Study on the Optimization of Data Mining in Big Data

2014 ◽  
Vol 989-994 ◽  
pp. 1837-1840 ◽  
Author(s):  
Gang Xin ◽  
Hui Yan
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Real Time ◽  
Clustering Analysis ◽  
Cloud Storage ◽  
Real Time Monitoring ◽  
Data Mining Techniques

This paper proposes an analysis measure for Big Data by optimizing traditional data mining, base on Weka data analyzing platform ,K-means algorithm is employed through the interface programs in Weka system, so that optimized data mining techniques can be applied in cloud storage, cloud computing of Big Data by clustering analysis base on Big Data pre-processing and real-time monitoring of memory.

Summary of Data Reduction Based on Cloud Environment

2014 ◽  
Vol 1079-1080 ◽  
pp. 779-781
Author(s):  
Shu Li Huang
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Data Reduction ◽  
Attribute Reduction ◽  
Cloud Environment ◽  
Mining Technology ◽  
Data Mining Techniques ◽  
Using Data

In today's era of big data, how to quickly find the data they need is a difficult thing from the mass of information, in order to achieve this goal, cloud computing to data mining technology provides a new direction, this article on how cloud environment attribute Reduction using data mining techniques are described.

scholarly journals INTELLIGENT DEVELOPMENT OF BIG DATA ANALYTICS FOR MANUFACTURING INDUSTRY IN CLOUD COMPUTING

2019 ◽  
Vol 01 (01) ◽  
pp. 13-22 ◽  
Author(s):  
Abul Bashar
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Real Time ◽  
Data Analytics ◽  
Smart Home ◽  
Industrial Development ◽  
Manufacturing Industry ◽  
Big Data Analytics ◽  
City Development ◽  
Real Time Monitoring

Big-data analytics being a useful technique in the analyzing the deeper values hidden inside a huge set of data flow that are generated in our day today lives, has almost become more prominent in variety of applications such as industrial development, smart home to smart city development and security management etc., despite its high potentials the challenges incurred makes it insufficient with certain applications that include a real time monitoring, so the paper proposes the real time monitoring of the developing manufacturing industry by proffering the intelligent big data analytics and cloud computing to present with the maximum possible insights to improvise the process of the manufacturing , by retaining the product consistency, optimal throughput and increasing the productivity.

Challenges and Cloud Computing Environments Towards Big Data

2014 ◽  
pp. 203-208
Author(s):  
Kiran Kumar S V N Madupu
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Technology Development ◽  
Computing Environments ◽  
Modern Technologies

Big Data has terrific influence on scientific discoveries and also value development. This paper presents approaches in data mining and modern technologies in Big Data. Difficulties of data mining as well as data mining with big data are discussed. Some technology development of data mining as well as data mining with big data are additionally presented.

Survey of Data Mining Techniques Used for Real Time Churn Prediction

2017 ◽  
Vol 7 (3) ◽  
pp. 219-222 ◽  
Author(s):  
Jean Claude Turiho ◽  
◽  
Wilson Cheruiyot ◽  
Anne Kibe ◽  
Irénée Mungwarakarama ◽  
...  
Keyword(s):  
Data Mining ◽  
Real Time ◽  
Churn Prediction ◽  
Data Mining Techniques

Trends and Opportunities in Health Analytics as a Service and Implications for Use in Low Resource Settings: A Literature Review Abstract (Preprint)

2019 ◽  
Author(s):  
Meghana Bastwadkar ◽  
Carolyn McGregor ◽  
S Balaji
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Intensive Care ◽  
Literature Review ◽  
Health Monitoring ◽  
Data Analytics ◽  
Neonatal Intensive Care ◽  
Big Data Analytics ◽  
Healthcare Facilities

BACKGROUND This paper presents a systematic literature review of existing remote health monitoring systems with special reference to neonatal intensive care (NICU). Articles on NICU clinical decision support systems (CDSSs) which used cloud computing and big data analytics were surveyed. OBJECTIVE The aim of this study is to review technologies used to provide NICU CDSS. The literature review highlights the gaps within frameworks providing HAaaS paradigm for big data analytics METHODS Literature searches were performed in Google Scholar, IEEE Digital Library, JMIR Medical Informatics, JMIR Human Factors and JMIR mHealth and only English articles published on and after 2015 were included. The overall search strategy was to retrieve articles that included terms that were related to “health analytics” and “as a service” or “internet of things” / ”IoT” and “neonatal intensive care unit” / ”NICU”. Title and abstracts were reviewed to assess relevance. RESULTS In total, 17 full papers met all criteria and were selected for full review. Results showed that in most cases bedside medical devices like pulse oximeters have been used as the sensor device. Results revealed a great diversity in data acquisition techniques used however in most cases the same physiological data (heart rate, respiratory rate, blood pressure, blood oxygen saturation) was acquired. Results obtained have shown that in most cases data analytics involved data mining classification techniques, fuzzy logic-NICU decision support systems (DSS) etc where as big data analytics involving Artemis cloud data analysis have used CRISP-TDM and STDM temporal data mining technique to support clinical research studies. In most scenarios both real-time and retrospective analytics have been performed. Results reveal that most of the research study has been performed within small and medium sized urban hospitals so there is wide scope for research within rural and remote hospitals with NICU set ups. Results have shown creating a HAaaS approach where data acquisition and data analytics are not tightly coupled remains an open research area. Reviewed articles have described architecture and base technologies for neonatal health monitoring with an IoT approach. CONCLUSIONS The current work supports implementation of the expanded Artemis cloud as a commercial offering to healthcare facilities in Canada and worldwide to provide cloud computing services to critical care. However, no work till date has been completed for low resource setting environment within healthcare facilities in India which results in scope for research. It is observed that all the big data analytics frameworks which have been reviewed in this study have tight coupling of components within the framework, so there is a need for a framework with functional decoupling of components.

scholarly journals Data Mining and Cloud Computing

2012 ◽  
Vol 4 (2) ◽  
Author(s):  
Robert Vrbić
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Data Warehouses ◽  
Cloud Data ◽  
Big Data Mining ◽  
New Knowledge ◽  
Flexible Infrastructure

Cloud computing provides a powerful, scalable and flexible infrastructure into which one can integrate, previously known, techniques and methods of Data Mining. The result of such integration should be strong and capacitive platform that will be able to deal with the increasing production of data, or that will create the conditions for the efficient mining of massive amounts of data from various data warehouses with the aim of creating (useful) information or the production of new knowledge. This paper discusses such technology - the technology of big data mining, known as Cloud Data Mining (CDM).

scholarly journals A Novel Dbmd Implementation for Big Data Mining and Clustering Via Cloud Computing

2021 ◽  
Vol 23 (06) ◽  
pp. 29-35
Author(s):  
A. Vaitheeswari ◽  
◽  
Dr. N. Krishnaveni ◽  
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Virtual Machines ◽  
Big Data Analysis ◽  
Correspondence Problem ◽  
Weighted Norm ◽  
Matrix Structure ◽  
Method Of Multipliers ◽  
Big Data Mining

Matrix structure was one of the most important devices for finding data from big data. Here you’ll find data produced by current applications using cloud computing. However, moving big data using such a system in a performance computer or through virtual machines is still inefficient or impossible. Furthermore, big data is often gathered data from a variety of data sources and stored on a variety of machines using scheduling algorithms. As a result, such data usually bear solid shifted commotion. Growing circulated matrix deterioration is necessary and beneficial for big data analysis. Such a plan should have a good chance of succeeding. Represent the diverse clamor and deal with the correspondence problem in a disseminated manner. In order to do this, we used a Bayesian matrix decay model (DBMD) for big data mining and grouping. Only three approaches to disseminated computation are considered: 1) accelerate slope drop, 2) alternating path method of multipliers (ADMM), and 3) observable derivation. We look at how these approaches could be mixed together in the future. To deal with the commotion’s heterogeneity, we suggest an ideal module weighted norm that reduces the assessment’s differentiation. Finally, a comparison was made between these approaches in order to understand the differences in their outcomes.

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