514,266 research outputs found
City Data Fusion: Sensor Data Fusion in the Internet of Things
Internet of Things (IoT) has gained substantial attention recently and play a
significant role in smart city application deployments. A number of such smart
city applications depend on sensor fusion capabilities in the cloud from
diverse data sources. We introduce the concept of IoT and present in detail ten
different parameters that govern our sensor data fusion evaluation framework.
We then evaluate the current state-of-the art in sensor data fusion against our
sensor data fusion framework. Our main goal is to examine and survey different
sensor data fusion research efforts based on our evaluation framework. The
major open research issues related to sensor data fusion are also presented.Comment: Accepted to be published in International Journal of Distributed
Systems and Technologies (IJDST), 201
VR-PMS: a new approach for performance measurement and management of industrial systems
A new performance measurement and management framework based on value and risk is proposed. The proposed framework is applied to the modelling and evaluation of the a priori performance evaluation of manufacturing processes and to deciding on their alternatives. For this reason, it consistently integrates concepts relevant to objectives, activity, and risk in a single framework comprising a conceptual value/risk model, and it conceptualises the idea of value- and risk based performance management in a process context. In addition, a methodological framework is developed to provide guidelines for the decision-makers or performance evaluators of the processes. To facilitate the performance measurement and management process, this latter framework is organized in four phases: context establishment, performance modelling, performance assessment, and decision-making. Each phase of the framework is then instrumented with state of-the-art quantitative analysis tools and methods. For process design and evaluation, the deliverable of the value- and risk-based performance measurement and management system (VR-PMS) is a set of ranked solutions (i.e. alternative business processes) evaluated against the developed value and risk indicators. The proposed VR-PMS is illustrated with a case study from discrete parts manufacturing but is indeed applicable to a wide range of processes or systems
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Integrating information and knowledge for enterprise innovation
It has widely been accepted that enterprise integration, can be a source of socio-technical and cultural problems within organisations wishing to provide a focussed end-to-end business service. This can cause possible “straitjacketing” of business process architectures, thus suppressing responsive business re-engineering and competitive advantage for some companies. Accordingly, the current typology and emergent forms of Enterprise Resource Planning (ERP) and Enterprise Application Integration (EAI) technologies are set in the context of understanding information and knowledge integration philosophies. As such, key influences and trends in emerging IS integration choices, for end-to-end, cost-effective and flexible knowledge integration, are examined. As touch points across and outside organisations proliferate, via work-flow and relationship management-driven value innovation, aspects of knowledge refinement and knowledge integration pose challenges to maximising the potential of innovation and sustainable success, within enterprises. This is in terms of the increasing propensity for data fragmentation and the lack of effective information management, in the light of information overload. Furthermore, the nature of IS mediation which is inherent within decision making and workflow-based business processes, provides the basis for evaluation of the effects of information and knowledge integration. Hence, the authors propose a conceptual, holistic evaluation framework which encompasses these ideas. It is thus argued that such trends, and their implications regarding enterprise IS integration to engender sustainable competitive advantage, require fundamental re-thinking
A Framework for Evaluating Model-Driven Self-adaptive Software Systems
In the last few years, Model Driven Development (MDD), Component-based
Software Development (CBSD), and context-oriented software have become
interesting alternatives for the design and construction of self-adaptive
software systems. In general, the ultimate goal of these technologies is to be
able to reduce development costs and effort, while improving the modularity,
flexibility, adaptability, and reliability of software systems. An analysis of
these technologies shows them all to include the principle of the separation of
concerns, and their further integration is a key factor to obtaining
high-quality and self-adaptable software systems. Each technology identifies
different concerns and deals with them separately in order to specify the
design of the self-adaptive applications, and, at the same time, support
software with adaptability and context-awareness. This research studies the
development methodologies that employ the principles of model-driven
development in building self-adaptive software systems. To this aim, this
article proposes an evaluation framework for analysing and evaluating the
features of model-driven approaches and their ability to support software with
self-adaptability and dependability in highly dynamic contextual environment.
Such evaluation framework can facilitate the software developers on selecting a
development methodology that suits their software requirements and reduces the
development effort of building self-adaptive software systems. This study
highlights the major drawbacks of the propped model-driven approaches in the
related works, and emphasise on considering the volatile aspects of
self-adaptive software in the analysis, design and implementation phases of the
development methodologies. In addition, we argue that the development
methodologies should leave the selection of modelling languages and modelling
tools to the software developers.Comment: model-driven architecture, COP, AOP, component composition,
self-adaptive application, context oriented software developmen
Context-aware adaptation in DySCAS
DySCAS is a dynamically self-configuring middleware for automotive control systems. The addition of autonomic, context-aware dynamic configuration to automotive control systems brings a potential for a wide range of benefits in terms of robustness, flexibility, upgrading etc. However, the automotive systems represent a particularly challenging domain for the deployment of autonomics concepts, having a combination of real-time performance constraints, severe resource limitations, safety-critical aspects and cost pressures. For these reasons current systems are statically configured. This paper describes the dynamic run-time configuration aspects of DySCAS and focuses on the extent to which context-aware adaptation has been achieved in DySCAS, and the ways in which the various design and implementation challenges are met
Transfer Learning for Improving Model Predictions in Highly Configurable Software
Modern software systems are built to be used in dynamic environments using
configuration capabilities to adapt to changes and external uncertainties. In a
self-adaptation context, we are often interested in reasoning about the
performance of the systems under different configurations. Usually, we learn a
black-box model based on real measurements to predict the performance of the
system given a specific configuration. However, as modern systems become more
complex, there are many configuration parameters that may interact and we end
up learning an exponentially large configuration space. Naturally, this does
not scale when relying on real measurements in the actual changing environment.
We propose a different solution: Instead of taking the measurements from the
real system, we learn the model using samples from other sources, such as
simulators that approximate performance of the real system at low cost. We
define a cost model that transform the traditional view of model learning into
a multi-objective problem that not only takes into account model accuracy but
also measurements effort as well. We evaluate our cost-aware transfer learning
solution using real-world configurable software including (i) a robotic system,
(ii) 3 different stream processing applications, and (iii) a NoSQL database
system. The experimental results demonstrate that our approach can achieve (a)
a high prediction accuracy, as well as (b) a high model reliability.Comment: To be published in the proceedings of the 12th International
Symposium on Software Engineering for Adaptive and Self-Managing Systems
(SEAMS'17
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