Model Transformation Approach Research Articles

GRAPH TRANSFORMATION RULES FOR IFC-TO-CITYGML ATTRIBUTE CONVERSION

Abstract. In model transformation, the population of attributes on the target side constitutes the last step of the conversion process that carries over that part of the input which is often perceived as the most valuable actual information. We are employing a graph-based model transformation approach to convert building information models into geospatial city models. In this paper, we are reporting on different types of transformation rules to populate the attributes on CityGML side using information extracted from the IFC data. We document the various ways how attribute values can be stored in IFC and CityGML respectively and identify patterns that bridge these endpoints in the conversion process. These patterns lead to a set of prototypical graph transformation rules which have been applied to a range of building projects. The novel graph-based approach to IFC-to-CityGML conversion implicates an intuitive visual representation of these rules. This work can also serve as a starting point to convert IFC data to other formats or to populate CityGML from other data sources.

Robust stabilization of uncertain 2-D discrete-time delayed systems using sliding mode control

This paper aims to solve the problem of sliding mode control for an uncertain two-dimensional (2-D) systems with states having time-varying delays. The uncertainties in the system dynamics are constituted of mismatched uncertain parameters and the unknown nonlinear bounded function. The proposed problem utilizes the model transformation approach. By segregating the proper Lyapunov–Krasovskii functional in concert with the improved version of Wirtinger-based summation inequality, sufficient solvability conditions for the existence of linear switching surfaces have been put forward, which ensure the asymptotical stability of the reduced-order equivalent sliding mode dynamics. Then, we solve the controller synthesis problem by extending the recently proposed reaching law to 2-D systems, whose proportional part is appropriately scaled by the factor that does not depend on some constant terms but rather on current switching surface’s value, which in turn ensures the faster convergence and better robustness against uncertainties. Finally, the proposed results have been validated through an implementation to a suitable physical system.

A Formalization Model Transformation Approach on Workflow Automatic Execution from CIM Level to PIM Level

Model transformation is a keystone in carrying out model-driven development. Currently, many studies have focused on model transformation, those researches study the automatic transformation from PIM to PSM and from PSM to code, but only few have dealt with how the CIM-level model is automatically mapped onto the PIM-level model. Even though some studies have proposed the semi-automatic transformation from CIM-level model to PIM-level model, only few mechanisms are discussed to protect the consistency between the CIM-level model and the PIM-level model in these researches because notation specification of these CIM-level model and PIM-level model does not contain precise semantics. Focusing on workflow perspective, this paper proposes an automatic model transformation approach, which applies a formal model to define the automatic mapping of the workflow from CIM-level model to PIM-level model. This proposal extends Petri nets model elements to define a bridge model, then we use this bridge model to link the CIM-level model (BPMN) and PIM-level model (SCM) following model-driven approach. The metamodels, the transformation rule and the transformation plug-ins have been defined in Eclipse Modeling Framework. Meanwhile, the plug-ins have been empirically validated by a Travel Agency case study. This study shows that using extended Petri nets model as a bridge model to define an automatic model transformation between CIM-level model and PIM-level model is feasible. This approach by means of formal model can ensure the correctness and completeness of the target model, and it can also maintain the consistency between the source model and target model.

Robust fusion steady‐state filtering for multisensor networked systems with one‐step random delay, missing measurements, and uncertain‐variance multiplicative and additive white noises

SummaryThe robust fusion steady‐state filtering problem is investigated for a class of multisensor networked systems with mixed uncertainties including multiplicative noises, one‐step random delay, missing measurements, and uncertain noise variances, the phenomena of one‐step random delay and missing measurements occur in a random way, and are described by two Bernoulli distributed random variables with known conditional probabilities. Using a model transformation approach, which consists of augmented approach, derandomization approach, and fictitious noise approach, the original multisensor system under study is converted into a multimodel multisensor system with only uncertain noise variances. According to the minimax robust estimation principle, based on the worst‐case subsystems with conservative upper bounds of uncertain noise variances, the robust local steady‐state Kalman estimators (predictor, filter, and smoother) are presented in a unified framework. Applying the optimal fusion algorithm weighted by matrices, the robust distributed weighted state fusion steady‐state Kalman estimators are derived for the considered system. In addition, by using the proposed model transformation approach, the centralized fusion system is obtained, furthermore the robust centralized fusion steady‐state Kalman estimators are proposed. The robustness of the proposed estimators is proved by using a combination method consisting of augmented noise approach, decomposition approach of nonnegative definite matrix, matrix representation approach of quadratic form, and Lyapunov equation approach, such that for all admissible uncertainties, the actual steady‐state estimation error variances of the estimators are guaranteed to have the corresponding minimal upper bounds. The accuracy relations among the robust local and fused steady‐state Kalman estimators are proved. An example with application to autoregressive signal processing is proposed, which shows that the robust local and fusion signal estimation problems can be solved by the state estimation problems. Simulation example verifies the effectiveness and correctness of the proposed results.

Actor-Critic Reinforcement Learning Control of Non-Strict Feedback Nonaffine Dynamic Systems

The most focuses of the existing actor-critic reinforcement learning control (ARLC) are on dealing with continuous affine systems or discrete nonaffine systems. In this paper, I propose a new ARLC method for continuous nonaffine dynamic systems subject to unknown dynamics and external disturbances. A new input-to-state stable system is developed to establish an augmented dynamic system, from which I further get a strict-feedback affine model that is convenient for control designing based on a model transformation approach. The Nussbaum function is connected with a fuzzy approximation to devise an actor network whose tracking performance is further enhanced via strengthening signals generated by a fuzzy critic network. The stability of the closed-loop control system is guaranteed by the Lyapunov synthesis. Finally, the comparison simulation results are presented to verify the design.

Formal semantics and transformation of BPMN models

Business process modelling notation (BPMN) represents an industrial standard created in order to provide a graphical semi-formal notation that is readily understandable by all business users, from the business analyst to the technical developers, and, finally, to the business people who will manage those processes. However, BPMN models are expressed in a semi-formal modelling language and need a well-defined formal semantics base for their notations. This formalisation enables analysis and verification tasks. Some efforts have been made to transform BPMN models into formal representations including Petri nets, but, to the best of our knowledge, no work has been done for transforming BPMN models into a language that supports a true concurrency semantics, DD-LOTOS. The DD-LOTOS language is defined for the formal specification of distributed real-time systems and has been defined on a semantic model of true concurrency. The main goal of this paper is to propose a formal semantics for a subset of BPMN, defined in terms of DD-LOTOS code. The paper also proposes a model transformation approach to generate DD-LOTOS code from BPMN models. This approach has been implemented using Eclipse Sirius and Eclipse Acceleo. Two well-known case studies are given to illustrate the transformation.

Institution-based UML activity diagram transformation with semantic preservation

This paper presents a specific tool, called MAV-UML-AD, allowing the specification and the verification of workflow models using UML activity diagrams (UML ADs) and Event-B and based on institutions. The developed tool translates an activity diagram model into an equivalent Event-B specification according to a mathematical semantics. The transformation approach of UML AD models is based on the theory of institutions. In fact, each of UML AD and Event-B specifications is defined by an instance of its corresponding institution. The transformation approach is represented by an institution co-morphism which is defined between the two institutions. Institution theory is adopted as the theoretical framework of the tool essentially for two reasons. First, it gives a locally mathematical semantics for UML AD and Event-B. Second, it defines a semantic preserving mapping between UML AD specification and Event-B machine. Thanks to the B theorem prover, functional proprieties like liveness and fairness can be formally checked. The core of the model transformation approach will be highlighted in this paper and how institution concepts such as category, co-morphism and signature are presented in the two used formalisms. This paper will also illustrate the use of the developed tool MAV-UML-AD through an example of specification and verification.

Slicing ATL model transformations for scalable deductive verification and fault localization

Model-driven engineering (MDE) is increasingly accepted in industry as an effective approach for managing the full life cycle of software development. In MDE, software models are manipulated, evolved and translated by model transformations (MT), up to code generation. Automatic deductive verification techniques have been proposed to guarantee that transformations satisfy correctness requirements (encoded as transformation contracts). However, to be transferable to industry, these techniques need to be scalable and provide the user with easily accessible feedback. In MT-specific languages like ATL, we are able to infer static trace information (i.e., mappings among types of generated elements and rules that potentially generate these types). In this paper, we show that this information can be used to decompose the MT contract and, for each sub-contract, slice the MT to the only rules that may be responsible for fulfilling it. Based on this contribution, we design a fault localization approach for MT, and a technique to significantly enhance scalability when verifying large MTs against a large number of contracts. We implement both these algorithms as extensions of the VeriATL verification system, and we show by experimentation that they increase its industry readiness.

A Tensor Product Model Transformation Approach to the Discretization of Uncertain Linear Systems

A Tensor Product Model Transformation Approach to the Discretization of Uncertain Linear Systems

EVL+Strace: a novel bidirectional model transformation approach

Context: Model transformation, as one of the cornerstones of Model-Driven Engineering (MDE) paradigm, produces target models from source models. In most of the practical cases, both source and target models are changed independently and it is essential to preserve the consistency between them. Bidirectional transformation (Bx) provides a mechanism to re-establish this inter-model consistency. Bx approaches suffer from several limitations, such as lack of a comprehensive implementation, low learnability, and mismanagement of update conflicts.Objective: To alleviate the aforementioned drawbacks, we propose a novel Bx approach, called EVL+Strace, which is built using the Epsilon Validation Language (EVL) on a domain-specific trace metamodel (Strace). Furthermore, an Eclipse-based toolkit, called MoDEBiTE, is developed to automatically produce the EVL+Strace artifacts including the specific trace metamodel and transformation code.Method: EVL+Strace exploits the ability of EVL to transform user updates on models from source to target and vice versa, simultaneously. The applied trace metamodel should be specific to the domains of source and target metamodels that prevents illegitimate trace elements. Additionally, it enables developers to specify the transformation concepts more precisely. A running example is applied to explain the components of EVL+Strace and application of MoDEBiTE.Result: EVL+Strace is the first practical interactive approach that can provide important bidirectional features, such as preservation and propagation. A feature model of Bx approaches is applied to compare EVL+Strace with the well-known Bx languages. To show the superiority of EVL+Strace and applicability of MoDEBiTE, a comprehensive evaluation on six case studies is performed.Conclusion: EVL+Strace provides an interactive transformation system to manage update conflicts. It uses the EVL language for defining Bx transformation that has an easy-to-learn syntax. It is developed based on Epsilon, which is a comprehensive and actively updated framework.

On Utilizing Model Transformation for the Performance Analysis of Queueing Networks

In this paper, we present an approach for model transformation from Queueing Network Models (QNMs) into Queueing Petri Nets (QPNs). The performance of QPNs can be analyzed using a powerful simulation engine, SimQPN, designed to exploit the knowledge and behavior of QPNs to improve the efficiency of simulation. When QNMs are transformed into QPNs, their performance can be analyzed efficiently using SimQPN. To validate our approach, we apply it to analyze the performance of several queueing network models including a model of a database system. The evaluation results show that the performance analysis of the transformed QNMs has high accuracy and low overhead. In this context, model transformation enables the performance analysis of queueing networks using different ways that can be more efficient.

Formal foundations for software model to model transformation operation

Software model transformation operations are central operations in Model-Driven approaches. In order to represent software models, graphical modeling notations, for example UML, are used. Quality of software model, obtained after transformation, influences on further operations with this model. Thus, it is important to design formal approaches for model to model transformation that are grounded on analytical and mathematical tools. These approaches should provide a background for flexible adopting software model transformational techniques for peculiarities of specific software development lifecycle model. Challenges to mathematical tools and transformation rules that are involved to designing of model to model transformation approaches are formulated in this paper. The ground of mathematical tools choice that is based on these challenges is performed. An approach for performing model to model transformation, which is based on graph transformation, is presented in this paper. Transformational operations are considered on meta-level and concrete level. On meta-level choosing of mathematical tools for representing of transformation stages and transformational artifacts are grounded. Software models are represented as graphs. Initial information for transformation is represented as a set of sub-graphs. Transformation rules are composed using second and first order logics. On the level of the first-order logic all software model elements that participate in transformation are considered. In the level of second-order logic transformation rule considers types of software model element that are participate in the transformation. Proposed approach is extensible and may be used for extend functionality of model to model tools that process software models. For example in MEDINI QVT there is no direct ways to compose a model to model transformation rule that considers those software models elements that have no direct links. Problems in programming 2018; 2-3: 124-131

An Automated Functional Testing Framework for Context-Aware Applications

In the modern era of mobile computing, context-aware computing is an emerging paradigm due to its widespread applications. Context-aware applications are gaining increasing popularity in our daily lives since these applications can determine and react according to the situational context and help users to enhance usability experience. However, testing these applications is not straightforward since it poses several challenges, such as generating test data, designing context-coupled test cases, and so on. However, the testing process can be automated to a greater extent by employing model-based testing technique for context-aware applications. To achieve this goal, it is necessary to automate model transformation, test data generation, and test case execution processes. In this paper, we propose an approach for behavior modeling of context-aware application by extending the UML activity diagram. We also propose an automated model transformation approach to transform the development model, i.e., extended UML activity diagram into the testing model in the form of function nets. The objective of this paper is to automate the context-coupled test case generation and execution. We propose a functional testing framework for automated execution of keyword-based test cases. Our functional testing framework can reduce the testing time and cost, thus enabling the test engineers to execute more testing cycles to attain a higher degree of test coverage.

Guaranteeing prescribed output tracking performance for air-breathing hypersonic vehicles via non-affine back-stepping control design

This study develops a novel back-stepping controller with prescribed performance for air-breathing hypersonic vehicles (AHVs) utilizing non-affine models. For the velocity dynamics, a non-affine control law is addressed to achieve prescribed tracking performance. The altitude subsystem is rewritten as a strict feedback formulation to facilitate the back-stepping control system design via a model transformation approach. At each step of back-stepping design, performance functions are constructed to force tracking errors to fall within prescribed boundaries, based on which desired transient performance and steady-state performance are guaranteed for both velocity and altitude control subsystems. Furthermore, the exploited controllers are accurate model independent, which guarantees control laws with satisfactory robustness against unknown uncertainties. Meanwhile, the proposed control scheme can cope with unknown control gains. By the Lyapunov stability theory, the stability of the closed-loop control system is confirmed. Finally, numerical simulations are given for an AHV to validate the effectiveness of the proposed control approach.

On the consistency of event processing: A semantic approach

Event processing is one of the cornerstone technologies in bridging physical world and cyber system together. Although event-based processing system has been widely used in various applications, however, consistency of event processing is still an open issue need further exploration. The inconsistency problem produces inaccurate detection result and corrupts the system correctness. In this paper, we propose a semantic approach for event modeling and detection model transformation with a semantic calculus system. Particularly, we first propose a complex event semantic model, OntoEvent, and define several key operators and properties to describe the logic, temporal and attribute relations in complex events. Second, we propose the concept of event constraint and elaborate the occurrence, temporal, and attribute functions to formalize the semantic implications in OntoEvent model. On that basis, we present the extraction rules and establish a calculus mechanism for constraints based on axioms. With these works, an automata-based detection model, named OntoCEP (Ontology-based Complex Event Detection), and a pipelined procedure for the assembly from constraints to OntoCEP model is proposed. The procedure is composed of several sequential phases and the consistency in each assembly phase is proved. Therefore, we establish a semantic-consistent mapping mechanism from event to detection model in the form of constraints. Experiments and evaluations prove that our approach ensure the consistency with event and detection models. Besides, our detection model consumes less computational resources and outperforms other selected benchmarked models in terms of computational efficiency and processing capability.

Given-time consensus for stochastic Markov jump networks by dynamic output feedback

This paper focuses on the given-time consensus of stochastic Markov jump networks with disturbances based on dynamic output feedback control. By exploring the relative output information, a distributed dynamic output feedback protocol is designed to guarantee that the disagreement dynamics of networks keeps within the prescribed bound in a fixed time interval. With the new model transformation approach, the obtained results are more general and lead to less conservativeness. An example of industrial material heating unit verifies the engineering application of the proposed algorithm for networks in the presence of both Markov jump topologies and external disturbances.

Supervisor Synthesis for Mealy Automata With Output Functions: A Model Transformation Approach

Recently, Ushio and Takai have proposed a Mealy-automata-based framework to study the non-blocking supervisory control problem under partial observation. This framework can handle observation uncertainties by taking both state-dependent observations and nondeterministic outputs into account. In this technical note, we propose a model-transformation-based approach to solve the supervisor synthesis problem in this framework. First, we propose a transformation algorithm that transforms the non-blocking supervisor synthesis problem for Mealy automata to a conventional supervisory synthesis problem under partial observation, which can be solved effectively by an existing algorithm. Then we show that the supervisor synthesized for the transformed problem indeed solves the original problem. Our results bridge the gap between the conventional supervisory control framework under partial observation and the recently proposed Mealy automata framework where nondeterministic output function is used.

Using internal domain-specific languages to inherit tool support and modularity for model transformations

Model-driven engineering (MDE) has proved to be a useful approach to cope with today’s ever-growing complexity in the development of software systems; nevertheless, it is not widely applied in industry. As suggested by multiple studies, tool support is a major factor for this lack of adoption. In particular, the development of model transformations lacks good tool support. Additionally, modularization techniques are inevitable for the development of larger model transformations to keep them maintainable. Existing tools for MDE, in particular model transformation approaches, are often developed by small teams and cannot keep up with advanced tool support for mainstream general-purpose programming languages, such as IntelliJ or Visual Studio. Internal DSLs are a promising solution to these problems. In this paper, we investigate the impact of design decisions of an internal DSL to the reuse of tool support and modularization concepts from the host language. We validate our findings in terms of understandability, applicability, tool support, and extensibility using three case studies from academia, a model-driven engineering platform, and the industrial automation domain where we apply an implementation of an internal model transformation language on the .NET platform. The results confirm the value of inherited modularity and tool support while conciseness and understandability are still competitive.

Accuracy Based Feature Ranking Metric for Multi-Label Text Classification

In many application domains, such as machine learning, scene and video classification, data mining, medical diagnosis and machine vision, instances belong to more than one categories. Feature selection in single label text classification is used to reduce the dimensionality of datasets by filtering out irrelevant and redundant features. The process of dimensionality reduction in multi-label classification is a different scenario because here features may belong to more then one classes. Label and instance space is rapidly increasing by the grandiose of Internet, which is challenging for Multi-Label Classification (MLC). Feature selection is crucial for reduction of data in MLC. Method adaptation and data set transformation are two techniques used to select features in multi label text classification. In this paper, we present dataset transformation technique to reduce the dimensionality of multi-label text data. We used two model transformation approaches: Binary Relevance, and Label Power set for transformation of data from multi-label to single label. The Process of feature selection is done using filter approach which utilizes the data to decide the importance of features without applying learning algorithm. In this paper we used a simple measure (ACC2) for feature selection in multi-label text data. We used problem transformation approach to apply single label feature selection measures on multi-label text data; did the comparison of ACC2 with two other feature selection methods, information gain (IG) and Relief measure. Experimentation is done on three bench mark datasets and their empirical evaluation results are shown. ACC2 is found to perform better than IG and Relief in 80% cases of our experiments.

Transforming Model Oriented Program into Android Source Code Based on Drools Rule Engine

Model transformation is one of the prominent features and the rising research area of Model Driven Engineering (MDE). MDE promotes models to primary artifacts that drive the whole development process. This paper presents the model transformation approach for textual model oriented programs Umple (UML Programming Language) to generate android applications (apps). The proposed approach improved the generation of android source code by using Drools transformation rules and introducing new concern in model driven mobile engineering. The major objective of proposed transformation approach intends to address consistency between source and target model and also intends to handle productivity issues in model driven software development. The main results of model transformation approach are Java class for model layer, XML file for view layer and android activity class for controller layer. Results show that proposed approach achieves high consistency between source and target model and also improves model transformation productivity.