35,778 research outputs found
Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure
Big data research has attracted great attention in science, technology,
industry and society. It is developing with the evolving scientific paradigm,
the fourth industrial revolution, and the transformational innovation of
technologies. However, its nature and fundamental challenge have not been
recognized, and its own methodology has not been formed. This paper explores
and answers the following questions: What is big data? What are the basic
methods for representing, managing and analyzing big data? What is the
relationship between big data and knowledge? Can we find a mapping from big
data into knowledge space? What kind of infrastructure is required to support
not only big data management and analysis but also knowledge discovery, sharing
and management? What is the relationship between big data and science paradigm?
What is the nature and fundamental challenge of big data computing? A
multi-dimensional perspective is presented toward a methodology of big data
computing.Comment: 59 page
Evolving artificial cell signaling networks using molecular classifier systems
Nature is a source of inspiration for computational techniques which have been successfully applied to a wide variety of complex application domains. In keeping with this we examine Cell Signaling Networks (CSN) which are chemical networks responsible for coordinating cell activities within their environment. Through evolution they have become highly efficient for governing critical control processes such as immunological responses, cell cycle control or homeostasis. Realising (and evolving) Artificial Cell Signaling Networks (ACSNs) may provide new computational paradigms for a variety of application areas. Our abstraction of Cell Signaling Networks focuses on four characteristic properties distinguished as follows: Computation, Evolution, Crosstalk and Robustness. These properties are also desirable for potential applications in the control systems, computation and signal processing field. These characteristics are used as a guide for the development of an ACSN evolutionary simulation platform. In this paper we present a novel evolutionary approach named Molecular Classifier System (MCS) to simulate such ACSNs. The MCS that we have designed is derived from Holland's Learning Classifier System. The research we are currently involved in is part of the multi disciplinary European funded project, ESIGNET, with the central question of the study of the computational properties of CSNs by evolving them using methods from evolutionary computation, and to re-apply this understanding in developing new ways to model and predict real CSNs
Ensuring Query Compatibility with Evolving XML Schemas
During the life cycle of an XML application, both schemas and queries may
change from one version to another. Schema evolutions may affect query results
and potentially the validity of produced data. Nowadays, a challenge is to
assess and accommodate the impact of theses changes in rapidly evolving XML
applications.
This article proposes a logical framework and tool for verifying
forward/backward compatibility issues involving schemas and queries. First, it
allows analyzing relations between schemas. Second, it allows XML designers to
identify queries that must be reformulated in order to produce the expected
results across successive schema versions. Third, it allows examining more
precisely the impact of schema changes over queries, therefore facilitating
their reformulation
Reflecting on Experiential Learning in Marketing Education
© Westburn Publishers Ltd 2014. This is the preprint (pre peer-review) version of an article which has been published in its definitive form in Marketing Review, and has been posted by permission of Westburn Publishers Ltd for personal use, not for redistribution. The article was published in [Marketing Review, Vol.14, No.1, pp.97-108, http://dx.doi.org/10.1362/146934714X13948909473266"Experiential learning methods have become increasingly popular in marketing education. Factors underlying this trend are: the desire to respond to the changing higher education environment (the student-customer); the need to endow students with employability skills; the common sense assumption that since marketing is a practical activity, learning from experience makes sense; and, pedagogic methods designed around experiential learning theory which has been widely influential in recent decades. While not seeking to argue that experiential learning methods are ineffective in marketing education, this article argues that they should be used thoughtfully and where the learning goals and the cohort of students are likely to benefit from them. In particular, marketing educators should be wary of imposing an excessively high cognitive load on their students by expecting them to learn complex concepts from experiential learning methods that themselves have an intrinsically sharp learning curve, such as client consultancy projectsNon peer reviewedSubmitted Versio
A System for Deduction-based Formal Verification of Workflow-oriented Software Models
The work concerns formal verification of workflow-oriented software models
using deductive approach. The formal correctness of a model's behaviour is
considered. Manually building logical specifications, which are considered as a
set of temporal logic formulas, seems to be the significant obstacle for an
inexperienced user when applying the deductive approach. A system, and its
architecture, for the deduction-based verification of workflow-oriented models
is proposed. The process of inference is based on the semantic tableaux method
which has some advantages when compared to traditional deduction strategies.
The algorithm for an automatic generation of logical specifications is
proposed. The generation procedure is based on the predefined workflow patterns
for BPMN, which is a standard and dominant notation for the modeling of
business processes. The main idea for the approach is to consider patterns,
defined in terms of temporal logic,as a kind of (logical) primitives which
enable the transformation of models to temporal logic formulas constituting a
logical specification. Automation of the generation process is crucial for
bridging the gap between intuitiveness of the deductive reasoning and the
difficulty of its practical application in the case when logical specifications
are built manually. This approach has gone some way towards supporting,
hopefully enhancing our understanding of, the deduction-based formal
verification of workflow-oriented models.Comment: International Journal of Applied Mathematics and Computer Scienc
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