Earlier research has identified network analysis techniques, methods, and models used to analyze structural aspects of an enterprise architecture (EA) modeled as a network or graph. However, there is still no common set of conceptual elements for such research that could allow one to identify the information requirements needed to perform this type of analysis. In the present research, we organize foundational conceptual elements in a meta-model as a step towards fostering the development of this research field and creating alignment among researchers. As a second contribution, we classify the existing knowledge about network analysis in an EA context through a systematic literature review, according to the proposed meta-model; this results in a library of 74 network analysis initiatives (e.g., metrics or methods) for EA, which we evaluate reasoning about their efficacy and utility and also surveying experts. Our work thus identifies and systematizes the state of art of network science as a toolset to be applied in the EA context.

Many computer vision algorithms are biologically inspired and designed based on the human visual system. Convolutional neural networks (CNNs) are similarly inspired by the primary visual cortex in the human brain. However, the key difference between current visual models and the human visual system is how the visual information is gathered and processed. We make eye movements to collect information from the environment for navigation and task performance. We also make specific eye movements to important regions in the stimulus to perform the task-at-hand quickly and efficiently. Researchers have used expert scanpaths to train novices for improving the accuracy of visual search tasks. One of the limitations of such a system is that we need an expert to examine each visual stimuli beforehand to generate the scanpaths. In order to extend the idea of gaze guidance to a new unseen stimulus, there is a need for a computational model that can automatically generate expert-like scanpaths. We propose a model for automatic scanpath generation using a convolutional neural network (CNN) and long short-term memory (LSTM) modules. Our model uses LSTMs due to the temporal nature of eye movement data (scanpaths) where the system makes fixation predictions based on previous locations examined.

Enterprise architecture (EA) network analysis has been attracting researchers' attention lately. The main source of information is the structural components, including the relations among them and how they might be structurally arranged. These relations are studied to generate valuable information for EA professionals. However, to the best of our knowledge, ours is the first attempt to combine structural information with a second source of information: expert's tacit knowledge. We believe combining these sources employing two new methods - what we call cognitive-structural diagnosis analysis and attribute check analysis - can refine the expert's knowledge about the architecture. To demonstrate these methods' feasibility, we apply them with two application architecture datasets collected in two different organizations. We also offer a classification schema for enterprise architecture network analysis at the component level, our focus. Our conclusions indicate that the cognitive- structural diagnosis analysis method minimizes analysis subjectivity while validating important components and also suggesting important structural ones to be further analyzed by experts. The attribute check analysis offers further contributions by helping in the investigation of particular attributes of applications in important architectural positions.

International audience

Control systems running on a computer are subject to timing disturbances coming from implementation constraints. Fortunately closed-loop systems behave robustly w.r.t. modelling errors and disturbances, and the controller design can be performed to explicitly enhance robustness against specific uncertainties. On one hand robustness in process controllers can be used to comply with weakly modelled timing uncertainties. On the other hand the principle of robust closed-loop control can also be applied to the real-time scheduler to provide online adaption of some scheduling parameters, with the objective of controlling the computing resource allocation. The control performance specification may be set according to both control and implementation constraints. The approach is illustrated through several examples using simulation and an experimental feedback scheduler is briefly described.

For original paper by T. Eisenbarth et al. see ibid., vol.29, no.3, p.210-24 (2003). We compare three approaches that apply formal concept analysis on execution profiles. This survey extends the discourse of related research by Bojic and Velasevic (2000).

This work is devoted to integrated control-scheduling in the case of real-time control of systems with communications delays, i.e. network induced delay and input output latency. Feedback control of computing resources is tackled to deal with CPU resource variations and unpredictable workload. Thus, two new control schemes are provided for feedback scheduling of single and multi control task systems. Moreover, the system control design takes into account the unknown delays due to the temporal uncertainties that are unavoidable in real-time control. A new method of state feedback control design for discrete time-delay systems is presented in a LMI formulation. Two illustrative examples show the interest of the proposed approach.

Understanding the implementation of a certain feature of a system requires identification of the computational units of the system that contribute to this feature. In many cases, the mapping of features to the source code is poorly documented. In this paper, we present a semiautomatic technique that reconstructs the mapping for features that are triggered by the user and exhibit an observable behavior. The mapping is in general not injective; that is, a computational unit may contribute to several features. Our technique allows for the distinction between general and specific computational units with respect to a given set of features. For a set of features, it also identifies jointly and distinctly required computational units. The presented technique combines dynamic and static analyses to rapidly focus on the system's parts that relate to a specific set of features. Dynamic information is gathered based on a set of scenarios invoking the features. Rather than assuming a one-to-one correspondence between features and scenarios as in earlier work, we can now handle scenarios that invoke many features. Furthermore, we show how our method allows incremental exploration of features while preserving the "mental map" the analyst has gained through the analysis.

The need for changing a program frequently confronts maintainers with the reality that no valid architectural description is at hand. To solve that problem, we presented at ICSM 2001 a language-independent and easy to use technique for opportunistic and demand driven location of features in source code based on static and dynamic analysis and concept analysis. In order to further validate the technique, we performed an industrial case study on a 1.2 million LOC production system. The experiences we made during that case study showed two problems of our approach: the growing complexity of concept lattices for large systems with many features and the need for handling compositions of features. This paper extends our technique to solve these problems. We show how this method allows incremental exploration of features while preserving the "mental map" the maintainer has gained through the analysis. The second improvement is a detailed look at composing features into more complex scenarios. Rather than assuming a one-to-one correspondence between features and scenarios as in earlier work, we can now handle scenarios that invoke many features.

Understanding a system's implementation without prior knowledge is a hard task for reengineers in general. However, some degree of automatic aid is possible. The authors present a technique for building a mapping between the system's externally visible behavior and the relevant parts of the source code. The technique combines dynamic and static analyses to rapidly focus on the system's parts urgently required for a goal-directed process of program understanding.

In this paper we present a preliminary study concerning the stabilization of an underwater free-floating remotely operated vehicle fitted with a redundant manipulator. The controller structure follows the Orccad approach for robotic systems programming, which gathers control laws in continuous time at the low levels and discrete time logical aspects at higher levels. After being designed and verified, the basic actions of individual subsystems are logically composed to build more complex procedures up to a full mission design. While the system's performance can be checked using realistic simulations, crucial properties such as deadlock avoidance, safety and liveness are formally verified at both levels, using in particular some advantages of synchronous programming and associated tools. The approach is illustrated through simulation results while experiments are in progress.

For underwater vehicles to be self-sufficient in an a priori unknown environment, feedback from the environment through altitude sensors is essential. This paper presents how this feedback can be efficiently achieved for AUVs in which different simultaneous tasks have to concur to the achievement of a mission. The approach is based on the task-function approach, with a continuous switching between the involved tasks. The presented application concerns the use of acoustic range/altitude sensors in the control of a fully-actuated AUV.

ORCCAD (Open Robot Controller Computer Aided Design), is a development environment for robotic controllers which provides verification and simulation tools along with a graphical human-machine interface, in order to bridge the gap between control laws as understood by the control systems community, and real-time computing as understood by the computer science community. In this paper, we introduce some recent extensions to ORCCAD which make it possible to perform certain kinds of temporal verification of real-time closed loop robotic tasks.>

The authors present some design issues for a task-level controller based on a specific control approach. The achievement of a robotic application requires the use of a large set of cooperating devices such as manipulators and sensors. An application is performed by scheduling several actions of the system. Each action is modeled with a functional aspect (control law) and a behavioral one (reactions on events). These two aspects are combined in a single object, the robot-task. An object-oriented approach was used to model these actions. The synchronous language Esterel was used both to program an application at the end-user level and to program the behavioral part of the robot-tasks at the control level. The main design issues of a real-time controller able to implement and perform these actions are outlined.>