scholarly journals The Metamorphosis (of RAM3S)

2021 ◽  
Vol 11 (24) ◽  
pp. 11584
Author(s):  
Ilaria Bartolini ◽  
Marco Patella
Keyword(s):  
Big Data ◽  
Real Time ◽  
Data Streams ◽  
Multimedia Data ◽  
Data Streaming ◽  
Time Analysis ◽  
Real Time Analysis ◽  
Big Data Technologies ◽  
Big Data Streams ◽  
The One

The real-time analysis of Big Data streams is a terrific resource for transforming data into value. For this, Big Data technologies for smart processing of massive data streams are available, but the facilities they offer are often too raw to be effectively exploited by analysts. RAM3S (Real-time Analysis of Massive MultiMedia Streams) is a framework that acts as a middleware software layer between multimedia stream analysis techniques and Big Data streaming platforms, so as to facilitate the implementation of the former on top of the latter. RAM3S has been proven helpful in simplifying the deployment of non-parallel techniques to streaming platforms, such as Apache Storm or Apache Flink. In this paper, we show how RAM3S has been updated to incorporate novel stream processing platforms, such as Apache Samza, and to be able to communicate with different message brokers, such as Apache Kafka. Abstracting from the message broker also provides us with the ability to pipeline several RAM3S instances that can, therefore, perform different processing tasks. This represents a richer model for stream analysis with respect to the one already available in the original RAM3S version. The generality of this new RAM3S version is demonstrated through experiments conducted on three different multimedia applications, proving that RAM3S is a formidable asset for enabling efficient and effective Data Mining and Machine Learning on multimedia data streams.

Extending Integrability of Nonlinear Water Wave Equations: Nonlinear Fourier Analysis of Breather Packets and Rogue Waves at Higher Order

2019 ◽  
Author(s):  
Alfred R. Osborne
Keyword(s):  
Real Time ◽  
Theta Function ◽  
Bilinear Form ◽  
Wave Equations ◽  
Higher Order ◽  
Plane Boundary ◽  
Integrable Case ◽  
Time Analysis ◽  
Real Time Analysis ◽  
The One

Abstract I suggest a formulation to give approximate spectral solutions of nonintegrable, nonlinear wave equations in 2+1 dimensions. Nonintegrable systems such as the 2+1 NLS, Dysthe and extended Dysthe equations can be approximately integrated by selecting a nearby theta function formulation. I study the subclass of wave equations that are in the form of nonlinear envelope equations for which all members can be reduced to a particular Hirota bilinear form. To find the approximately integrable formulation associated with a nonintegrable equation, I first study the one and two soliton solutions and subsequently extend these to larger numbers of solitons to obtain the Hirota N-soliton solution (for infinite-plane boundary conditions). Subsequently, I address the one and two periodic solutions from the bilinear form, so that I can develop the associated Riemann theta function solution to a nearby integrable case. I discuss how to obtain the higher order breather packets from the point of view of the theta functions. This work is being developed for real time analysis of shipboard radar analysis of ocean waves. Further applications include real time analysis of lidar and synthetic aperture radar (SAR) data taken by airplanes flying over high sea states.

scholarly journals Integration of Microresonant Sensor into a Microfluidic Platform for the Real Time Analysis of Platelets-Collagen Interaction in Flow Condition

Proceedings ◽  
2018 ◽  
Vol 2 (13) ◽  
pp. 940
Author(s):  
Aleksandr Oseev ◽  
Benoît Le Roy de Boiseaumarié ◽  
Fabien Remy-Martin ◽  
Jean-François Manceau ◽  
Alain Rouleau ◽  
...  
Keyword(s):  
Real Time ◽  
Flow Condition ◽  
Flow Conditions ◽  
Time Analysis ◽  
Flow Rates ◽  
Microfluidic Platform ◽  
Real Time Analysis ◽  
Controlled Flow ◽  
The One

The contribution focuses on the development of microresonant sensor solution integrated in microfluidic platform for the haemostasis assessment at realistic rheological flow conditions similar to the one in blood vessels. A multi-parameter sensor performs real time analysis of interactions between immobilized collagen and platelets. The detection and characterization of such interactions at controlled flow rates provide information to evaluate the dynamic of each step of primary haemostasis. The microresonant sensor concept was developed and is described in the contribution.

scholarly journals A Distributed Stream Processing Middleware Framework for Real-Time Analysis of Heterogeneous Data on Big Data Platform: Case of Environmental Monitoring

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3166
Author(s):  
Adeyinka Akanbi ◽  
Muthoni Masinde
Keyword(s):  
Big Data ◽  
Environmental Monitoring ◽  
Real Time ◽  
Stream Processing ◽  
Heterogeneous Data ◽  
Legacy Systems ◽  
Time Analysis ◽  
Real Time Analysis ◽  
Distributed Stream Processing ◽  
Processing Engine

In recent years, the application and wide adoption of Internet of Things (IoT)-based technologies have increased the proliferation of monitoring systems, which has consequently exponentially increased the amounts of heterogeneous data generated. Processing and analysing the massive amount of data produced is cumbersome and gradually moving from classical ‘batch’ processing—extract, transform, load (ETL) technique to real-time processing. For instance, in environmental monitoring and management domain, time-series data and historical dataset are crucial for prediction models. However, the environmental monitoring domain still utilises legacy systems, which complicates the real-time analysis of the essential data, integration with big data platforms and reliance on batch processing. Herein, as a solution, a distributed stream processing middleware framework for real-time analysis of heterogeneous environmental monitoring and management data is presented and tested on a cluster using open source technologies in a big data environment. The system ingests datasets from legacy systems and sensor data from heterogeneous automated weather systems irrespective of the data types to Apache Kafka topics using Kafka Connect APIs for processing by the Kafka streaming processing engine. The stream processing engine executes the predictive numerical models and algorithms represented in event processing (EP) languages for real-time analysis of the data streams. To prove the feasibility of the proposed framework, we implemented the system using a case study scenario of drought prediction and forecasting based on the Effective Drought Index (EDI) model. Firstly, we transform the predictive model into a form that could be executed by the streaming engine for real-time computing. Secondly, the model is applied to the ingested data streams and datasets to predict drought through persistent querying of the infinite streams to detect anomalies. As a conclusion of this study, a performance evaluation of the distributed stream processing middleware infrastructure is calculated to determine the real-time effectiveness of the framework.

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