Environmental uncertainties and hyperconnectivity force technosocio-economic systems to introspect and adapt to succeed and survive. Current practices in decision-making are predominantly intuition-driven with attendant challenges for precision and rigor. We propose to use the concept of digital twins by combining results from Modelling & Simulation, Artificial Intelligence, and Control Theory to create a risk free 'in silico' experimentation aid to help: (i) understand why a system is the way it is, (ii) be prepared for possible outlier conditions, and (iii) identify plausible solutions for mitigating the outlier conditions in an evidence-backed manner. We use reinforcement learning to systematically explore the digital twin solution space. Our proposal is significant because it advances the effective use of digital twins to new problem domains that have new potential for impact. Our approach contributes an original meta model for simulatable digital twin of industry scale techno-socioeconomic systems, agent-based implementation of the digital twin, and an architecture that serves as a risk-free experimentation aid to support simulation-based evidence-backed decision-making. We also discuss the rigor of our validation of the proposed approach and associated technology infrastructure through a representative sample of industry-scale real-world use cases.

Modern enterprises are large complex systems operating in highly dynamic environments thus requiring to respond quickly to a variety of change drivers. Moreover, they are systems of systems wherein understanding is available in localized contexts only and that too is typically partial and uncertain. With the overall system behaviour hard to know a-priori and conventional techniques for system-wide analysis either lacking in rigour or defeated by the scale of the problem, the current practice often exclusively relies on human expertise for monitoring and adaptation. We outline a knowledge-guided simulation-aided data-driven model-based evidence-backed approach to make enterprises adaptive. The approach hinges on the concept of Digital Twin - a set of relevant models that are amenable to analysis and simulation. We describe the core modeling and model processing infrastructure developed, and early stage explorations of its application to problems where the mechanistic world view holds. We argue similar benefits are possible for problem spaces involving human actors as well.

Effective decision-making of modern organisation requires deep understanding of various aspects of organisation such as its goals, structure, business-as-usual operational processes etc. The large size and complex structure of organisations, socio-technical characteristics, and fast business dynamics make this decision-making a challenging endeavour. The state-of-practice of decision-making that relies heavily on human experts is often reported as ineffective, imprecise and lacking in agility. This paper evaluates a set of candidate technologies and makes a case for using actor based simulation techniques as an aid for complex dynamic decision-making. The approach is justified by enumeration of basic requirements of complex dynamic decision-making and the conducting a suitability of analysis of state-of-the-art enterprise modelling techniques. The research contributes a conceptual meta-model that represents necessary aspects of organisation for complex dynamic decision-making together with a realisation in terms of a meta model that extends Actor model of computation. The proposed approach is illustrated using a real life case study from business process outsourcing industry

Enterprise agility, generally defined as the ability of an enterprise to detect and respond to changes timely and effectively, is a core imperative for effective change management and optimal performance in contemporary enterprises. It can improve operational efficiency, and enhance competitive ability. At the same time, it is elusive, challenging, and difficult to achieve. A data centric approach can support change management process by providing an avenue to capture, store, and manage information, activities, and knowledge relating to changes. In addition, it can provide an avenue for re-using previous (successful) change management strategies to adapt to similar changes in the future. In this paper, we examine change management concepts and requirements, integrate them into a conceptual data model. To demonstrate utility of this data model, we apply it to real world industry case study. Results show that this approach can be useful to enterprise change management by providing information and intelligence to support change management decisions.

This paper takes a practitioner's perspective on the problem of organisational decision-making. Industry practice follows a refinement based iterative method for organizational decision-making. However, existing enterprise modelling tools are not complete with respect to the needs of organizational decision-making. As a result, today, a decision maker is forced to use a chain of non-interoperable tools supporting paradigmatically diverse modelling languages with the onus of their co-ordinated use lying entirely on the decision maker. This paper argues the case for a model-based approach to overcome this accidental complexity. A bridge meta-model, specifying relationships across models created by individual tools, ensures integration and a method, describing what should be done when and how, and ensures better tool integration. Validation of the proposed solution using a case study is presented with current limitations and possible means of overcoming them outlined.

Abstract A model transformation verification task may involve a number of different transformations, from one or more of a wide range of different model transformation languages, each transformation may have a particular transformation style, and there are a number of different verification properties which can be verified for each language and style of transformation. Transformations may operate upon many different modelling languages. This diversity of languages and properties indicates the need for a suitably generic framework for model transformation verification, independent of particular model transformation languages, and able to provide support for systematic procedures for verification across a range of languages, and for a range of properties. In this paper we describe the elements of such a framework, and apply this framework to some example transformation verification problems. The paper is novel in covering a wide range of different verification techniques for a wide range of MT languages, within an integrated framework.

Dynamic Software Product Line (DSPL) Engineering has gained interest through its promise of being able to unify software adaptation whereby software adaptation can be realised at compile time and runtime. While previous work has enabled program logic adaptation by the use of language extensions and platform support, little attention has been placed on Graphical User Interface (GUI) variability. Different design patterns including the Model View Controller are commonly used in GUI implementation, with GUI documents being used for declaring the GUI. To handle dynamic GUI variability currently, the developer needs to implement GUI refinements using multiple techniques. This paper proposes a solution for dealing with GUI document variability, statically and dynamically, in a unified way. In our approach, we currently use a compile time method for producing GUI variants, and code transformations to handle these variants within the application at runtime. To avoid GUI duplicates, only GUI variants that are unique, and related to a valid product configuration, are produced. To validate our approach, we implemented tool support to enable this for Android based applications.

Industry has been practicing model-driven development in various flavours. In general it can be said that modelling and use of models have delivered on the promises of platform independence, enhanced productivity, and delivery certainty as regards development of software-intensive systems. Globalization market forces, increased regulatory compliance, ever-increasing penetration of internet, and rapid advance of technology are some of the key drivers leading to increased business dynamics. Increased number of factors impacting the decision and interdependency amongst the key drivers is leading to increased complexity in making business decisions. Also, enterprise software systems need to commensurately change to quickly support the business decisions. The paper presents synthesis of our experience over a decade and half in developing model-driven development technology and using it to deliver several business-critical software systems worldwide.

In this paper, we extend our previously proposed line detection method to line segmentation using a so-called unite-and-divide (UND) approach. The methodology includes two phases, namely the union of spectra in the frequency domain, and the division of the sinogram in Radon space. In the union phase, given an image, its sinogram is obtained by parallel 2D multilayer Fourier transforms, Cartesian-to-polar mapping and 1D inverse Fourier transform. In the division phase, the edges of butterfly wings in the neighborhood of every sinogram peak are firstly specified, with each neighborhood area corresponding to a window in image space. By applying the separated sinogram of each such windowed image, we can extract the line segments. The division Phase identifies the edges of butterfly wings in the neighborhood of every sinogram peak such that each neighborhood area corresponds to a window in image space. Line segments are extracted by applying the separated sinogram of each windowed image. Our experiments are conducted on benchmark images and the results reveal that the UND method yields higher accuracy, has lower computational cost and is more robust to noise, compared to existing state-of-the-art methods.

Business and IT alignment remains an ongoing concern for organizations. Enterprise Architecture has emerged as a possible tool for understanding and planning for BIA, but the current range of methods and frameworks for EA do not support precise measurement. In this paper, we propose a set of technologies and concepts - notably goals and computable functions which can be used to provide a measure of equivalence between as-is and to-be enterprise architectures. The goals enable description of business level requirements whilst the functions enable the goals to be evaluated at a technical architecture level. The technology is evaluated with a detailed and authentic case study from higher education.

Annotations provide a limited way of extending Java in order to tailor the language for specific tasks. This paper describes a proposal for a Java extension which generalises annotations to allow Java to be a platform for developing domain specific languages.

This paper shows how techniques devised for aspect-oriented modelling (AOM) can be used for aspect-oriented metamodelling. Metamodelling is an approach to language definition, which has been adopted by industry for defining modelling languages, in particular the Unified Modelling Language (UML), and involves the construction of an object-oriented model of the abstract syntax and, optionally, the concrete notation and semantics of the target language. As the importance of models in software development grows, so does the importance of metamodels, not least to provide the basis of implementations of modelling tools. Specifically, there is a need for complete and accurate metamodels, which have a uniform architecture and which can be organized appropriately to support the definition of families of languages. The approach described in this paper uses AOM techniques to address this need. It is illustrated by extracts from the 2U submission to the UML 2.0 RFPs issued by the Object Management Group in 2001. The paper concludes with a discussion on the customization and generation of tools from such definitions.

Abstract This paper makes a contribution to the refinement of systems which involve search by proposing a simple non-deterministic model for rule based transition systems and using this to define a meaning for rule based refinement which allows each stage of the software development path to be verified with respect to the previous stage. The proposal allows a system which involves search to be specified in terms of all the possible outcomes. Each stage of refinement will introduce complexity to the rules and therefore develop the search space in ever more sophisticated ways. At each stage of the refinement it will be possible to be precise about which collections of outcomes have been deleted, thereby achieving a verified (prototype) implementation.

Vision systems are now delivering real-time tracked and classified data from which behaviours can be inferred. Behavioural specifications of observable entities are viewed as attribute grammars with general constraints on the values of the attributes. The characteristics features of realistic input data to a behavioural recogniser are missing, inserted and noisy values. Behaviour recognition is defined in terms of a simple language which is used to express behaviour specifications. The meaning of a specification is given by a mapping to the set of observable value sequences which exhibit the specified behaviour. A computational mechanism for behaviour recognition, which is consistent with the meaning of behaviour specifications, is given using a simple functional programming language which has been enriched with non-deterministic features. The non-determinism is controlled using a simple belief mechanism.

Accurate planning is an essential part of efficient project management, but computer-aided techniques have, in the past, not fulfilled their potential in this important field, This paper describes how interactive graphics techniques have been used to help the planner in the design of his programme of work and in the dissemination of his recommended plan.