Aspect] is a simple and practical aspect-oriented extension to Java With just a few new constructs, AspectJ provides support for modular implementation of a range of crosscutting concerns. In AspectJ's dynamic join point model, join points are well-defined points in the execution of the program; pointcuts are collections of join points; advice are special method-like constructs that can be attached to pointcuts; and aspects are modular units of crosscutting implementation, comprising pointcuts, advice, and ordinary Java member declarations. AspectJ code is compiled into standard Java bytecode. Simple extensions to existing Java development environments make it possible to browse the crosscutting structure of aspects in the same kind of way as one browses the inheritance structure of classes. Several examples show that AspectJ is powerful, and that programs written using it are easy to understand.

An overview of AspectJ

This book discusses how model-based approaches can improve the daily practice of software professionals. This is known as Model-Driven Software Engineering (MDSE) or, simply, Model-Driven Engineering (MDE). MDSE practices have proved to increase efficiency and effectiveness in software development, as demonstrated by various quantitative and qualitative studies. MDSE adoption in the software industry is foreseen to grow exponentially in the near future, e.g., due to the convergence of software development and business analysis. The aim of this book is to provide you with an agile and flexible tool to introduce you to the MDSE world, thus allowing you to quickly understand its basic principles and techniques and to choose the right set of MDSE instruments for your needs so that you can start to benefit from MDSE right away. The book is organized into two main parts. The first part discusses the foundations of MDSE in terms of basic concepts (i.e., models and transformations), driving principles, application scenarios and current standards, like the well-known MDA initiative proposed by OMG (Object Management Group) as well as the practices on how to integrate MDSE in existing development processes. The second part deals with the technical aspects of MDSE, spanning from the basics on when and how to build a domain-specific modeling language, to the description of Model-to-Text and Model-to-Model transformations, and the tools that support the management of MDSE projects. The book is targeted to a diverse set of readers, spanning: professionals, CTOs, CIOs, and team managers that need to have a bird's eye vision on the matter, so as to take the appropriate decisions when it comes to choosing the best development techniques for their company or team; software analysts, developers, or designers that expect to use MDSE for improving everyday work productivity, either by applying the basic modeling techniques and notations or by defining new domain-specific modeling languages and applying end-to-end MDSE practices in the software factory; and academic teachers and students to address undergrad and postgrad courses on MDSE. In addition to the contents of the book, more resources are provided on the book's website http://www.mdse-book.com/, including the examples presented in the book. Table of Contents: Introduction / MDSE Principles / MDSE Use Cases / Model-Driven Architecture (MDA) / Integration of MDSE in your Development Process / Modeling Languages at a Glance / Developing your Own Modeling Language / Model-to-Model Transformations / Model-to-Text Transformations / Managing Models / Summary

Model-Driven Software Engineering in Practice

This paper introduces a knowledge-driven approach to real-time, continuous activity recognition based on multisensor data streams in smart homes The approach goes beyond the traditional data-centric methods for activity recognition in three ways First, it makes extensive use of domain knowledge in the life cycle of activity recognition Second, it uses ontologies for explicit context and activity modeling and representation Third and finally, it exploits semantic reasoning and classification for activity inferencing, thus enabling both coarse-grained and fine-grained activity recognition In this paper, we analyze the characteristics of smart homes and Activities of Daily Living (ADL) upon which we built both context and ADL ontologies We present a generic system architecture for the proposed knowledge-driven approach and describe the underlying ontology-based recognition process Special emphasis is placed on semantic subsumption reasoning algorithms for activity recognition The proposed approach has been implemented in a function-rich software system, which was deployed in a smart home research laboratory We evaluated the proposed approach and the developed system through extensive experiments involving a number of various ADL use scenarios An average activity recognition rate of 9444 percent was achieved and the average recognition runtime per recognition operation was measured as 25 seconds

A Knowledge-Driven Approach to Activity Recognition in Smart Homes

The development of new technologies enables learners to learn more effectively, efficiently, flexibly and comfortably. Learners utilize smart devices to access digital resources through wireless network and to immerse in both personalized and seamless learning. Smart education, a concept that describes learning in digital age, has gained increased attention. This paper discusses the definition of smart education and presents a conceptual framework. A four-tier framework of smart pedagogies and ten key features of smart learning environments are proposed for foster smart learners who need master knowledge and skills of the 21st century learning. The smart pedagogy framework includes class-based differentiated instruction, group-based collaborative learning, individual-based personalized learning and mass-based generative learning. Furthermore, a technological architecture of smart education, which emphasizes the role of smart computing, is proposed. The tri-tier architecture and key functions are all presented. Finally, challenges of smart education are discussed.

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A research framework of smart education

To realise an Ambient Intelligence environment, it is paramount that applications can dispose of information about the context in which they operate, preferably in a very general manner. For this purpose various types of information should be assembled to form a representation of the context of the device on which aforementioned applications run. To allow interoperability in an Ambient Intelligence environment, it is necessary that the context terminology is commonly understood by all participating devices. In this paper we propose an adaptable and extensible context ontology for creating context-aware computing infrastructures, ranging from small embedded devices to high-end service platforms. The ontology has been designed to solve several key challenges in Ambient Intelligence, such as application adaptation, automatic code generation and code mobility, and generation of device specific user interfaces.

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Towards an Extensible Context Ontology for Ambient Intelligence

As the application of model transformation becomes increasingly commonplace, the focus is shifting from model transformation languages to the model transformations themselves. The properties of model transformations, such as scalability, maintainability and reusability, have become important. Composition of model transformations allows for the creation of smaller, maintainable and reusable transformation definitions that together perform a larger transformation. This paper focuses on composition for two rule-based model transformation languages: the ATLAS Transformation Language (ATL) and the QVT Relations language. We propose a composition technique called module superimposition that allows for extending and overriding rules in transformation modules. We provide executable semantics as well as a concise and scalable implementation of module superimposition based on ATL.

Module superimposition: a composition technique for rule-based model transformation languages

Model transformation languages have matured to a point where people have started experimenting with model transformation definitions themselves in addition to the language they are written in. In addition to the transformation language properties, the properties of model transformation definitions themselves become important, such as scalability, maintainability and reusability. Composition of model transformations allows for the creation of smaller, maintainable and reusable model transformation definitions that can scale up to a larger model transformation. There are two kinds of composition for model transformations. External composition deals with chaining separate model transformations together by passing models from one transformation to another. Internal composition composes two model transformation definitions into one new model transformation, which typically requires knowledge of the transformation language. This paper focuses on internal composition for two rule-based model transformation languages. One is the ATLAS Transformation Language, which serves as our implementation vehicle. The other is the QVT Relations language, which is a standard transformation language for MOF. We propose a composition technique called module superimposition. We discuss how module superimposition interacts with other composition techniques in ATL, such as helpers, called rules and rule inheritance. Together, these techniques allow for powerful composition of entire transformation modules as well as individual transformation rules. By applying superimposition to QVT Relations, we demonstrate that our composition technique is relevant outside the ATL language as well.

Composition Techniques for Rule-Based Model Transformation Languages

As model transformations have become an integral part of the automated software engineering lifecycle, reuse, modularisation, and composition of model transformations becomes important. One way to compose model transformations is to compose modules of transformation rules, and execute the composition as one transformation (internal composition). This kind of composition can provide fine-grained semantics, as it is part of the transformation language. This paper aims to generalise two internal composition mechanisms for rule-based transformation languages, module import and rule inheritance, by providing executable semantics for the composition mechanisms within a virtual machine. The generality of the virtual machine is demonstrated for different rule-based transformation languages by compiling those languages to, and executing them on this virtual machine. We will discuss how ATL and graph transformations can be mapped to modules and rules inside the virtual machine.

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Towards a general composition semantics for rule-based model transformation

Model-to-model transformations are often employed to establish translational semantics of Domain-Specific Languages (DSLs) by mapping high-level models into more concrete ones. Such semantics are also executable when there exists a target platform able to execute the target models. Conceiving a transformation that targets a low-level language still remains arduous due to the large semantic gap between the DSL and the corresponding target language. In this respect, depending on the domain of the DSL, this task can be made easier by reusing an existing platform and bytecode language for that domain, as for instance the EMF Transformation Virtual Machine (EMFTVM) for the domain of model transformation. This paper defines executable semantics for EMFMigrate, a model transformation language specifically designed for managing the coupled evolution in model-driven development. To this end, the approach considers EMFTVM as the runtime engine targeted by the proposed semantic mappings.

Dennis Wagelaar

Papers

Towards an Extensible Context Ontology for Ambient Intelligence

Module superimposition: a composition technique for rule-based model transformation languages

Composition Techniques for Rule-Based Model Transformation Languages

Towards a general composition semantics for rule-based model transformation

Translational semantics of a co-evolution specific language with the EMF transformation virtual machine