scholarly journals Research on Big Data Analytics by Using High-Level Fuzzy Petri Nets

2018 ◽  
Author(s):  
CHENG-CHAO QIU
Keyword(s):  
Big Data ◽  
Petri Nets ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Fuzzy Petri Nets ◽  
High Level

scholarly journals Effect of SBI Bank Merger on Education Loan, a Comparative Study Using Big Data Analytics

2018 ◽  
Vol 7 (2.32) ◽  
pp. 452
Author(s):  
Anjali Mathur ◽  
K Vinitha ◽  
R Shubham ◽  
K Gowtham
Keyword(s):  
Big Data ◽  
Comparative Study ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Global Level ◽  
Analysis Techniques ◽  
Bank Merger ◽  
Huge Data ◽  
High Level ◽  
The Impact

A bank merger is a situation in which two banks or all branches of a bank join together to become one bank. The bank merger of State Bank of India was implemented on 1stApril 2017 in India. The bank merger is a good idea to centralize the customer’s data from nationwide. However, it is a difficult task for administrators and technologists. Some high level techniques are required to collect the data from the branches, of the bank present at nationwide, and merge them accordingly. For this huge data Big-Data Analysis techniques can be used to manage and access the data. The big data analytics provides algorithms to compare, classify and cluster the data at local and global level. This research paper proposes big data analytics for education loan provided by State Bank of India. The loan granting process becomes centralized after merger. It affects the processing of granting a loan, as earlier it was according to branches only. The proposed work is for comparative study of the impact of bank merger on education loan provided by State Bank of India.  

scholarly journals Big Data Analytics in Healthcare

2020 ◽  
Vol 5 (1) ◽  
pp. 19-27
Author(s):  
Jaimin Navinchandra Undavia ◽  
Atul Manubhai Patel
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Heterogeneous Data ◽  
Simple Type ◽  
Healthcare Industry ◽  
Technological Advancement ◽  
Huge Amount ◽  
High Level ◽  
Almost All

The technological advancement has also opened up various ways to collect data through automatic mechanisms. One such mechanism collects a huge amount of data without any further maintenance or human interventions. The health industry sector has been confronted by the need to manage the big data being produced by various sources, which are well known for producing high volumes of heterogeneous data. High level of sophistication has been incorporated in almost all the industry, and healthcare is one of them. The article shows that the existence of huge amount of data in healthcare industry and the data generated in healthcare industry is neither homogeneous nor a simple type of data. Then the various sources and objectives of data are also highlighted and discussed. As data come from various sources, they must be versatile in nature in all aspects. So, rightly and meaningfully, big data analytics has penetrated the healthcare industry and its impact is also highlighted.

scholarly journals Big Data Analytics in Government: Improving Decision Making for R&D Investment in Korean SMEs

2019 ◽  
Vol 12 (1) ◽  
pp. 202
Author(s):  
Eun Sun Kim ◽  
Yunjeong Choi ◽  
Jeongeun Byun
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Driven ◽  
Medium Size ◽  
The Government ◽  
Using Data ◽  
High Level ◽  
D Investment

To expand the field of governmental applications of Big Data analytics, this study presents a case of data-driven decision-making using information on research and development (R&D) projects in Korea. The Korean government has continuously expanded the proportion of its R&D investment in small and medium-size enterprises to improve the commercialization performance of national R&D projects. However, the government has struggled with the so-called “Korea R&D Paradox”, which refers to how performance has lagged despite the high level of investment in R&D. Using data from 48,309 national R&D projects carried out by enterprises from 2013 to 2017, we perform a cluster analysis and decision tree analysis to derive the determinants of their commercialization performance. This study provides government entities with insights into how they might adjust their approach to Big Data analytics to improve the efficiency of R&D investment in small- and medium-sized enterprises.

scholarly journals A Comparison of Big Data Frameworks on a Layered Dataflow Model

2017 ◽  
Vol 27 (01) ◽  
pp. 1740003 ◽  
Author(s):  
Claudia Misale ◽  
Maurizio Drocco ◽  
Marco Aldinucci ◽  
Guy Tremblay
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Levels Of Abstraction ◽  
Level Data ◽  
Dataflow Model ◽  
Proposed Model ◽  
High Level ◽  
Execution Models ◽  
Different Levels

In the world of Big Data analytics, there is a series of tools aiming at simplifying programming applications to be executed on clusters. Although each tool claims to provide better programming, data and execution models–for which only informal (and often confusing) semantics is generally provided–all share a common underlying model, namely, the Dataflow model. The model we propose shows how various tools share the same expressiveness at different levels of abstraction. The contribution of this work is twofold: first, we show that the proposed model is (at least) as general as existing batch and streaming frameworks (e.g., Spark, Flink, Storm), thus making it easier to understand high-level data-processing applications written in such frameworks. Second, we provide a layered model that can represent tools and applications following the Dataflow paradigm and we show how the analyzed tools fit in each level.

scholarly journals Deduction of Efficient Scanning Machines using Big Data Technology

2019 ◽  
Vol 8 (4) ◽  
pp. 3770-3776
Keyword(s):  
Big Data ◽  
Cost Effectiveness ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Industry ◽  
X Rays ◽  
Huge Amount ◽  
Brain Scans ◽  
Medical Reports ◽  
High Level

Nowadays, the advancement in the field of information technology has witnessed stupendous growth in various industries, especially the medical imaging technologies in the healthcare industry. However, these advancements in the different technologies have not only made the data bigger but also a bit difficult to process and handle it. Though, these advancements may have resulted in huge amount of unnecessary data, it still cannot be considered as a major problem in today’s world as nowadays, the various advancements in technologies such as Big Data Analytics, Cloud Computing and several others, have made it really easy and effortless for storing huge amount of datasets and handling them. One of the boon that the advancement in technology has given to the world in the field of healthcare industry is the evolution of the scanning machines which can be used for the diagnosis of different diseases and to assemble the conclusions in the form of various medical reports for different scans such as ECG (Electrocardiogram), MRI(Magnetic Resonance Imaging) Brain scans, Ultrasounds, X-Rays, CT-Scanners and much more. But, the interesting part here is that though these scanning machines have their own advantages, one of the main disadvantages of them is that the efficiency of the results produced by them are yet to be known when comparing their performance’s to justify their enormous costs. Therefore, in the paper, the key challenges and various methodologies are being investigated in the healthcare industry with prime focus on comparing the scanning machines such as ECG, MRI, and Ultrasoundetc. by using Big Data Analytics. The various manufacturers of the scanning devices which are used by the hospitals or diagnostic centers have already fixed their price to such a high level that, even the hospitals have to spend lots of money to buy those machines and install them. Therefore, as a management side it becomes difficult to cope up with the performance related cost effectiveness of machines, which even shatters the trust of patients related to technical issues with a particular hospital. The prime aim is to focus on the precise implementation, performance efficiency and cost effectiveness of all the medical scans. The idea can also be implemented in improving theperformance along with the cost effectiveness of machines and devices other than the medical industry as well.

An Article on Big Data Analytics in Healthcare Applications and Challenges

2022 ◽  
pp. 1450-1457
Author(s):  
Jaimin Navinchandra Undavia ◽  
Atul Manubhai Patel
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Heterogeneous Data ◽  
Healthcare Industry ◽  
Technological Advancement ◽  
Huge Amount ◽  
Healthcare Applications ◽  
High Level ◽  
Almost All

The technological advancement has also opened up various ways to collect data through automatic mechanisms. One installed mechanism collects a huge amount of data without any further maintenance or human interventions. The health industry has been confronted by the need to manage the big data being produced by various sources, which are well known for producing high volumes of heterogeneous data. A high level of sophistication has been incorporated in almost all the industry, and healthcare is also one of them. The article explores the existence of a huge amount of data in the healthcare industry, and the data generated in the healthcare industry is neither homogeneous nor simple. Then the various sources and objectives of data are highlighted and discussed. As data come from various sources, they must be versatile in nature in all aspects. So, rightly and meaningfully, big data analytics has penetrated the healthcare industry, and its impact is highlighted.

scholarly journals TORNADO: Intermediate Results Orchestration Based Service-Oriented Data Curation Framework for Intelligent Video Big Data Analytics in the Cloud

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3581
Author(s):  
Aftab Alam ◽  
Young-Koo Lee
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Content Management ◽  
General Purpose ◽  
Video Analytics ◽  
Big Data Technologies ◽  
Service Oriented ◽  
Intermediate Results ◽  
High Level

In the recent past, the number of surveillance cameras placed in the public has increased significantly, and an enormous amount of visual data is produced at an alarming rate. Resultantly, there is a demand for a distributed system for video analytics. However, a majority of existing research on video analytics focuses on improving video content management and rely on a traditional client/server framework. In this paper, we develop a scalable and flexible framework called TORNADO on top of general-purpose big data technologies for intelligent video big data analytics in the cloud. The proposed framework acquires video streams from device-independent data-sources utilizing distributed streams and file management systems. High-level abstractions are provided to allow the researcher to develop and deploy video analytics algorithms and services in the cloud under the as-a-service paradigm. Furthermore, a unified IR Middleware has been proposed to orchestrate the intermediate results being generated during video big data analytics in the cloud. We report results demonstrating the performance of the proposed framework and the viability of its usage in terms of better scalability, less fault-tolerance, and better performance.

A High-Level Interactive Query Language for Big Data Analytics Based on a Functional Model

2020 ◽  
Vol 1 (1) ◽  
pp. 22-37
Author(s):  
Symphorien Monsia ◽  
Sami Faiz
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Query Language ◽  
Functional Model ◽  
Big Data Analytics ◽  
High Rate ◽  
Interactive Query ◽  
Data Querying ◽  
High Level ◽  
The Impact

Information technologies such as the internet, and social networks, produce vast amounts of data exponentially (known as Big Data) and use conventional information systems. Big Data is characterized by volume, a high rate of generation, and variety. Systems integration and data querying systems must be adapted to cope with the emergence of Big Data. The authors' interest is with the impact Big Data has on the decision-making environment, most particularly, the data querying phase. Their contribution is the development of a parallel and distributed platform, named high level query language for big data analytics (HLQL-BDA), created to query vast amounts of data in a computer cluster based on the MapReduce paradigm. The query language in HLQL-BDA is implemented by means of interactive query language based on a functional model. The researchers' experiment shows the scalability of HLQL-BDA when they increase the number of nodes and the size of data.

scholarly journals An Article on Big Data Analytics in Healthcare Applications and Challenges

2020 ◽  
Vol 5 (2) ◽  
pp. 58-64
Author(s):  
Jaimin Navinchandra Undavia ◽  
Atul Manubhai Patel
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Heterogeneous Data ◽  
Healthcare Industry ◽  
Technological Advancement ◽  
Huge Amount ◽  
Healthcare Applications ◽  
High Level ◽  
Almost All

The technological advancement has also opened up various ways to collect data through automatic mechanisms. One installed mechanism collects a huge amount of data without any further maintenance or human interventions. The health industry has been confronted by the need to manage the big data being produced by various sources, which are well known for producing high volumes of heterogeneous data. A high level of sophistication has been incorporated in almost all the industry, and healthcare is also one of them. The article explores the existence of a huge amount of data in the healthcare industry, and the data generated in the healthcare industry is neither homogeneous nor simple. Then the various sources and objectives of data are highlighted and discussed. As data come from various sources, they must be versatile in nature in all aspects. So, rightly and meaningfully, big data analytics has penetrated the healthcare industry, and its impact is highlighted.

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