Real-time big data technologies of energy internet platform

2016 ◽  
Author(s):  
Wang Guilan ◽  
Zhou Guoliang ◽  
Zhao Hongshan ◽  
Liu Hongyang
Keyword(s):  
Big Data ◽  
Real Time ◽  
Energy Internet ◽  
Big Data Technologies ◽  
Internet Platform

Big Data Analytics Framework for Real-Time Genome Analysis: A Comprehensive Approach

2019 ◽  
Vol 16 (8) ◽  
pp. 3419-3427
Author(s):  
Shishir K. Shandilya ◽  
S. Sountharrajan ◽  
Smita Shandilya ◽  
E. Suganya
Keyword(s):  
Big Data ◽  
Real Time ◽  
Genome Analysis ◽  
High Speed ◽  
High Performance ◽  
Big Data Analytics ◽  
Good Precision ◽  
Entire Genome ◽  
Big Data Technologies ◽  
Sequencing Platforms

Big Data Technologies are well-accepted in the recent years in bio-medical and genome informatics. They are capable to process gigantic and heterogeneous genome information with good precision and recall. With the quick advancements in computation and storage technologies, the cost of acquiring and processing the genomic data has decreased significantly. The upcoming sequencing platforms will produce vast amount of data, which will imperatively require high-performance systems for on-demand analysis with time-bound efficiency. Recent bio-informatics tools are capable of utilizing the novel features of Hadoop in a more flexible way. In particular, big data technologies such as MapReduce and Hive are able to provide high-speed computational environment for the analysis of petabyte scale datasets. This has attracted the focus of bio-scientists to use the big data applications to automate the entire genome analysis. The proposed framework is designed over MapReduce and Java on extended Hadoop platform to achieve the parallelism of Big Data Analysis. It will assist the bioinformatics community by providing a comprehensive solution for Descriptive, Comparative, Exploratory, Inferential, Predictive and Causal Analysis on Genome data. The proposed framework is user-friendly, fully-customizable, scalable and fit for comprehensive real-time genome analysis from data acquisition till predictive sequence analysis.

scholarly journals THE IMPACT OF BIG DATA TECHNOLOGIES ON COMPETITIVE ADVANTAGE OF COMPANIES

2017 ◽  
pp. 255 ◽  
Author(s):  
Jelena Lukić
Keyword(s):  
Big Data ◽  
Competitive Advantage ◽  
Real Time ◽  
New Technologies ◽  
Knowledge And Skills ◽  
Big Data Technologies ◽  
The Impact

The emergence of large quantity of data, from various sources, available in real-time, known as Big Data, have stimulated development of new technologies, techniques, tools, knowledge and skills which allows to work with that data. Big Data represent not only the factor from environment that confronts the companies with avalanche of data, but also very imporant resource which provide opportunities for companies to make value on the basis of collected data. Characteristics and possibilities which Big Data technologies offer have positioned them as a valuable factor for gaining and sustaining the competitive advantage in companies. The aim of this paper is to examine how Big Data technologies impact on competitive advantage of the companies that use them.

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.

scholarly journals On the use of IoT and Big Data Technologies for Real-time Monitoring and Data Processing

2017 ◽  
Vol 113 ◽  
pp. 429-434 ◽  
Author(s):  
Y. Nait Malek ◽  
A. Kharbouch ◽  
H. El Khoukhi ◽  
M. Bakhouya ◽  
V. De Florio ◽  
...  
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Real Time ◽  
Real Time Monitoring ◽  
Big Data Technologies

scholarly journals A Hadoop-Based Platform for Patient Classification and Disease Diagnosis in Healthcare Applications

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1931
Author(s):  
Hassan Harb ◽  
Hussein Mroue ◽  
Ali Mansour ◽  
Abbass Nasser ◽  
Eduardo Motta Cruz
Keyword(s):  
Big Data ◽  
Real Time ◽  
Medical Staff ◽  
Patient Monitoring ◽  
Data Retrieval ◽  
Disease Diagnosis ◽  
Patient Classification ◽  
Healthcare Applications ◽  
Number Of Patients ◽  
Big Data Technologies

Nowadays, the increasing number of patients accompanied with the emergence of new symptoms and diseases makes heath monitoring and assessment a complicated task for medical staff and hospitals. Indeed, the processing of big and heterogeneous data collected by biomedical sensors along with the need of patients’ classification and disease diagnosis become major challenges for several health-based sensing applications. Thus, the combination between remote sensing devices and the big data technologies have been proven as an efficient and low cost solution for healthcare applications. In this paper, we propose a robust big data analytics platform for real time patient monitoring and decision making to help both hospital and medical staff. The proposed platform relies on big data technologies and data analysis techniques and consists of four layers: real time patient monitoring, real time decision and data storage, patient classification and disease diagnosis, and data retrieval and visualization. To evaluate the performance of our platform, we implemented our platform based on the Hadoop ecosystem and we applied the proposed algorithms over real health data. The obtained results show the effectiveness of our platform in terms of efficiently performing patient classification and disease diagnosis in healthcare applications.

Clustering‐based real‐time anomaly detection—A breakthrough in big data technologies

2019 ◽  
pp. e3647 ◽  
Author(s):  
Riyaz Ahamed Ariyaluran Habeeb ◽  
Fariza Nasaruddin ◽  
Abdullah Gani ◽  
Mohamed Ahzam Amanullah ◽  
Ibrahim Abaker Targio Hashem ◽  
...  
Keyword(s):  
Big Data ◽  
Anomaly Detection ◽  
Real Time ◽  
Big Data Technologies

IoT and Big Data Technologies for Monitoring and Processing Real-Time Healthcare Data

2019 ◽  
Vol 10 (4) ◽  
pp. 17-30 ◽  
Author(s):  
Abdelhak Kharbouch ◽  
Youssef Naitmalek ◽  
Hamza Elkhoukhi ◽  
Mohamed Bakhouya ◽  
Vincenzo De Florio ◽  
...  
Keyword(s):  
Big Data ◽  
Real Time ◽  
Wireless Ad Hoc Networks ◽  
Ad Hoc ◽  
The Internet ◽  
Case Scenario ◽  
Healthcare Data ◽  
Big Data Technologies ◽  
Hoc Networks ◽  
Data Acquisition And Processing

Recent advances in pervasive technologies, such as wireless, ad hoc networks, and wearable sensor devices, allow the connection of everyday things to the Internet, commonly denoted as the Internet of Things (IoT). The IoT is seen as an enabler to the development of intelligent and context-aware services and applications. However, handling dynamic and frequent context changes is a difficult task without a real-time event/data acquisition and processing platform. Big data technologies and data analytics have been recently proposed for timely analyzing information (i.e., data, events) streams. The main aim is to make users' life more comfortable according to their locations, current requirements, and ongoing activities. In this article, combining IoT techniques and Big data technologies into a holistic platform for continuous and real-time health-care data monitoring and processing is introduced. Real-testing experiments have been conducted and results are reported to show the usefulness of this platform in a real-case scenario.

scholarly journals Big data traffic management in vehicular ad-hoc network

2021 ◽  
Vol 11 (4) ◽  
pp. 3483
Author(s):  
Tantaoui Mouad ◽  
Laanaoui My Driss ◽  
Kabil Mustapha
Keyword(s):  
Big Data ◽  
Real Time ◽  
Ad Hoc Network ◽  
Traffic Management ◽  
Ad Hoc ◽  
Vehicular Ad Hoc Network ◽  
Time Of Arrival ◽  
Data Traffic ◽  
Management Tools ◽  
Big Data Technologies

<span>Today, the world has experienced a new trend with regard to data system management, traditional database management tools have become outdated and they will no longer be able to process the mass of data generated by different systems, that's why big data is there to process this mass of data to bring out crucial information hidden in this data, and without big data technologies the treatment is very difficult to manage; among the domains that uses big data technologies is vehicular ad-hoc network to manage their voluminous data. In this article, we establish in the first step a method that allow to detect anomalies or accidents within the road and compute the time spent in each road section in real time, which permit us to obtain a database having the estimated time spent in all sections in real time, this will serve us to send to the vehicles the right estimated time of arrival all along their journey and the optimal route to attain their destination. This database is useful to utilize it like inputs for machine learning to predict the places and times where the probability of accidents is higher. The experimental results prove that our method permits us to avoid congestions and apportion the load of vehicles in all roads effectively, also it contributes to road safety.</span>

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