22,042 research outputs found
Actors vs Shared Memory: two models at work on Big Data application frameworks
This work aims at analyzing how two different concurrency models, namely the
shared memory model and the actor model, can influence the development of
applications that manage huge masses of data, distinctive of Big Data
applications. The paper compares the two models by analyzing a couple of
concrete projects based on the MapReduce and Bulk Synchronous Parallel
algorithmic schemes. Both projects are doubly implemented on two concrete
platforms: Akka Cluster and Managed X10. The result is both a conceptual
comparison of models in the Big Data Analytics scenario, and an experimental
analysis based on concrete executions on a cluster platform
An ontological view in telemedicine.
The verification and validation of information system models impact on the adequacy and appropriateness of using the value of telemedicine services for continuously optimizing healthcare outcomes. We have defined a methodology to help the modeling and rigorous analysis of the requirements of information systems in telemedicine. On one hand, this methodology will be based on a formal representation of requirements (systemic, generic domain, etc.) within a knowledge base that will be a requirements repository. On the other hand, this methodology will use conceptual graphs for the formalization of ontology of activities and the production of arguments related to the formal verification of models built from this ontology. We describe an example illustrating the engagement of conceptual graph procedures to model the contextual situations in the telemedicine development. We also discuss the way in which ethical issues will actually take place in telemedicine applications
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Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: NL
Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: N
Business Intelligence Healthcare Model: Getting the Right Requirements for Malaysian Rural Citizens
It is a big challenge for Malaysian healthcare authorities in providing services to rural communities. To meet up with the challenges, a concept called business intelligence (BI) is employed for their informed decision-making that utilizes their enormous data. However, only a few of BI initiatives have their success stories as many are still struggling to justify the investments. Among the most reason of the failures were that BI requirements were overlooked, leading to poor BI deployments. Taking rural healthcare in Malaysia as a case study, the paper attempts to model BI requirements using goal-oriented approach. Rural healthcare BI requirements were modelled two-folds: (1) decision making requirements, cantered on stakeholders; and (2) BI data requirements, focused on organizational and decisional aspects. The model can guide BI developers on the process and data needed in rural healthcare strategic decision-making. Theoretically it provides new insights and facilitates the improvement of new healthcare knowledge
Revisiting Actor Programming in C++
The actor model of computation has gained significant popularity over the
last decade. Its high level of abstraction makes it appealing for concurrent
applications in parallel and distributed systems. However, designing a
real-world actor framework that subsumes full scalability, strong reliability,
and high resource efficiency requires many conceptual and algorithmic additives
to the original model.
In this paper, we report on designing and building CAF, the "C++ Actor
Framework". CAF targets at providing a concurrent and distributed native
environment for scaling up to very large, high-performance applications, and
equally well down to small constrained systems. We present the key
specifications and design concepts---in particular a message-transparent
architecture, type-safe message interfaces, and pattern matching
facilities---that make native actors a viable approach for many robust,
elastic, and highly distributed developments. We demonstrate the feasibility of
CAF in three scenarios: first for elastic, upscaling environments, second for
including heterogeneous hardware like GPGPUs, and third for distributed runtime
systems. Extensive performance evaluations indicate ideal runtime behaviour for
up to 64 cores at very low memory footprint, or in the presence of GPUs. In
these tests, CAF continuously outperforms the competing actor environments
Erlang, Charm++, SalsaLite, Scala, ActorFoundry, and even the OpenMPI.Comment: 33 page
Modelling and analyzing adaptive self-assembling strategies with Maude
Building adaptive systems with predictable emergent behavior is a challenging task and it is becoming a critical need. The research community has accepted the challenge by introducing approaches of various nature: from software architectures, to programming paradigms, to analysis techniques. We recently proposed a conceptual framework for adaptation centered around the role of control data. In this paper we show that it can be naturally realized in a reflective logical language like Maude by using the Reflective Russian Dolls model. Moreover, we exploit this model to specify, validate and analyse a prominent example of adaptive system: robot swarms equipped with self-assembly strategies. The analysis exploits the statistical model checker PVeStA
Applications of Machine Learning to Threat Intelligence, Intrusion Detection and Malware
Artificial Intelligence (AI) and Machine Learning (ML) are emerging technologies with applications to many fields. This paper is a survey of use cases of ML for threat intelligence, intrusion detection, and malware analysis and detection. Threat intelligence, especially attack attribution, can benefit from the use of ML classification. False positives from rule-based intrusion detection systems can be reduced with the use of ML models. Malware analysis and classification can be made easier by developing ML frameworks to distill similarities between the malicious programs. Adversarial machine learning will also be discussed, because while ML can be used to solve problems or reduce analyst workload, it also introduces new attack surfaces
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