scholarly journals A Cost Effective Virtual Cluster with Hadoop Framework for Big Data Analytics

2015 ◽  
Vol 8 (6) ◽  
pp. 199-214
Author(s):  
Seraj Al Mahmud Mostafa ◽  
A. B. M Moniruzzaman
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Cost Effective ◽  
Virtual Cluster ◽  
Hadoop Framework

scholarly journals Optimizing Hadoop Performance for Big Data Analytics in Smart Grid

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Mukhtaj Khan ◽  
Zhengwen Huang ◽  
Maozhen Li ◽  
Gareth A. Taylor ◽  
Phillip M. Ashton ◽  
...  
Keyword(s):  
Big Data ◽  
Smart Grid ◽  
Power Systems ◽  
Data Analytics ◽  
High Performance ◽  
Gene Expression Programming ◽  
Big Data Analytics ◽  
Fluctuation Analysis ◽  
Grid Applications ◽  
Hadoop Framework

The rapid deployment of Phasor Measurement Units (PMUs) in power systems globally is leading to Big Data challenges. New high performance computing techniques are now required to process an ever increasing volume of data from PMUs. To that extent the Hadoop framework, an open source implementation of the MapReduce computing model, is gaining momentum for Big Data analytics in smart grid applications. However, Hadoop has over 190 configuration parameters, which can have a significant impact on the performance of the Hadoop framework. This paper presents an Enhanced Parallel Detrended Fluctuation Analysis (EPDFA) algorithm for scalable analytics on massive volumes of PMU data. The novel EPDFA algorithm builds on an enhanced Hadoop platform whose configuration parameters are optimized by Gene Expression Programming. Experimental results show that the EPDFA is 29 times faster than the sequential DFA in processing PMU data and 1.87 times faster than a parallel DFA, which utilizes the default Hadoop configuration settings.

scholarly journals Novel holistic architecture for analytical operation on sensory data relayed as cloud services

2020 ◽  
Vol 10 (4) ◽  
pp. 4322
Author(s):  
Manujakshi B. C ◽  
K. B. Ramesh
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Large Scale ◽  
Big Data Analytics ◽  
Cost Effective ◽  
Cloud Services ◽  
Sensor Nodes ◽  
Sensory Data ◽  
Energy Constrained ◽  
Analytical Operation

With increasing adoption of the sensor-based application, there is an exponential rise of the sensory data that eventually take the shape of the big data. However, the practicality of executing high end analytical operation over the resource-constrained big data has never being studied closely. After reviewing existing approaches, it is explored that there is no cost effective schemes of big data analytics over large scale sensory data processiing that can be directly used as a service. Therefore, the propsoed system introduces a holistic architecture where streamed data after performing extraction of knowedge can be offered in the form of services. Implemented in MATLAB, the proposed study uses a very simplistic approach considering energy constrained of the sensor nodes to find that proposed system offers better accuracy, reduced mining duration (i.e. faster response time), and reduced memory dependencies to prove that it offers cost effective analytical solution in contrast to existing system.

scholarly journals Big Data Analytics and Processing Platform in Czech Republic Healthcare

2020 ◽  
Vol 10 (5) ◽  
pp. 1705
Author(s):  
Martin Štufi ◽  
Boris Bačić ◽  
Leonid Stoimenov
Keyword(s):  
Big Data ◽  
Czech Republic ◽  
Data Analytics ◽  
High Performance ◽  
Big Data Analytics ◽  
Cost Effective ◽  
The European Union ◽  
Production Environment ◽  
The Czech Republic ◽  
Data Mining Algorithms

Big data analytics (BDA) in healthcare has made a positive difference in the integration of Artificial Intelligence (AI) in advancements of analytical capabilities, while lowering the costs of medical care. The aim of this study is to improve the existing healthcare eSystem by implementing a Big Data Analytics (BDA) platform and to meet the requirements of the Czech Republic National Health Service (Tender-Id. VZ0036628, No. Z2017-035520). In addition to providing analytical capabilities on Linux platforms supporting current and near-future AI with machine-learning and data-mining algorithms, there is the need for ethical considerations mandating new ways to preserve privacy, all of which are preconditioned by the growing body of regulations and expectations. The presented BDA platform, has met all requirements (N > 100), including the healthcare industry-standard Transaction Processing Performance Council (TPC-H) decision support benchmark in compliance with the European Union (EU) and the Czech Republic legislations. Currently, the presented Proof of Concept (PoC) that has been upgraded to a production environment has unified isolated parts of Czech Republic healthcare over the past seven months. The reported PoC BDA platform, artefacts, and concepts are transferrable to healthcare systems in other countries interested in developing or upgrading their own national healthcare infrastructure in a cost-effective, secure, scalable and high-performance manner.

Cost-Effective Cloud Server Provisioning for Predictable Performance of Big Data Analytics

2019 ◽  
Vol 30 (5) ◽  
pp. 1036-1051 ◽  
Author(s):  
Fei Xu ◽  
Haoyue Zheng ◽  
Huan Jiang ◽  
Wujie Shao ◽  
Haikun Liu ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Cost Effective ◽  
Cloud Server

scholarly journals Big Data and Personalisation for Non-Intrusive Smart Home Automation

2021 ◽  
Vol 5 (1) ◽  
pp. 6
Author(s):  
Suriya Priya R. Asaithambi ◽  
Sitalakshmi Venkatraman ◽  
Ramanathan Venkatraman
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Smart Home ◽  
Big Data Analytics ◽  
Cost Effective ◽  
Smart Devices ◽  
Home Automation ◽  
Open Standard ◽  
Iot Devices ◽  
And Control

With the advent of the Internet of Things (IoT), many different smart home technologies are commercially available. However, the adoption of such technologies is slow as many of them are not cost-effective and focus on specific functions such as energy efficiency. Recently, IoT devices and sensors have been designed to enhance the quality of personal life by having the capability to generate continuous data streams that can be used to monitor and make inferences by the user. While smart home devices connect to the home Wi-Fi network, there are still compatibility issues between devices from different manufacturers. Smart devices get even smarter when they can communicate with and control each other. The information collected by one device can be shared with others for achieving an enhanced automation of their operations. This paper proposes a non-intrusive approach of integrating and collecting data from open standard IoT devices for personalised smart home automation using big data analytics and machine learning. We demonstrate the implementation of our proposed novel technology instantiation approach for achieving non-intrusive IoT based big data analytics with a use case of a smart home environment. We employ open-source frameworks such as Apache Spark, Apache NiFi and FB-Prophet along with popular vendor tech-stacks such as Azure and DataBricks.

scholarly journals Hadoop Framework for Big Data Analytics to Analyze Attacks on Business Data

2019 ◽  
Vol 9 (2) ◽  
pp. 3995-4000
Keyword(s):  
Big Data ◽  
Data Security ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Sets ◽  
Business Data ◽  
Analytical Tools ◽  
Security Analytics ◽  
Day By Day ◽  
Hadoop Framework

Big Data consist large volumes of data sets with various formats i.e., structured, unstructured and semi structured. Big Data requires security because day by day attackers attack on it in different manner. Big Data Security Analytics analyses Big Data for finding various threats and complex attacks. By increasing the number of targeting attacks on data and one side rapid growing of data, it is too difficult to analyze accurately. The Security Analytics Systems are used the untrusted data. So, strong security analytical tools are required to analyze the data. The organizations and industries exchange the data through networks dynamically, so this may become more vulnerable for data misusing and theft. Attackers are more advanced in the attacking on data that the existing security mechanisms are not identified before damaging. At present, the collecting and analyzing various attacks is major challenging task for Security Analytics Systems, to take suitable decision. In this research paper, we have addressed about Hadoop tool that how it analyses Big Data and how Big Data Security Analytics is applied to analyze the various threats and securing the business data more accurately.

scholarly journals A Review Paper On Big Data Analytics in Cloud

2021 ◽  
pp. 477-483
Author(s):  
Shaheen Mohsin Ansari
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Review Paper ◽  
Big Data Analytics ◽  
Cost Effective ◽  
Choice Data ◽  
Computing Services ◽  
Cloud Computing Services ◽  
Data As A Service ◽  
Cloud Technologies

The amount of data produced in the enterprise is increasing. Any industry will have to cope with exploding data volumes in the future, which will accelerate exponential data growth. It is critical to use a cost-effective, flexible approach for storing and analyzing this data. As a service to big data, the cloud will offer storage, platform, and software capabilities. Big data and cloud technologies are combining to make big data analytics in the cloud a viable choice. Data Analytics as a Service is another name for Cloud for Big Data Analytics. In this review paper we will get to know how big data analytics used cloud computing services for better performance or experience with their benefits, challenges and so on.

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