The [email protected] paradigm promotes the use of models during the execution of cyber-physical systems to represent their context and to reason about their runtime behaviour. However, current modeling techniques do ...
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ISBN:
(纸本)9781467369084
The [email protected] paradigm promotes the use of models during the execution of cyber-physical systems to represent their context and to reason about their runtime behaviour. However, current modeling techniques do not allow to cope at the same time with the large-scale, distributed, and constantly changing nature of these systems. In this paper, we introduce a distributed [email protected] approach, combining ideas from reactive programming, peer-to-peer distribution, and large-scale [email protected] We define distributed models as observable streams of chunks that are exchanged between nodes in a peer-to-peer manner. A lazy loading strategy allows to transparently access the complete virtual model from every node, although chunks are actually distributed across nodes. Observers and automatic reloading of chunks enable a reactive programming style. We integrated our approach into the Kevoree Modeling Framework and demonstrate that it enables frequently changing, reactive distributed models that can scale to millions of elements and several thousand nodes.
The Internet of Things (IoT) paradigm has given rise to a new class of applications wherein complex data analytics must be performed in real-time on large volumes of fast-moving and heterogeneous sensor-generated data...
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ISBN:
(纸本)9781450332866
The Internet of Things (IoT) paradigm has given rise to a new class of applications wherein complex data analytics must be performed in real-time on large volumes of fast-moving and heterogeneous sensor-generated data. Such data streams are often unbounded and must be processed in a distributed and parallel manner to ensure timely processing and delivery to interested subscribers. Dataflow architectures based on event-based design have served well in such applications because events support asynchrony, loose coupling, and helps build resilient, responsive and scalable applications. However, a unified programming model for event processing and distribution that can naturally compose the processing stages in a dataflow while exploiting the inherent parallelism available in the environment and computation is still lacking. To that end, we investigate the benefits of blending reactive programming with data distribution frameworks for building distributed, reactive, and high-performance stream-processing applications. Specifically, we present insights from our study integrating and evaluating Microsoft .NET reactive Extensions (Rx) with OMG Data Distribution Service (DDS), which is a standards-based publish/subscribe middleware suitable for demanding industrial IoT applications. Several key insights from both qualitative and quantitative evaluation of our approach are presented.
Epilepsy is a common neural disorder disease, while difficult to cure. There is still a risk of suffering from seizures even though patients have used antiepileptic drugs or had an operation. In such cases, patients m...
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ISBN:
(纸本)9781509048984
Epilepsy is a common neural disorder disease, while difficult to cure. There is still a risk of suffering from seizures even though patients have used antiepileptic drugs or had an operation. In such cases, patients must be immediately taken care;on the other hand, how to avoid introducing danger to their family and people around is also a concern. To address this issue, we integrate and develop a health cloud system to detect, record, and further predict epileptic seizure, which includes a dedicated wearable device for detection, a medical-IoT box to avoid heavy computation on the wearable device and provide cross reference to cameras, a cloud computing platform for complex computation, and an application on tablets for health care professionals. We propose a reactive and highly programmable model for such a system to allow health care professionals to easily and quickly query data from different devices and customize the trigger conditions, while optimize computing resource to achieve power saving on devices. We base this research on reactive programming (RP), which recently attracts the interests of researchers and developers, to construct our model, and develop a domain-specific language (DSL) that is applied among the medical-IoT box, cloud computing, and user interface for health care professionals. Such a DSL must be easy-to-write since health care professionals are not necessarily experts in programming, but it must also be powerful enough to allow them to query the logging data, analyze the interaction between different devices, and further configure the setting of devices for individual patients to benefit from our platform. At the same time, it automatically optimizes the communication and computation based the trigger conditions to achieve power saving on devices.
Beamline is a software library to support the research and development of streaming process mining algorithms. Specifically, it comprises a Java library, built on top of Apache Flink, which fosters high performance an...
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Beamline is a software library to support the research and development of streaming process mining algorithms. Specifically, it comprises a Java library, built on top of Apache Flink, which fosters high performance and deployment. The second component is a Python library (called pyBeamline, built using reactiveX) which allows the quick prototyping and development of new streaming process mining algorithms. The two libraries share the same underlying data structures (BEvent) as well as the same fundamental principles, thus making the prototypes (built by researchers using pyBeamline) quickly transferrable to full-fledged and highly scalable applications (using Java Beamline).
Variational Bayesian (VB) inference has become an increasingly popular method for approximating exact Bayesian inference in model-based machine learning. The VB approach provides a way to trade off accuracy versus com...
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Variational Bayesian (VB) inference has become an increasingly popular method for approximating exact Bayesian inference in model-based machine learning. The VB approach provides a way to trade off accuracy versus computational complexity and scales better to large-dimensional inference problems than sampling solutions. The Julia package *** implements and automates reactive VB inference by minimization of a constrained Bethe Free Energy functional through message passing on a factor graph representation of a probabilistic model. Moreover, through support for specification of explicit constraints on the Free Energy functional, *** allows for comparative analysis of different variational cost function proposals.
SugarCubes is a set of Java classes used to implement dynamic, reactive, event-based, parallel systems. SugarCubes can be seen as a low-level basis upon which more complex reactive formalisms can be implemented. It al...
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