MUSA: A Scalable Multi-Touch and Multi-Perspective Variability Management Tool

Variability management is one of the main activities in the Software Product Line Engineering process. Common and varied features of related products are modelled along with the dependencies and relationships among them. With the increase in size and complexity of product lines and the more holistic systems approach to the design process, managing the ever- growing variability models has become a challenge. In this paper, we present MUSA, a tool for managing variability and features in large-scale models. MUSA adopts the Separation of Concerns design principle by providing multiple perspectives to the model, each conveying different set of information. The demonstration is conducted using a real-life model (comprising of 1000+ features) particularly showing the Structural View, which is displayed using a mind-mapping visualisation technique (hyperbolic trees), and the Dependency View, which is displayed graphically using logic gates

A review of data visualization: opportunities in manufacturing sequence management.

Data visualization now benefits from developments in technologies that offer innovative ways of presenting complex data. Potentially these have widespread application in communicating the complex information domains typical of manufacturing sequence management environments for global enterprises. In this paper the authors review the visualization functionalities, techniques and applications reported in literature, map these to manufacturing sequence information presentation requirements and identify the opportunities available and likely development paths. Current leading-edge practice in dynamic updating and communication with suppliers is not being exploited in manufacturing sequence management; it could provide significant benefits to manufacturing business. In the context of global manufacturing operations and broad-based user communities with differing needs served by common data sets, tool functionality is generally ahead of user application

Sensor Data Visualisation: A Composition-Based Approach to Support Domain Variability

International audienceIn the context of the Internet of Things, sensors are surrounding our environment. These small pieces of electronics are inserted in everyday life's elements (e.g., cars, doors, radiators, smartphones) and continuously collect information about their environment. One of the biggest challenges is to support the development of accurate monitoring dashboard to visualise such data. The one-size-fits-all paradigm does not apply in this context, as user's roles are variable and impact the way data should be visualised: a building manager does not need to work on the same data as classical users. This paper presents an approach based on model composition techniques to support the development of such monitoring dashboards, taking into account the domain variability. This variability is supported at both implementation and modelling levels. The results are validated on a case study named SmartCampus, involving sensors deployed in a real academic campus

Principal Flow Patterns across renewable electricity networks

Using Principal Component Analysis (PCA), the nodal injection and line flow patterns in a network model of a future highly renewable European electricity system are investigated. It is shown that the number of principal components needed to describe 95$\%$ of the nodal power injection variance first increases with the spatial resolution of the system representation. The number of relevant components then saturates at around 76 components for network sizes larger than 512 nodes, which can be related to the correlation length of wind patterns over Europe. Remarkably, the application of PCA to the transmission line power flow statistics shows that irrespective of the spatial scale of the system representation a very low number of only 8 principal flow patterns is sufficient to capture 95$\%$ of the corresponding spatio-temporal variance. This result can be theoretically explained by a particular alignment of some principal injection patterns with topological patterns inherent to the network structure of the European transmission system

Towards Visualisation and Analysis of Runtime Variability in Execution Time of Business Information Systems based on Product Lines

There is a set of techniques that build Business Information Systems (BIS) deploying business processes of the company directly on a process engine. Business processes of companies are continuously changing in order to adapt to changes in the environment. This kind of variability appears at runtime, when a business subprocess is enabled or disabled. To the best of our knowledge, there exists only one approach able to represent properly runtime variability of BIS using Software Product Lines (SPL), namely, Product Evolution Model (PEM). This approach manages the variability by means of a SPL where each product represents a possible evolution of the system. However, although this approach is quite valuable, it does not provide process engineers with the proper support for improving the processes by visualising and analysing execution-time (non-design) properties taking advantage of the benefits provided by the use of SPL. In this paper, we present our first steps towards solving this problem. The contribution of this paper is twofold: on the one hand, we provide a visualisation dashboard for execution-traces based on the use of UML 2.0 timing diagrams, that uses the PEM approach; on the other hand, we provide a conceptual framework that shows a roadmap of the future research needed for analysing execution-time properties of this kind of systems. Thus, due the use of SPL, our approach opens the possibility for evaluating specific conditions and properties of a business process that current approaches do not cover.Comisión Interministerial de Ciencia y Tecnología (CICYT) TIN2006-0047