scholarly journals On a Certain Research Gap in Big Data Mining for Customer Insights

2021 ◽  
Vol 11 (15) ◽  
pp. 6993
Author(s):  
Maria Mach-Król ◽  
Bartłomiej Hadasik
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Big Data Analytics ◽  
Customer Behavior ◽  
Temporal Dimension ◽  
Big Data Mining ◽  
Analysis And Synthesis ◽  
Research Gap ◽  
Key Phrases

The main purpose of this paper is to provide a theoretically grounded discussion on big data mining for customer insights, as well as to identify and describe a research gap due to the shortcomings in the use of the temporal approach in big data analyzes in scientific literature sources. This article adopts two research methods. The first method is the systematic search in bibliographic repositories aimed at identifying the concepts of big data mining for customer insights. This method has been conducted in four steps: search, selection, analysis, and synthesis. The second research method is the bibliographic verification of the obtained results. The verification consisted of querying the Scopus database with previously identified key phrases and then performing trend analysis on the revealed Scopus results. The main contributions of this study are: (1) to organize knowledge on the role of advanced big data analytics (BDA), mainly big data mining in understanding customer behavior; (2) to indicate the importance of the temporal dimension of customer behavior; and (3) to identify an interesting research gap: mining of temporal big data for a complete picture of customers.

Boosting innovation performance through big data analytics:An empirical investigationon the role of firm agility

2021 ◽  
pp. 016555152110474
Author(s):  
Ahad ZareRavasan
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Current Knowledge ◽  
Big Data Analytics ◽  
Innovation Performance ◽  
Data Driven ◽  
Moderating Variables ◽  
Research Gap ◽  
Moderating Role

While past studies proposed the role of big data analytics (BDA) as one of the primary pathways to business value creation, current knowledge on the link between BDA and innovation performance remains limited. In this regard, this study intends to fill this research gap by developing a theoretical framework for understanding how and under which mechanisms BDA influences innovation performance. Firm agility (conceptualised as sensing agility, decision-making agility and acting agility) is used in this research as the mediator between BDA and innovation performance. Besides, this research conceptualises two moderating variables: data-driven culture and BDA team sophistication. This study employs partial least squares (PLS) to test and validate the proposed hypotheses using survey data of 185 firms. The results show that firm agility significantly mediates the link between BDA use and innovation performance. Besides, the results suggest that data-driven culture moderates the relation between sensing agility and decision-making agility. This research also supports the moderating role of BDA team sophistication on the link between BDA use and sensing agility.

Role of Big Data in Education

2022 ◽  
pp. 22-37
Author(s):  
Simin Ghavifekr ◽  
Seng Yue Wong
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Decision Making ◽  
Big Data ◽  
Information Management ◽  
Higher Education Institutions ◽  
Big Data Analytics ◽  
Time Performance ◽  
Teaching Support

Big data has the variety of characteristics, such as real-time performance, timeliness short, and data mining analysis of large value generated. Application of big data in education can be reviewed in various aspects such as 1) providing students with appropriate teaching, 2) providing teaching support to teachers, and 3) providing information management for the administrations. This chapter can serve as a guide for the management of higher education institutions to recognize possible challenges in big data analytics and better prepare for them in future decision making.

Role of Big Data Analytics in Cellular Network Design

2015 ◽  
pp. 110-116
Author(s):  
Pushpa Mannava
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Network Design ◽  
Cellular Network ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Innovative Methods ◽  
Modern Technologies ◽  
Cellular Network Design

Big Data is an arising idea that describes innovative methods and also modern technologies to assess big volume of complicated datasets that are greatly created from numerous sources and with numerous prices. Data mining strategies are offering terrific aid in the location of Big Data analytics, considering that handling Big Data allow challenges for the applications. Big Data analytics is the capability of removing helpful information from such significant datasets. This paper provides an overview of big data analytics.

scholarly journals Big Data Mining: Tools & Algorithms

2016 ◽  
Vol 4 (1) ◽  
pp. 36 ◽  
Author(s):  
Adeel Shiraz Hashmi ◽  
Tanvir Ahmad
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Distributed Algorithms ◽  
Data Streams ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Large Datasets ◽  
Complex Data ◽  
Big Data Mining ◽  
Mining Tools

We are now in Big Data era, and there is a growing demand for tools which can process and analyze it. Big data analytics deals with extracting valuable information from that complex data which can’t be handled by traditional data mining tools. This paper surveys the available tools which can handle large volumes of data as well as evolving data streams. The data mining tools and algorithms which can handle big data have also been summarized, and one of the tools has been used for mining of large datasets using distributed algorithms.

scholarly journals DBMD Implementation for Big Data Mining in the CEASE Method

2021 ◽  
Vol 9 (3) ◽  
Author(s):  
Krishnaveni N ◽  
Ishwariya A ◽  
Priyanka R
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Virtual Machines ◽  
Scale Up ◽  
Big Data Analytics ◽  
Weighted Average ◽  
Matrix Decomposition ◽  
Matrix Composition ◽  
Big Data Mining ◽  
Accelerated Gradient

The fundamental tools to discover knowledge from big data was matrix composition. Here data generated by modern applications via cloud computing. However, it is still inefficient or infeasible to process very big data using such a method in a single machine or through virtual machines. Moreover, big data are often distributedly collected data from various data centers and stored on different machines via scheduling algorithms. Thus, such data generally bear strong heterogeneous noise. It is essential and useful to develop distributed matrix decomposition for big data analytics. Such a method should scale up well, model the heterogeneous noise, and address the communication issue in a distributed system. To this end, we propose a Distributed Bayesian Matrix Decomposition model (DBMD) for big data mining and clustering. Specifically, we adopt three strategies to implement the distributed computing including (1) the accelerated gradient descent, (2) the alternating direction method of multipliers (ADMM), and (3) the statistical inference. We investigate the theoretical convergence behaviors of these algorithms. To address the heterogeneity of the noise, we propose an optimal plug-in weighted average that reduces the variance of the estimation. Finally, comparison made between these algorithms to understand the result between them.

Big Data Mining and Knowledge Discovery

2016 ◽  
Vol 9 ◽  
pp. 5 ◽  
Author(s):  
Tomas Ruzgas ◽  
Kristina Jakubėlienė ◽  
Aistė Buivytė
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Analysis ◽  
Knowledge Discovery ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Big Data Analysis ◽  
Big Data Mining

The article dealt with exploration methods and tools for big data. It identifies the challenges encountered in the analysis of big data. Defined notion of big data. describe the technology for big data analysis. Article provides an overview of tools which are designed for big data analytics.

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.

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