scholarly journals An Experimental Analysis Of The Applications Of Datamiing Methods On Bigdata

2019 ◽  
Vol 2 (3) ◽  
Author(s):  
Chittoju Naga Santhosh Kumar ◽  
K S Reddy
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Design Of Experiment ◽  
Current Data ◽  
Work Processes ◽  
Valuable Data ◽  
Assessment Technique ◽  
Huge Data ◽  
Mining Methods ◽  
Data Investigation

Data mining is a procedure of separating covered up, obscure, however possibly valuable data from gigantic data. Huge Data impactsly affects logical disclosures and worth creation. Data mining (DM) with Big Data has been broadly utilized in the lifecycle of electronic items that range from the structure and generation stages to the administration organize. A far reaching examination of DM with Big Data and a survey of its application in the phases of its lifecycle won't just profit scientists to create solid research. As of late huge data have turned into a trendy expression, which constrained the analysts to extend the current data mining methods to adapt to the advanced idea of data and to grow new scientific procedures. In this paper, we build up an exact assessment technique dependent on the standard of Design of Experiment. We apply this technique to assess data mining instruments and AI calculations towards structure huge data examination for media transmission checking data. Two contextual investigations are directed to give bits of knowledge of relations between the necessities of data examination and the decision of an instrument or calculation with regards to data investigation work processes. 

scholarly journals Enterprise Commodity Marketing Innovation Based on Big Data Mining Technology

CONVERTER ◽  
2021 ◽  
pp. 613-619
Author(s):  
Feng Jun
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Association Rule ◽  
Current Data ◽  
Marketing Channels ◽  
Marketing Strategies ◽  
Mining Technology ◽  
Valuable Data ◽  
Marketing Innovation ◽  
Big Data Mining

The advent of the era of big data has brought many opportunities and challenges to the marketing of enterprises. Enterprises should develop marketing channels according to the requirements of the market. At the same time, enterprises further mine valuable data information, so as to improve customer satisfaction for enterprise products. This paper analyzes the opportunities and challenges brought by the era of big data to the marketing market of enterprises, and explores how to innovate the marketing strategies of enterprises. This paper describes the background of the current data mining and the main data mining technology in this field. Then, it focuses on the association rule algorithm which is widely used in knowledge data mining technology and its application in marketing strategy.

Using Big Data to Improve the Educational Infrastructure and Learning Paradigm

2016 ◽  
pp. 158-181 ◽  
Author(s):  
Areej Fatemah Meghji ◽  
Naeem A. Mahoto
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Student Behavior ◽  
Learning Experience ◽  
Educational Data Mining ◽  
Generic Model ◽  
Gold Mine ◽  
Information Retention ◽  
Pedagogical Decisions ◽  
Mining Methods

In higher education, the demand for improved information in relation to educational and learning outcomes is greater than ever before. Leveraging technology, new models of education have emerged that are not only improving modes of lecture delivery and information retention, but also generating huge amounts of data. This data is potentially a gold mine that needs to be explored to uncover patterns associated with student behavior and how information is processed, retained and used by the students. This chapter proposes a generic model that uses the techniques of educational data mining to explore and analyze Big Data being generated by the education sector. This chapter also examines the various questions that can be answered using educational data mining methods and how the discovered patterns can be used to enrich the learning experience of a student as well as help teachers make pedagogical decisions.

Big Data and Service Science

2016 ◽  
pp. 180-196
Author(s):  
Tu-Bao Ho ◽  
Siriwon Taewijit ◽  
Quang-Bach Ho ◽  
Hieu-Chi Dam
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Service Science ◽  
Customer Relationship ◽  
Service Value ◽  
Mining Methods ◽  
The Relationship

Big data is about handling huge and/or complex datasets that conventional technologies cannot handle or handle well. Big data is currently receiving tremendous attention from both industry and academia as there is much more data around us than ever before. This chapter addresses the relationship between big data and service science, especially how big data can contribute to the process of co-creation of service value. In particular, the value co-creation in terms of customer relationship management is mentioned. The chapter starts with brief descriptions of big data, machine learning and data mining methods, service science and its model of value co-creation, and then addresses the key idea of how big data can contribute to co-create service value.

scholarly journals Implementasi Bussiness Intelligent dalam e-Tourism Berbasis Big Data

2019 ◽  
Vol 3 (2) ◽  
pp. 163
Author(s):  
Chandra Eko wahyudi Utomo
Keyword(s):  
Big Data ◽  
Business Intelligence ◽  
High Growth ◽  
Tourism Industry ◽  
The Internet ◽  
Company Performance ◽  
Work Processes ◽  
Intelligence System ◽  
Huge Data ◽  
Data Explosion

Abstract The use of information technology that is integrated with work processes in an organization has become an absolute necessity. The availability of complete, correct and accurate data and information has become a basic requirement for the survival of an organization. Business Intelligence (BI) is a form of implementation that is able to answer the above needs. BI has been widely used by organizations in managing data and information to support decision making. BI is usually associated with efforts to maximize the performance of an organization. Business Intelligence System is a term that is generally used for the type of application or technology used to assist BI activities, such as collecting data, providing access, and analyzing data and information about company performance. Along with the rapid online-based information systems including e-tourism, creating a huge data explosion on the internet (bigdata). The very high growth of tourism data on the internet can be utilized for the needs of the tourism industry and research needs in the field of tourism. Keywords: intelligent business, e-tourism, big data

scholarly journals A review on performance analysis of data mining methods in IoT

2020 ◽  
Vol 7 (73) ◽  
pp. 193-200
Author(s):  
Ashutosh Kumar Dubey ◽  
Dimple Kapoor ◽  
Vijaita Kashyap
Keyword(s):  
Systematic Review ◽  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Performance Analysis ◽  
System Component ◽  
Different Types ◽  
Communication Capability ◽  
Mining Methods ◽  
And Performance

IoT is capable and helpful in interdisciplinary areas along with the convergence of multiple technologies and platforms. This study adheres the adaptation of data mining technologies along with big data and cloud computing with the IoT system with detailed discussion. This paper supports and provide systematic review and analysis based on the computational parameters and performance analysis. The analysis and discussion are based on the communication capability, system component communication, aspects of data mining, big data and cloud computing in IoT. Different types of transmission and communication barriers have also been discussed and analyze. Finally, based on the study and analysis a framework has been suggested for the smooth functioning of the IoT protocols.

Big Data Analysis and Implementation in Different Areas Using IoT

2017 ◽  
Vol 1 (2) ◽  
pp. 12-25
Author(s):  
Aqeel ur Rehman ◽  
Muhammad Fahad ◽  
Rafi Ullah ◽  
Faisal Abdullah
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Healthcare Applications ◽  
New Techniques ◽  
Standard Data ◽  
Huge Data ◽  
Data Rates ◽  
Definition Of

This article describes how in IoT, data management is a major issue because of communication among billions of electronic devices, which generate the huge dataset. Due to the unavailability of any standard, data analysis on such a large amount of data is a complex task. There should be a definition of IoT-based data to find out what is available and its applicable solutions. Such a study also directs the need for new techniques to cope up with such challenges. Due to the heterogeneity of connected nodes, different data rates, and formats, it is a huge challenge to deal with such a variety of data. As IoT is providing processing nodes in the form of smart nodes; it is presenting a good platform to support the big data study. In this article, the characteristics of data mining requirements for data mining analysis are highlighted. The associated challenges of facts generation, as well as the plausible suitable platform of such huge data analysis is also underlined. The application of IoT to support big data analysis in healthcare applications is also presented.

Data Mining for the Internet of Things: Literature Review and Challenges

2021 ◽  
Vol 9 (VII) ◽  
pp. 3351-3362
Author(s):  
Dr. Mohd Zuber
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Internet Of Things ◽  
The Internet ◽  
Mining System ◽  
Research Issues ◽  
Data Mining Algorithms ◽  
Huge Data ◽  
Mining Algorithms ◽  
The Internet Of Things

The huge data generate by the Internet of Things (IOT) are measured of high business worth, and data mining algorithms can be applied to IOT to take out hidden information from data. In this paper, we give a methodical way to review data mining in knowledge, technique and application view, together with classification, clustering, association analysis and time series analysis, outlier analysis. And the latest application luggage is also surveyed. As more and more devices connected to IOT, huge volume of data should be analyzed, the latest algorithms should be customized to apply to big data. We reviewed these algorithms and discussed challenges and open research issues. At last a suggested big data mining system is proposed.

Implementation of Mining Techniques to Enhance Discovery in Service-Oriented Computing

2014 ◽  
pp. 249-271
Author(s):  
Chellammal Surianarayanan ◽  
Gopinath Ganapathy
Keyword(s):  
Data Mining ◽  
Service Discovery ◽  
Service Usage ◽  
Data Repositories ◽  
Huge Data ◽  
Usage Patterns ◽  
Service Oriented ◽  
Mining Methods ◽  
Using Data ◽  
Semantic Service

Web services have become the de facto platform for developing enterprise applications using existing interoperable and reusable services that are accessible over networks. Development of any service-based application involves the process of discovering and combining one or more required services (i.e. service discovery) from the available services, which are quite large in number. With the availability of several services, manually discovering required services becomes impractical and time consuming. In applications having composition or dynamic needs, manual discovery even prohibits the usage of services itself. Therefore, effective techniques which extract relevant services from huge service repositories in relatively short intervals of time are crucial. Discovery of service usage patterns and associations/relationships among atomic services would facilitate efficient service composition. Further, with availability of several services, it is more likely to find many matched services for a given query, and hence, efficient methods are required to present the results in useful form to enable the client to choose the best one. Data mining provides well known exploratory techniques to extract relevant and useful information from huge data repositories. In this chapter, an overview of various issues of service discovery and composition and how they can be resolved using data mining methods are presented. Various research works that employ data mining methods for discovery and composition are reviewed and classified. A case study is presented that serves as a proof of concept for how data mining techniques can enhance semantic service discovery.

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