12,073 research outputs found
Continuous Improvement Through Knowledge-Guided Analysis in Experience Feedback
Continuous improvement in industrial processes is increasingly a key element of competitiveness for industrial systems. The management of experience feedback in this framework is designed to build, analyze and facilitate the knowledge sharing among problem solving practitioners of an organization in order to improve processes and products achievement. During Problem Solving Processes, the intellectual investment of experts is often considerable and the opportunities for expert knowledge exploitation are numerous: decision making, problem solving under uncertainty, and expert configuration. In this paper, our contribution relates to the structuring of a cognitive experience feedback framework, which allows a flexible exploitation of expert knowledge during Problem Solving Processes and a reuse such collected experience. To that purpose, the proposed approach uses the general principles of root cause analysis for identifying the root causes of problems or events, the conceptual graphs formalism for the semantic conceptualization of the domain vocabulary and the Transferable Belief Model for the fusion of information from different sources. The underlying formal reasoning mechanisms (logic-based semantics) in conceptual graphs enable intelligent information retrieval for the effective exploitation of lessons learned from past projects. An example will illustrate the application of the proposed approach of experience feedback processes formalization in the transport industry sector
Context Aware Computing for The Internet of Things: A Survey
As we are moving towards the Internet of Things (IoT), the number of sensors
deployed around the world is growing at a rapid pace. Market research has shown
a significant growth of sensor deployments over the past decade and has
predicted a significant increment of the growth rate in the future. These
sensors continuously generate enormous amounts of data. However, in order to
add value to raw sensor data we need to understand it. Collection, modelling,
reasoning, and distribution of context in relation to sensor data plays
critical role in this challenge. Context-aware computing has proven to be
successful in understanding sensor data. In this paper, we survey context
awareness from an IoT perspective. We present the necessary background by
introducing the IoT paradigm and context-aware fundamentals at the beginning.
Then we provide an in-depth analysis of context life cycle. We evaluate a
subset of projects (50) which represent the majority of research and commercial
solutions proposed in the field of context-aware computing conducted over the
last decade (2001-2011) based on our own taxonomy. Finally, based on our
evaluation, we highlight the lessons to be learnt from the past and some
possible directions for future research. The survey addresses a broad range of
techniques, methods, models, functionalities, systems, applications, and
middleware solutions related to context awareness and IoT. Our goal is not only
to analyse, compare and consolidate past research work but also to appreciate
their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201
On the similarity relation within fuzzy ontology components
Ontology reuse is an important research issue. Ontology
merging, integration, mapping, alignment and versioning
are some of its subprocesses. A considerable research work has
been conducted on them. One common issue to these subprocesses
is the problem of defining similarity relations among ontologies
components. Crisp ontologies become less suitable in all domains
in which the concepts to be represented have vague, uncertain
and imprecise definitions. Fuzzy ontologies are developed to
cope with these aspects. They are equally concerned with the
problem of ontology reuse. Defining similarity relations within
fuzzy context may be realized basing on the linguistic similarity
among ontologies components or may be deduced from their
intentional definitions. The latter approach needs to be dealt
with differently in crisp and fuzzy ontologies. This is the scope
of this paper.ou
Crowd-Sourcing Fuzzy and Faceted Classification for Concept Search
Searching for concepts in science and technology is often a difficult task.
To facilitate concept search, different types of human-generated metadata have
been created to define the content of scientific and technical disclosures.
Classification schemes such as the International Patent Classification (IPC)
and MEDLINE's MeSH are structured and controlled, but require trained experts
and central management to restrict ambiguity (Mork, 2013). While unstructured
tags of folksonomies can be processed to produce a degree of structure
(Kalendar, 2010; Karampinas, 2012; Sarasua, 2012; Bragg, 2013) the freedom
enjoyed by the crowd typically results in less precision (Stock 2007).
Existing classification schemes suffer from inflexibility and ambiguity.
Since humans understand language, inference, implication, abstraction and hence
concepts better than computers, we propose to harness the collective wisdom of
the crowd. To do so, we propose a novel classification scheme that is
sufficiently intuitive for the crowd to use, yet powerful enough to facilitate
search by analogy, and flexible enough to deal with ambiguity. The system will
enhance existing classification information. Linking up with the semantic web
and computer intelligence, a Citizen Science effort (Good, 2013) would support
innovation by improving the quality of granted patents, reducing duplicitous
research, and stimulating problem-oriented solution design.
A prototype of our design is in preparation. A crowd-sourced fuzzy and
faceted classification scheme will allow for better concept search and improved
access to prior art in science and technology
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