The handbook of engineering self-aware and self-expressive systems

When faced with the task of designing and implementing a new self-aware and self-expressive computing system, researchers and practitioners need a set of guidelines on how to use the concepts and foundations developed in the Engineering Proprioception in Computing Systems (EPiCS) project. This report provides such guidelines on how to design self-aware and self-expressive computing systems in a principled way. We have documented different categories of self-awareness and self-expression level using architectural patterns. We have also documented common architectural primitives, their possible candidate techniques and attributes for architecting self-aware and self-expressive systems. Drawing on the knowledge obtained from the previous investigations, we proposed a pattern driven methodology for engineering self-aware and self-expressive systems to assist in utilising the patterns and primitives during design. The methodology contains detailed guidance to make decisions with respect to the possible design alternatives, providing a systematic way to build self-aware and self-expressive systems. Then, we qualitatively and quantitatively evaluated the methodology using two case studies. The results reveal that our pattern driven methodology covers the main aspects of engineering self-aware and self-expressive systems, and that the resulted systems perform significantly better than the non-self-aware systems

The Agent Pattern Driven Business Engineering (APBDE) approach enabled business-based systems

Agent design patterns form a new methodology used to improve the development of software agents. Agent design patterns can help by capturing solutions to common problems in agent design [Lange and Oshima, 1998].Agent design patterns are applied in different systems such as knowledge management systems, real-time systems, and network management systems. Agent design patterns for business-based systems, aim to support different ecommerce paradigms business-to-business (B2B) and business-to-consumer (B2C).In this paper, we developed an approach for extracting agent-based design patterns for B2C e-commerce to improve business-based processes.This approach is called an Agent Pattern Driven Business Engineering (APDBE).Based on this approach, we derived two agent-based commerce design patterns namely, the De-coupler Design Pattern (DecDP), and the Dynamic Design Pattern (DynDP). These design patterns are used to support selling/buying-based processes in e-commerce domain

Actionable knowledge discovery : methodologies and frameworks

University of Technology, Sydney. Faculty of Engineering and Information Technology.Most data mining algorithms and tools stop at the mining and delivery of patterns satisfying expected technical interestingness. There are often many patterns mined but business people either are not interested in them or do not know what follow-up actions to take to support their business decisions. This issue has seriously affected the widespread employment of advanced data mining techniques in greatly promoting enterprise operational quality and productivity. In this thesis, a formal and systematic view of actionable knowledge discovery (AKD for short) has been proposed from the system and microeconomy perspectives. AKD is a closed-loop optimization problem-solving process from problem definition, framework/model design to actionable pattern discovery, and to deliver operationalizable business rules that can be seamlessly associated or integrated with business processes and systems. To support AKD, corresponding methodologies, frameworks and tools have been proposed with case studies in the real world to address critical challenges facing the traditional KDD and. to cater for crucially important factors surrounding real-life AKD. First, a comprehensive survey and retrospection on the existing data mining methodologies, issues and challenges in actionable knowledge discovery are reviewed. Second, a practical data mining methodology: domain driven data mining is addressed. Third, several frameworks have been proposed to support domain drivenactionable knowledge discovery. Fourth, case studies of domain-driven actionable pattern mining in stock markets and social security data are presented to demonstrate the usefulness and potential of the proposed domain driven actionable knowledge discovery. In summary, this thesis explores in detail how domain driven actionable knowledge discovery can be effectively and efficiently applied to the discovery and delivery of knowledge satisfying both technical and business concerns as well as to support smart decision-making in the real world. The issues and techniques addressed in this thesis have potential to promote the research on critical KDD challenges, and contribute to the paradigm shift from data-centered and technical significance-oriented hidden pattern mining to domain-driven and balanced actionable knowledge discovery. The proposed methodologies and frameworks are flexible, general and effective to be expanded and applied to mining real-life complex data for actionable knowledge

A Component-based Framework for Distributed Business Simulations in E-Business Environments

Simulations preserve the knowledge of complex dynamic systems and consequently transfer the knowledge of the cohesions of its elements to a specified target group. As the progress in information technology and therefore the dynamic e-business driven economy adapts even faster to the business demands, new ways to preserve this growing amount of knowledge have to be found. This paper presents an extensible business simulation framework which is realized as a component-based distributed Java Version 2 Enterprise Edition (J2EE) architecture. The framework aspires to offer an extensible and domain independent simulation environment which ensures the return of investment in the sense of implementing this framework once and extending it to the future requirements of diverse domains in e-business. The system architecture follows the requirements in offering distributed deployment of its components on highly standardized level by nevertheless staying vendor independent. The architecture itself was developed by model driven architecture (MDA)-conform software engineering methods using best of breed design patterns composed to a flexible micro-architecture which possess import facilities for simulation entities (business objects) and (business) processes from e-business solutions. Combining the features of the framework, the layered pattern driven micro-architecture, and the distributed J2EE architecture, the postulated knowledge transfer from rapid changes in e-business can be realized

Design of Turing Systems with Physics-Informed Neural Networks

Reaction-diffusion (Turing) systems are fundamental to the formation of spatial patterns in nature and engineering. These systems are governed by a set of non-linear partial differential equations containing parameters that determine the rate of constituent diffusion and reaction. Critically, these parameters, such as diffusion coefficient, heavily influence the mode and type of the final pattern, and quantitative characterization and knowledge of these parameters can aid in bio-mimetic design or understanding of real-world systems. However, the use of numerical methods to infer these parameters can be difficult and computationally expensive. Typically, adjoint solvers may be used, but they are frequently unstable for very non-linear systems. Alternatively, massive amounts of iterative forward simulations are used to find the best match, but this is extremely effortful. Recently, physics-informed neural networks have been proposed as a means for data-driven discovery of partial differential equations, and have seen success in various applications. Thus, we investigate the use of physics-informed neural networks as a tool to infer key parameters in reaction-diffusion systems in the steady-state for scientific discovery or design. Our proof-of-concept results show that the method is able to infer parameters for different pattern modes and types with errors of less than 10\%. In addition, the stochastic nature of this method can be exploited to provide multiple parameter alternatives to the desired pattern, highlighting the versatility of this method for bio-mimetic design. This work thus demonstrates the utility of physics-informed neural networks for inverse parameter inference of reaction-diffusion systems to enhance scientific discovery and design

Quality-aware model-driven service engineering

Service engineering and service-oriented architecture as an integration and platform technology is a recent approach to software systems integration. Quality aspects ranging from interoperability to maintainability to performance are of central importance for the integration of heterogeneous, distributed service-based systems. Architecture models can substantially influence quality attributes of the implemented software systems. Besides the benefits of explicit architectures on maintainability and reuse, architectural constraints such as styles, reference architectures and architectural patterns can influence observable software properties such as performance. Empirical performance evaluation is a process of measuring and evaluating the performance of implemented software. We present an approach for addressing the quality of services and service-based systems at the model-level in the context of model-driven service engineering. The focus on architecture-level models is a consequence of the black-box character of services

Pattern-Based Development of Domain-Specific Modelling Languages

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. A. Pescador, A. Garmendia, E. Guerra, J. Sánchez Cuadrado and J. de Lara, "Pattern-based development of Domain-Specific Modelling Languages," Model Driven Engineering Languages and Systems (MODELS), 2015 ACM/IEEE 18th International Conference on, Ottawa, ON, 2015, pp. 166-175. doi: 10.1109/MODELS.2015.7338247Model-Driven Engineering (MDE) promotes the use of models to conduct all phases of software development in an automated way. Models are frequently defined using Domain- Specific Modelling Languages (DSMLs), which many times need to be developed for the domain at hand. However, while constructing DSMLs is a recurring activity in MDE, there is scarce support for gathering, reusing and enacting knowledge for their design and implementation. This forces the development of every new DSML to start from scratch. To alleviate this problem, we propose the construction of DSMLs and their modelling environments aided by patterns which gather knowledge of specific domains, design alternatives, concrete syntax, dynamic semantics and functionality for the modelling environment. They may have associated services, realized via components. Our approach is supported by a tool that enables the construction of DSMLs through the application of patterns, and synthesizes a graphical modelling environment according to them.Work supported by the Spanish MINECO (TIN2011-24139 and TIN2014-52129-R), the R&D programme of the Madrid Region (S2013/ICE-3006), and the EU commission (FP7-ICT-2013-10, #611125)

Multi-perspective requirements engineering for networked business systems: a framework for pattern composition

How business and software analysts explore, document, and negotiate requirements for enterprise systems is critical to the benefits their organizations will eventually derive. In this paper, we present a framework for analysis and redesign of networked business systems. It is based on libraries of patterns which are derived from existing Internet businesses. The framework includes three perspectives: Economic value, Business processes, and Application communication, each of which applies a goal-oriented method to compose patterns. By means of consistency relationships between perspectives, we demonstrate the usefulness of the patterns as a light-weight approach to exploration of business ideas

Application of biosignal-driven intelligent systems for multifunction prosthesis control

University of Technology, Sydney. Faculty of Engineering and Information Technology.Prosthetic devices aim to provide an artificial alternative to missing limbs. The controller for such devices is usually driven by the biosignals generated by the human body, particularly Electromyogram (EMG) or Electroencephalogram (EEG) signals. Such a controller utilizes a pattern recognition approach to classify the EMG signal recorded from the human muscles or the EEG signal from the brain. The aim of this thesis is to improve the EMG and EEG pattern classification accuracy. Due to the fact that the success of pattern recognition based biosignal driven systems highly depends on the quality of extracted features, a number of novel, robust, hybrid and innovative methods are proposed to achieve better performance. These methods are developed to effectively tackle many of the limitations of existing systems, in particular feature representation and dimensionality reduction. A set of knowledge extraction methods that can accurately and rapidly identify the most important attributes for classifying the arm movements are formulated. This is accomplished through the following: 1. Developing a new feature extraction technique that can identify the most important features from the high-dimensional time-frequency representation of the multichannel EMG and EEG signals. For this task, an information content estimation method using fuzzy entropies and fuzzy mutual information is proposed to identify the optimal wravelet packet transform decomposition for classification. 2. Developing a powerful variable (feature or channel) selection paradigm to improve the performance of multi-channel EMG and EEG driven systems. This will eventually lead to the development of a combined channel and feature selection technique as one possible scheme for dimensionality reduction. Two novel feature selection methods are developed under this scheme utilizing the ant colony arid differential evolution optimization techniques. The differential evolution optimization technique is further modified in a novel attempt in employing a float optimizer for the combinatorial task of feature selection, proving powerful performance by both methods. 3. Developing two feature projection techniques that extract a small subset of highly informative discriminant features, thus acting as an alternative scheme for dimensionality reduction. The two methods represent novel variations to fuzzy discriminant analysis based projection techniques. In addition, an extension to the non-linear discriminant analysis is proposed based on a mixture of differential evolution and fuzzy discriminant analysis. The testing and verification process of the proposed methods on different EMG and EEG datasets provides very encouraging results

Ontology-based patterns for the integration of business processes and enterprise application architectures

Increasingly, enterprises are using Service-Oriented Architecture (SOA) as an approach to Enterprise Application Integration (EAI). SOA has the potential to bridge the gap between business and technology and to improve the reuse of existing applications and the interoperability with new ones. In addition to service architecture descriptions, architecture abstractions like patterns and styles capture design knowledge and allow the reuse of successfully applied designs, thus improving the quality of software. Knowledge gained from integration projects can be captured to build a repository of semantically enriched, experience-based solutions. Business patterns identify the interaction and structure between users, business processes, and data. Specific integration and composition patterns at a more technical level address enterprise application integration and capture reliable architecture solutions. We use an ontology-based approach to capture architecture and process patterns. Ontology techniques for pattern definition, extension and composition are developed and their applicability in business process-driven application integration is demonstrated