1,473 research outputs found
From model-driven software development processes to problem diagnoses at runtime
Following the âconvention over configurationâ paradigm, model-driven software development (MDSD) generates code to implement the âdefaultâ behaviour that has been specified by a template separate from the input model. On the one hand, developers can produce end-products without a full understanding of the templates; on the other hand, the tacit knowledge in the templates is subtle to diagnose when a runtime software failure occurs. Therefore, there is a gap between templates and runtime adapted models. Generalising from the concrete problematic examples in MDSD processes to a model-based problem diagnosis, the chapter presents a procedure to separate the automated fixes from those runtime gaps that require human judgments
An Autonomous Engine for Services Configuration and Deployment.
The runtime management of the infrastructure providing service-based systems is a complex task, up to the point where manual operation struggles to be cost effective. As the functionality is provided by a set of dynamically composed distributed services, in order to achieve a management objective multiple operations have to be applied over the distributed elements of the managed infrastructure. Moreover, the manager must cope with the highly heterogeneous characteristics and management interfaces of the runtime resources. With this in mind, this paper proposes to support the configuration and deployment of services with an automated closed control loop. The automation is enabled by the definition of a generic information model, which captures all the information relevant to the management of the services with the same abstractions, describing the runtime elements, service dependencies, and business objectives. On top of that, a technique based on satisfiability is described which automatically diagnoses the state of the managed environment and obtains the required changes for correcting it (e.g., installation, service binding, update, or configuration). The results from a set of case studies extracted from the banking domain are provided to validate the feasibility of this propos
Emergent Frameworks for Decision Support Systems
Knowledge is generated and accessed from heterogeneous spaces. The recent advances in in-formation technologies provide enhanced tools for improving the efficiency of knowledge-based decision support systems. The purpose of this paper is to present the frameworks for developing the optimal blend of technologies required in order to better the knowledge acquisition and reuse in large scale decision making environments. The authors present a case study in the field of clinical decision support systems based on emerging technologies. They consider the changes generated by the upraising social technologies and the challenges brought by the interactive knowledge building within vast online communities.Knowledge Acquisition, CDDSS, 2D Barcodes, Mobile Interface
Model-based testing for space-time interaction using point processes: An application to psychiatric hospital admissions in an urban area
Spatio-temporal interaction is inherent to cases of infectious diseases and
occurrences of earthquakes, whereas the spread of other events, such as cancer
or crime, is less evident. Statistical significance tests of space-time
clustering usually assess the correlation between the spatial and temporal
(transformed) distances of the events. Although appealing through simplicity,
these classical tests do not adjust for the underlying population nor can they
account for a distance decay of interaction. We propose to use the framework of
an endemic-epidemic point process model to jointly estimate a background event
rate explained by seasonal and areal characteristics, as well as a superposed
epidemic component representing the hypothesis of interest. We illustrate this
new model-based test for space-time interaction by analysing psychiatric
inpatient admissions in Zurich, Switzerland (2007-2012). Several socio-economic
factors were found to be associated with the admission rate, but there was no
evidence of general clustering of the cases.Comment: 21 pages including 4 figures and 5 tables; methods are implemented in
the R package surveillance (https://CRAN.R-project.org/package=surveillance
A framework for Model-Driven Engineering of resilient software-controlled systems
AbstractEmergent paradigms of Industry 4.0 and Industrial Internet of Things expect cyber-physical systems to reliably provide services overcoming disruptions in operative conditions and adapting to changes in architectural and functional requirements. In this paper, we describe a hardware/software framework supporting operation and maintenance of software-controlled systems enhancing resilience by promoting a Model-Driven Engineering (MDE) process to automatically derive structural configurations and failure models from reliability artifacts. Specifically, a reflective architecture developed around digital twins enables representation and control of system Configuration Items properly derived from SysML Block Definition Diagrams, providing support for variation. Besides, a plurality of distributed analytic agents for qualitative evaluation over executable failure models empowers the system with runtime self-assessment and dynamic adaptation capabilities. We describe the framework architecture outlining roles and responsibilities in a System of Systems perspective, providing salient design traits about digital twins and data analytic agents for failure propagation modeling and analysis. We discuss a prototype implementation following the MDE approach, highlighting self-recovery and self-adaptation properties on a real cyber-physical system for vehicle access control to Limited Traffic Zones
Don't Treat the Symptom, Find the Cause! Efficient Artificial-Intelligence Methods for (Interactive) Debugging
In the modern world, we are permanently using, leveraging, interacting with,
and relying upon systems of ever higher sophistication, ranging from our cars,
recommender systems in e-commerce, and networks when we go online, to
integrated circuits when using our PCs and smartphones, the power grid to
ensure our energy supply, security-critical software when accessing our bank
accounts, and spreadsheets for financial planning and decision making. The
complexity of these systems coupled with our high dependency on them implies
both a non-negligible likelihood of system failures, and a high potential that
such failures have significant negative effects on our everyday life. For that
reason, it is a vital requirement to keep the harm of emerging failures to a
minimum, which means minimizing the system downtime as well as the cost of
system repair. This is where model-based diagnosis comes into play.
Model-based diagnosis is a principled, domain-independent approach that can
be generally applied to troubleshoot systems of a wide variety of types,
including all the ones mentioned above, and many more. It exploits and
orchestrates i.a. techniques for knowledge representation, automated reasoning,
heuristic problem solving, intelligent search, optimization, stochastics,
statistics, decision making under uncertainty, machine learning, as well as
calculus, combinatorics and set theory to detect, localize, and fix faults in
abnormally behaving systems.
In this thesis, we will give an introduction to the topic of model-based
diagnosis, point out the major challenges in the field, and discuss a selection
of approaches from our research addressing these issues.Comment: Habilitation Thesi
Formal Design of Asynchronous Fault Detection and Identification Components using Temporal Epistemic Logic
Autonomous critical systems, such as satellites and space rovers, must be
able to detect the occurrence of faults in order to ensure correct operation.
This task is carried out by Fault Detection and Identification (FDI)
components, that are embedded in those systems and are in charge of detecting
faults in an automated and timely manner by reading data from sensors and
triggering predefined alarms. The design of effective FDI components is an
extremely hard problem, also due to the lack of a complete theoretical
foundation, and of precise specification and validation techniques. In this
paper, we present the first formal approach to the design of FDI components for
discrete event systems, both in a synchronous and asynchronous setting. We
propose a logical language for the specification of FDI requirements that
accounts for a wide class of practical cases, and includes novel aspects such
as maximality and trace-diagnosability. The language is equipped with a clear
semantics based on temporal epistemic logic, and is proved to enjoy suitable
properties. We discuss how to validate the requirements and how to verify that
a given FDI component satisfies them. We propose an algorithm for the synthesis
of correct-by-construction FDI components, and report on the applicability of
the design approach on an industrial case-study coming from aerospace.Comment: 33 pages, 20 figure
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