62,736 research outputs found
Data-Driven Computational Intelligence for Scientific Programming
Rubio-Largo, Á., Preciado, J. C., & Iribarne, L. (2019). Data-Driven Computational Intelligence for Scientific Programming. Scientific Programming,[5235706].[Editorial]. Doi: https://doi.org/10.1155/2019/5235706publishersversionpublishe
Facilitating the Quantitative Analysis ofComplexEvents through a Computational Intelligence Model-Driven Tool
Complex event processing (CEP) is a computational intelligence technology capable of analyzing big data streams for event
pattern recognition in real time. In particular, this technology is vastly useful for analyzing multicriteria conditions in a pattern,
which will trigger alerts (complex events) upon their fulfillment. However, one of the main challenges to be faced by CEP is how to
define the quantitative analysis to be performed in response to the produced complex events. In this paper, we propose the use of
the MEdit4CEP-CPN model-driven tool as a solution for conducting such quantitative analysis of events of interest for an
application domain, without requiring knowledge of any scientific programming language for implementing the pattern
conditions. Precisely, MEdit4CEP-CPN facilitates domain experts to graphically model event patterns, transform them into a
Prioritized Colored Petri Net (PCPN) model, modify its initial marking depending on the application scenario, and make the
quantitative analysis through the simulation and monitor capabilities provided by CPN tools
Challenging the Computational Metaphor: Implications for How We Think
This paper explores the role of the traditional computational metaphor in our thinking as computer scientists, its influence on epistemological styles, and its implications for our understanding of cognition. It proposes to replace the conventional metaphor--a sequence of steps--with the notion of a community of interacting entities, and examines the ramifications of such a shift on these various ways in which we think
Improving the scalability of parallel N-body applications with an event driven constraint based execution model
The scalability and efficiency of graph applications are significantly
constrained by conventional systems and their supporting programming models.
Technology trends like multicore, manycore, and heterogeneous system
architectures are introducing further challenges and possibilities for emerging
application domains such as graph applications. This paper explores the space
of effective parallel execution of ephemeral graphs that are dynamically
generated using the Barnes-Hut algorithm to exemplify dynamic workloads. The
workloads are expressed using the semantics of an Exascale computing execution
model called ParalleX. For comparison, results using conventional execution
model semantics are also presented. We find improved load balancing during
runtime and automatic parallelism discovery improving efficiency using the
advanced semantics for Exascale computing.Comment: 11 figure
Simulation modelling and visualisation: toolkits for building artificial worlds
Simulations users at all levels make heavy use of compute resources to drive computational
simulations for greatly varying applications areas of research using different simulation
paradigms. Simulations are implemented in many software forms, ranging from highly standardised
and general models that run in proprietary software packages to ad hoc hand-crafted
simulations codes for very specific applications. Visualisation of the workings or results of a
simulation is another highly valuable capability for simulation developers and practitioners.
There are many different software libraries and methods available for creating a visualisation
layer for simulations, and it is often a difficult and time-consuming process to assemble a
toolkit of these libraries and other resources that best suits a particular simulation model. We
present here a break-down of the main simulation paradigms, and discuss differing toolkits and
approaches that different researchers have taken to tackle coupled simulation and visualisation
in each paradigm
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