The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform
D.b Rieu - One of the best experts on this subject based on the ideXlab platform.
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A Process Engineering Method based on a Process Domain Model and Patterns
2017Co-Authors: Charlotte Hug, D.b RieuAbstract:There are many different Process meta-models that offer different viewpoints of a same Process: activity oriented, product oriented, decision oriented, context oriented and strategy oriented. However, the complementarity between their concepts is not explicit and there is no consensus about the concepts themselves. This problem leads to inadequate Process meta-models with organization needs, so the instantiated models do not correspond to the specific demands and constraints of the organizations or projects. However,, method engineers should be able to build Process meta-models according to the specific organization needs. We propose a method to build unified, fitted and multi-viewpoints Process meta-models. The Process engineering method is based on a Process Domain model and on patterns.
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A Process Engineering Method Based on Ontology and Patterns
2017Co-Authors: Charlotte Hug, D.b RieuAbstract:Many different Process meta-models offer different viewpoints of a same information system engineering Process: activity oriented, product oriented, decision oriented, context oriented and strategy oriented. However, the complementarity between their concepts is not explicit and there is no consensus about the concepts themselves. This leads to inadequate Process meta-models with organization needs, so the instantiated models do not correspond to the specific demands and constraints of the organizations or projects. Nevertheless, method engineers should be able to build Process meta-models according to the specific organization needs. We propose a method to build unified, fitted and multi-viewpoints Process meta-models. The method is composed of two phases and is based on a Process Domain ontology and patterns.
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A method to build information systems engineering Process metamodels
Journal of Systems and Software, 2009Co-Authors: Agnès Front, D.b Rieu, Brian Henderson-sellersAbstract:Several Process metamodels exist. Each of them presents a different viewpoint of the same information systems engineering Process. However, there are no existing correspondences between them. We propose a method to build unified, fitted and multi-viewpoint Process metamodels for information systems engineering. Our method is based on a Process Domain metamodel that contains the main concepts of information systems engineering Process field. This Process Domain metamodel helps selecting the needed metamodel concepts for a particular situational context. Our method is also based on patterns to refine the Process metamodel. The Process metamodel can then be instantiated according to the organisation's needs. The resulting method is represented as a pattern system.
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A method to build information systems engineering Process metamodels
Journal of Systems and Software, 2009Co-Authors: Charlotte Hug, D.b Rieu, Agnès Front, Brian Henderson-sellersAbstract:Several Process metamodels exist. Each of them presents a different viewpoint of the same information systems engineering Process. However, there are no existing correspondences between them. We propose a method to build unified, fitted and multi-viewpoint Process metamodels for information systems engineering. Our method is based on a Process Domain metamodel that contains the main concepts of information systems engineering Process field. This Process Domain metamodel helps selecting the needed metamodel concepts for a particular situational context. Our method is also based on patterns to refine the Process metamodel. The Process metamodel can then be instantiated according to the organisation's needs. The resulting method is represented as a pattern system. © 2009 Elsevier Inc. All rights reserved.
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MoDISE-EUS - A Process Engineering Method based on a Process Domain Model and Patterns
2008Co-Authors: Agnès Front, D.b RieuAbstract:There are many different Process meta-models that offer different viewpoints of a same Process: activity oriented, product oriented, decision oriented, context oriented and strategy oriented. However, the complementarity between their concepts is not explicit and there is no consensus about the concepts themselves. This problem leads to inadequate Process meta-models with organization needs, so the instantiated models do not correspond to the specific demands and constraints of the organizations or projects. However,, method engineers should be able to build Process metamodels according to the specific organization needs. We propose a method to build unified, fitted and multi-viewpoints Process meta-models. The Process engineering method is based on a Process Domain model and on patterns.
Elyes Lamine - One of the best experts on this subject based on the ideXlab platform.
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Semantic-based model analysis towards enhancing information values of Process mining: Case study of learning Process Domain
Advances in Intelligent Systems and Computing, 2018Co-Authors: Kingsley Okoye, Abdel-rahman H. Tawil, Usman Naeem, Syed Islam, Umair Naeem, Elyes LamineAbstract:© Springer International Publishing AG 2018. Process mining results can be enhanced by adding semantic knowledge to the derived models. Information discovered due to semantic enrichment of the deployed Process models can be used to lift Process analysis from syntactic level to a more conceptual level. The work in this paper corroborates that semantic-based Process mining is a useful technique towards improving the information value of derived models from the large volume of event logs about any Process Domain. We use a case study of learning Process to illustrate this notion. Our goal is to extract streams of event logs from a learning execution environment and describe formats that allows for mining and improved Process analysis of the captured data. The approach involves mapping of the resulting learning model derived from mining event data about a learning Process by semantically annotating the Process elements with concepts they represent in real time using Process descriptions languages, and linking them to an ontology specifically designed for representing learning Processes. The semantic analysis allows the meaning of the learning objects to be enhanced through the use of property characteristics and classification of discoverable entities, to generate inference knowledge which are used to determine useful learning patterns by means of the Semantic Learning Process Mining (SLPM) algorithm - technically described as Semantic-Fuzzy Miner. To this end, we show how data from learning Processes are being extracted, semantically prepared, and transformed into mining executable formats to enable prediction of individual learning patterns through further semantic analysis of the discovered models.
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SoCPaR - Semantic-Based Model Analysis Towards Enhancing Information Values of Process Mining: Case Study of Learning Process Domain
Advances in Intelligent Systems and Computing, 2017Co-Authors: Kingsley Okoye, Abdel-rahman H. Tawil, Usman Naeem, Syed Islam, Elyes LamineAbstract:Process mining results can be enhanced by adding semantic knowledge to the derived models. Information discovered due to semantic enrichment of the deployed Process models can be used to lift Process analysis from syntactic level to a more conceptual level. The work in this paper corroborates that semantic-based Process mining is a useful technique towards improving the information value of derived models from the large volume of event logs about any Process Domain. We use a case study of learning Process to illustrate this notion. Our goal is to extract streams of event logs from a learning execution environment and describe formats that allows for mining and improved Process analysis of the captured data. The approach involves mapping of the resulting learning model derived from mining event data about a learning Process by semantically annotating the Process elements with concepts they represent in real time using Process descriptions languages, and linking them to an ontology specifically designed for representing learning Processes. The semantic analysis allows the meaning of the learning objects to be enhanced through the use of property characteristics and classification of discoverable entities, to generate inference knowledge which are used to determine useful learning patterns by means of the Semantic Learning Process Mining (SLPM) algorithm - technically described as Semantic-Fuzzy Miner. To this end, we show how data from learning Processes are being extracted, semantically prepared, and transformed into mining executable formats to enable prediction of individual learning patterns through further semantic analysis of the discovered models.
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Semantic-Based Model Analysis Towards Enhancing Information Values of Process Mining: Case Study of Learning Process Domain
2016Co-Authors: Kingsley Okoye, Abdel-rahman H. Tawil, Usman Naeem, Syed Islam, Elyes LamineAbstract:Process mining results can be enhanced by adding semantic knowledge to the derived models. Information discovered due to semantic enrichment of the deployed Process models can be used to lift Process analysis from syntactic level to a more conceptual level. The work in this paper corroborates that semantic-based Process mining is a useful technique towards improving the information value of derived models from the large volume of event logs about any Process Domain. We use a case study of learning Process to illustrate this notion. Our goal is to extract streams of event logs from a learning execution environment and describe formats that allows for mining and improved Process analysis of the captured data. The approach involves mapping of the resulting learning model derived from mining event data about a learning Process by semantically annotating the Process elements with concepts they represent in real time using Process descriptions languages, and linking them to an ontology specifically designed for representing learning Processes. The semantic analysis allows the meaning of the learning objects to be enhanced through the use of property characteristics and classification of discoverable entities, to generate inference knowledge which are used to determine useful learning patterns by means of the Semantic Learning Process Mining (SLPM) algorithm - technically described as Semantic-Fuzzy Miner. To this end, we show how data from learning Processes are being extracted, semantically prepared, and transformed into mining executable formats to enable prediction of individual learning patterns through further semantic analysis of the discovered models.
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BigData - Using semantic-based approach to manage perspectives of Process mining: Application on improving learning Process Domain data
2016 IEEE International Conference on Big Data (Big Data), 2016Co-Authors: Okoye Kingsley, Abdel-rahman H. Tawil, Usman Naeem, Syed Islam, Elyes LamineAbstract:Mining useful knowledge from data readily available in today's information systems has been a common challenge in recent years as more and more events are being recorded, and there is need to improve and support many organisational Processes in a competitive and rapidly changing environments. The work in this paper shows using a case study of Learning Process — how data from various Process Domains can be extracted, semantically prepared, and transformed into mining executable formats to support the discovery, monitoring and enhancement of real-time Processes. In so doing, it enables the prediction of individual patterns/behaviour through further semantic analysis of the discovered models. Our aim is to extract streams of event logs from a learning execution environment and describe formats that allows for mining and improved Process analysis of the captured data. The approach involves augmenting the informative value of the resulting model derived from mining event data about the Process by semantically annotating the Process elements with concepts they represent in real time using Process descriptions languages, and linking them to an ontology specifically designed for representing learning Processes to allow for the analysis of the extracted event logs based on concepts rather than the event tags of the Process. The semantic analysis allows the meaning of the learning object properties and model to be enhanced through the use of property characteristics and classification of discoverable entities, to generate inference knowledge which are then used to determine useful learning patterns by means of the proposed Semantic Learning Process Mining (SLPM) formalization — described technically as Semantic-Fuzzy Miner. As a result, the approach provides us with the capability to infer new and discover hidden relationships/attributes the Process instances share amongst themselves within the knowledge base, and the ability to identify and address the problem of determining the presence of different learning patterns or behaviour. Inference knowledge discovered due to semantic enrichment of the Process model is advantageous especially in solving some didactic issues and answering some questions with regards to different Learners behaviour within the context of Process mining and semantic model analysis. To this end, we show that information derived from Process mining algorithms can be improved by adding semantic knowledge to the resulting model.
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Using semantic-based approach to manage perspectives of Process mining: Application on improving learning Process Domain data
Proceedings - 2016 IEEE International Conference on Big Data Big Data 2016, 2016Co-Authors: Okoye Kingsley, Abdel-rahman H. Tawil, Syed Islam, Umair Naeem, Elyes LamineAbstract:Mining useful knowledge from data readily available in today's information systems has been a common challenge in recent years as more and more events are being recorded, and there is need to improve and support many organisational Processes in a competitive and rapidly changing environments. The work in this paper shows using a case study of Learning Process - how data from various Process Domains can be extracted, semantically prepared, and transformed into mining executable formats to support the discovery, monitoring and enhancement of real-time Processes. In so doing, it enables the prediction of individual patterns/behaviour through further semantic analysis of the discovered models. Our aim is to extract streams of event logs from a learning execution environment and describe formats that allows for mining and improved Process analysis of the captured data. The approach involves augmenting the informative value of the resulting model derived from mining event data about the Process by semantically annotating the Process elements with concepts they represent in real time using Process descriptions languages, and linking them to an ontology specifically designed for representing learning Processes to allow for the analysis of the extracted event logs based on concepts rather than the event tags of the Process. The semantic analysis allows the meaning of the learning object properties and model to be enhanced through the use of property characteristics and classification of discoverable entities, to generate inference knowledge which are then used to determine useful learning patterns by means of the proposed Semantic Learning Process Mining (SLPM) formalization - described technically as Semantic-Fuzzy Miner. As a result, the approach provides us with the capability to infer new and discover hidden relationships/attributes the Process instances share amongst themselves within the knowledge base, and the ability to identify and address the problem of determining the presence of different learning patterns or behaviour. Inference knowledge discovered due to semantic enrichment of the Process model is advantageous especially in solving some didactic issues and answering some questions with regards to different Learners behaviour within the context of Process mining and semantic model analysis. To this end, we show that information derived from Process mining algorithms can be improved by adding semantic knowledge to the resulting model.
Agnès Front - One of the best experts on this subject based on the ideXlab platform.
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A method to build information systems engineering Process metamodels
Journal of Systems and Software, 2009Co-Authors: Agnès Front, D.b Rieu, Brian Henderson-sellersAbstract:Several Process metamodels exist. Each of them presents a different viewpoint of the same information systems engineering Process. However, there are no existing correspondences between them. We propose a method to build unified, fitted and multi-viewpoint Process metamodels for information systems engineering. Our method is based on a Process Domain metamodel that contains the main concepts of information systems engineering Process field. This Process Domain metamodel helps selecting the needed metamodel concepts for a particular situational context. Our method is also based on patterns to refine the Process metamodel. The Process metamodel can then be instantiated according to the organisation's needs. The resulting method is represented as a pattern system.
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A method to build information systems engineering Process metamodels
Journal of Systems and Software, 2009Co-Authors: Charlotte Hug, D.b Rieu, Agnès Front, Brian Henderson-sellersAbstract:Several Process metamodels exist. Each of them presents a different viewpoint of the same information systems engineering Process. However, there are no existing correspondences between them. We propose a method to build unified, fitted and multi-viewpoint Process metamodels for information systems engineering. Our method is based on a Process Domain metamodel that contains the main concepts of information systems engineering Process field. This Process Domain metamodel helps selecting the needed metamodel concepts for a particular situational context. Our method is also based on patterns to refine the Process metamodel. The Process metamodel can then be instantiated according to the organisation's needs. The resulting method is represented as a pattern system. © 2009 Elsevier Inc. All rights reserved.
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MoDISE-EUS - A Process Engineering Method based on a Process Domain Model and Patterns
2008Co-Authors: Agnès Front, D.b RieuAbstract:There are many different Process meta-models that offer different viewpoints of a same Process: activity oriented, product oriented, decision oriented, context oriented and strategy oriented. However, the complementarity between their concepts is not explicit and there is no consensus about the concepts themselves. This problem leads to inadequate Process meta-models with organization needs, so the instantiated models do not correspond to the specific demands and constraints of the organizations or projects. However,, method engineers should be able to build Process metamodels according to the specific organization needs. We propose a method to build unified, fitted and multi-viewpoints Process meta-models. The Process engineering method is based on a Process Domain model and on patterns.
Brian Henderson-sellers - One of the best experts on this subject based on the ideXlab platform.
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A method to build information systems engineering Process metamodels
Journal of Systems and Software, 2009Co-Authors: Agnès Front, D.b Rieu, Brian Henderson-sellersAbstract:Several Process metamodels exist. Each of them presents a different viewpoint of the same information systems engineering Process. However, there are no existing correspondences between them. We propose a method to build unified, fitted and multi-viewpoint Process metamodels for information systems engineering. Our method is based on a Process Domain metamodel that contains the main concepts of information systems engineering Process field. This Process Domain metamodel helps selecting the needed metamodel concepts for a particular situational context. Our method is also based on patterns to refine the Process metamodel. The Process metamodel can then be instantiated according to the organisation's needs. The resulting method is represented as a pattern system.
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A method to build information systems engineering Process metamodels
Journal of Systems and Software, 2009Co-Authors: Charlotte Hug, D.b Rieu, Agnès Front, Brian Henderson-sellersAbstract:Several Process metamodels exist. Each of them presents a different viewpoint of the same information systems engineering Process. However, there are no existing correspondences between them. We propose a method to build unified, fitted and multi-viewpoint Process metamodels for information systems engineering. Our method is based on a Process Domain metamodel that contains the main concepts of information systems engineering Process field. This Process Domain metamodel helps selecting the needed metamodel concepts for a particular situational context. Our method is also based on patterns to refine the Process metamodel. The Process metamodel can then be instantiated according to the organisation's needs. The resulting method is represented as a pattern system. © 2009 Elsevier Inc. All rights reserved.
Maja Pantic - One of the best experts on this subject based on the ideXlab platform.
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Gaussian Process Domain Experts for Modeling of Facial Affect
IEEE Transactions on Image Processing, 2017Co-Authors: Stefanos Eleftheriadis, Ognjen Rudovic, Marc Peter Deisenroth, Maja PanticAbstract:Most of existing models for facial behavior analysis rely on generic classifiers, which fail to generalize well to previously unseen data. This is because of inherent differences in source (training) and target (test) data, mainly caused by variation in subjects' facial morphology, camera views, and so on. All of these account for different contexts in which target and source data are recorded, and thus, may adversely affect the performance of the models learned solely from source data. In this paper, we exploit the notion of Domain adaptation and propose a data efficient approach to adapt already learned classifiers to new unseen contexts. Specifically, we build upon the probabilistic framework of Gaussian Processes (GPs), and introduce Domain-specific GP experts (e.g., for each subject). The model adaptation is facilitated in a probabilistic fashion, by conditioning the target expert on the predictions from multiple source experts. We further exploit the predictive variance of each expert to define an optimal weighting during inference. We evaluate the proposed model on three publicly available data sets for multi-class (MultiPIE) and multi-label (DISFA, FERA2015) facial expression analysis by performing adaptation of two contextual factors: “where” (view) and “who” (subject). In our experiments, the proposed approach consistently outperforms: 1) both source and target classifiers, while using a small number of target examples during the adaptation and 2) related state-of-the-art approaches for supervised Domain adaptation.
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CVPR Workshops - Gaussian Process Domain Experts for Model Adaptation in Facial Behavior Analysis
2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2016Co-Authors: Stefanos Eleftheriadis, Ognjen Rudovic, Marc Peter Deisenroth, Maja PanticAbstract:We present a novel approach for supervised Domain adaptation that is based upon the probabilistic framework of Gaussian Processes (GPs). Specifically, we introduce Domain-specific GPs as local experts for facial expression classification from face images. The adaptation of the classifier is facilitated in probabilistic fashion by conditioning the target expert on multiple source experts. Furthermore, in contrast to existing adaptation approaches, we also learn a target expert from available target data solely. Then, a single and confident classifier is obtained by combining the predictions from multiple experts based on their confidence. Learning of the model is efficient and requires no retraining/ reweighting of the source classifiers. We evaluate the proposed approach on two publicly available datasets for multi-class (MultiPIE) and multi-label (DISFA) facial expression classification. To this end, we perform adaptation of two contextual factors: 'where' (view) and 'who' (subject). We show in our experiments that the proposed approach consistently outperforms both source and target classifiers, while using as few as 30 target examples. It also outperforms the state-of-the-art approaches for supervised Domain adaptation.
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Gaussian Process Domain Experts for Model Adaptation in Facial Behavior Analysis
2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2016Co-Authors: Stefanos Eleftheriadis, Ognjen Rudovic, Marc Peter Deisenroth, Maja PanticAbstract:We present a novel approach for supervised Domain adaptation that is based upon the probabilistic framework of Gaussian Processes (GPs). Specifically, we introduce Domain-specific GPs as local experts for facial expression classification from face images. The adaptation of the classifier is facilitated in probabilistic fashion by conditioning the target expert on multiple source experts. Furthermore, in contrast to existing adaptation approaches, we also learn a target expert from available target data solely. Then, a single and confident classifier is obtained by combining the predictions from multiple experts based on their confidence. Learning of the model is efficient and requires no retraining/reweighting of the source classifiers. We evaluate the proposed approach on two publicly available datasets for multi-class (MultiPIE) and multi-label (DISFA) facial expression classification. To this end, we perform adaptation of two contextual factors: 'where' (view) and 'who' (subject). We show in our experiments that the proposed approach consistently outperforms both source and target classifiers, while using as few as 30 target examples. It also outperforms the state-of-the-art approaches for supervised Domain adaptation.