine to produce a collection of resources that support analysis as well as sharing. Underlying semantic web resources additionally extract and serve SEEK metadata in RDF (Resource Description Format). SEEK RDF enables rich semantic queries, both within SEEK and between related resources in the web of Linked Open Data. The SEEK platform has been adopted by many systems biology consortia across Europe. It is a data Management environment that has a low barrier of uptake and provides rich resources for collaboration. This paper provides an update on the functions and features of the SEEK software, and describes the use of the SEEK in the SysMO consortium (Systems biology for Micro-organisms), and the VLN (virtual Liver Network), two large systems biology initiatives with different research aims and different scientific communities.

  • seek a systems biology data and Model Management platform
    BMC Systems Biology, 2015
    Co-Authors: Katherine Wolstencroft, Stuart Owen, Olga Krebs, Quyen Nguyen, Natalie J Stanford, Martin Golebiewski, Andreas Weidemann, Meik Bittkowski, David Shockley, Jacky L Snoep
    Abstract:

    Background Systems biology research typically involves the integration and analysis of heterogeneous data types in order to Model and predict biological processes. Researchers therefore require tools and resources to facilitate the sharing and integration of data, and for linking of data to systems biology Models. There are a large number of public repositories for storing biological data of a particular type, for example transcriptomics or proteomics, and there are several Model repositories. However, this silo-type storage of data and Models is not conducive to systems biology investigations. Interdependencies between multiple omics datasets and between datasets and Models are essential. Researchers require an environment that will allow the Management and sharing of heterogeneous data and Models in the context of the experiments which created them.

Dimitrios S Kolovos - One of the best experts on this subject based on the ideXlab platform.

  • Bridging proprietary Modelling and open-source Model Management tools: the case of PTC Integrity Modeller and Epsilon
    Software & Systems Modeling, 2019
    Co-Authors: Athanasios Zolotas, Horacio Hoyos rodriguez, Stuart Hutchesson, Beatriz Sanchez pina, Alan Grigg, Mole Li, Dimitrios S Kolovos
    Abstract:

    While the majority of research on Model-Based Software Engineering revolves around open-source Modelling frameworks such as the Eclipse Modelling Framework, the use of commercial and closed-source Modelling tools such as RSA, Rhapsody, MagicDraw and Enterprise Architect appears to be the norm in industry at present. This technical gap can prohibit industrial users from reaping the benefits of state-of-the-art research-based tools in their practice. In this paper, we discuss an attempt to bridge a proprietary UML Modelling tool (PTC Integrity Modeller), which is used for Model-based development of safety-critical systems at Rolls-Royce, with an open-source family of languages for automated Model Management (Epsilon). We present the architecture of our solution, the challenges we encountered in developing it, and a performance comparison against the tool’s built-in scripting interface. In addition, we use the bridge in a real-world industrial case study that involves the coordination with other bridges between proprietary tools and Epsilon.

  • bridging proprietary Modelling and open source Model Management tools the case of ptc integrity Modeller and epsilon
    Model Driven Engineering Languages and Systems, 2017
    Co-Authors: Athanasios Zolotas, Dimitrios S Kolovos, Richard F Paige, Horacio Hoyos Rodriguez, Stuart Hutchesson
    Abstract:

    While the majority of research on Model-Based Software Engineering revolves around open-source Modelling frameworks such as EMF, the use of commercial and closed-source Modelling tools such as RSA, Rhapsody, MagicDraw and PTC Integrity Modeller appears to be the norm in industry at present. This technical gap can prohibit industrial users from reaping the benefits of state-of-the-art research-based tools in their practice. In this paper, we discuss an attempt to bridge a proprietary UML Modelling tool (PTC Integrity Modeller), which is used for Model-based development of safety-critical systems at Rolls-Royce, with an open-source family of languages for automated Model Management (Epsilon). We present the architecture of our solution, the challenges we encountered in developing it, and a performance comparison against the tool's built-in scripting interface.

  • the design of a conceptual framework and technical infrastructure for Model Management language engineering
    International Conference on Engineering of Complex Computer Systems, 2009
    Co-Authors: Dimitrios S Kolovos, Louis M Rose, Nicholas Drivalos, Fiona A. C. Polack
    Abstract:

    Model Management is the discipline of managing artefacts used in Model-Driven Engineering (MDE). A Model Management framework defines and implements the operations (such as transformation or code generation) required to manipulate MDE artefacts. Modern approaches to Model Management generally implement these operations via domain-specific languages (DSLs). This paper presents and compares the principles behind three approaches to implementing DSLs for Model Management and identifies some of the key differences between DSL engineering in general and for Model Management. It then shows how theory relates to practice by illustrating how DSL design and implementation approaches have been used in practice to build working languages from the Epsilon Model Management framework. A set of questions for guiding the development of new Model Management DSLs is summarised, and data on development costs for the different approaches is presented.

  • the epsilon generation language
    European conference on Model driven architecture-foundations and applications, 2008
    Co-Authors: Louis M Rose, Dimitrios S Kolovos, Richard F Paige, Fiona A. C. Polack
    Abstract:

    We present the Epsilon Generation Language (EGL), a Model-to-text (M2T) transformation language that is a component in a Model Management tool chain. The distinctive features of EGL are described, in particular its novel design which inherits a number of language concepts and logical features from a base Model navigation and modification language. The value of being able to use a M2T language as part of an extensible Model Management tool chain is outlined in a case study, and EGL is compared to other M2T languages.

Meik Bittkowski - One of the best experts on this subject based on the ideXlab platform.

  • seek a systems biology data and Model Management platform
    BMC Systems Biology, 2015
    Co-Authors: Katherine Wolstencroft, Stuart Owen, Olga Krebs, Quyen Nguyen, Natalie J Stanford, Martin Golebiewski, Andreas Weidemann, Meik Bittkowski, David Shockley, Jacky L Snoep
    Abstract:

    Systems biology research typically involves the integration and analysis of heterogeneous data types in order to Model and predict biological processes. Researchers therefore require tools and resources to facilitate the sharing and integration of data, and for linking of data to systems biology Models. There are a large number of public repositories for storing biological data of a particular type, for example transcriptomics or proteomics, and there are several Model repositories. However, this silo-type storage of data and Models is not conducive to systems biology investigations. Interdependencies between multiple omics datasets and between datasets and Models are essential. Researchers require an environment that will allow the Management and sharing of heterogeneous data and Models in the context of the experiments which created them. The SEEK is a suite of tools to support the Management, sharing and exploration of data and Models in systems biology. The SEEK platform provides an access-controlled, web-based environment for scientists to share and exchange data and Models for day-to-day collaboration and for public dissemination. A plug-in architecture allows the linking of experiments, their protocols, data, Models and results in a configurable system that is available 'off the shelf'. Tools to run Model simulations, plot experimental data and assist with data annotation and standardisation combine to produce a collection of resources that support analysis as well as sharing. Underlying semantic web resources additionally extract and serve SEEK metadata in RDF (Resource Description Format). SEEK RDF enables rich semantic queries, both within SEEK and between related resources in the web of Linked Open Data. The SEEK platform has been adopted by many systems biology consortia across Europe. It is a data Management environment that has a low barrier of uptake and provides rich resources for collaboration. This paper provides an update on the functions and features of the SEEK software, and describes the use of the SEEK in the SysMO consortium (Systems biology for Micro-organisms), and the VLN (virtual Liver Network), two large systems biology initiatives with different research aims and different scientific communities.

  • seek a systems biology data and Model Management platform
    BMC Systems Biology, 2015
    Co-Authors: Katherine Wolstencroft, Stuart Owen, Olga Krebs, Quyen Nguyen, Natalie J Stanford, Martin Golebiewski, Andreas Weidemann, Meik Bittkowski, David Shockley, Jacky L Snoep
    Abstract:

    Background Systems biology research typically involves the integration and analysis of heterogeneous data types in order to Model and predict biological processes. Researchers therefore require tools and resources to facilitate the sharing and integration of data, and for linking of data to systems biology Models. There are a large number of public repositories for storing biological data of a particular type, for example transcriptomics or proteomics, and there are several Model repositories. However, this silo-type storage of data and Models is not conducive to systems biology investigations. Interdependencies between multiple omics datasets and between datasets and Models are essential. Researchers require an environment that will allow the Management and sharing of heterogeneous data and Models in the context of the experiments which created them.