Big data stream analysis: a systematic literature review

PurposeThe proposed article is aimed at exploring the opportunities, challenges and possible outcomes of incorporating big data analytics (BDA) into health-care sector. The purpose of this study is to find the research gaps in the literature and to investigate the scope of incorporating new strategies in the health-care sector for increasing the efficiency of the system.Design/methodology/approachFora state-of-the-art literature review, a systematic literature review has been carried out to find out research gaps in the field of healthcare using big data (BD) applications. A detailed research methodology including material collection, descriptive analysis and categorization is utilized to carry out the literature review.FindingsBD analysis is rapidly being adopted in health-care sector for utilizing precious information available in terms of BD. However, it puts forth certain challenges that need to be focused upon. The article identifies and explains the challenges thoroughly.Research limitations/implicationsThe proposed study will provide useful guidance to the health-care sector professionals for managing health-care system. It will help academicians and physicians for evaluating, improving and benchmarking the health-care strategies through BDA in the health-care sector. One of the limitations of the study is that it is based on literature review and more in-depth studies may be carried out for the generalization of results.Originality/valueThere are certain effective tools available in the market today that are currently being used by both small and large businesses and corporations. One of them is BD, which may be very useful for health-care sector. A comprehensive literature review is carried out for research papers published between 1974 and 2021.

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Big data-driven strategic orientation in international marketing

International Marketing Review ◽

10.1108/imr-11-2020-0256 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Shahriar Akter ◽

Md Afnan Hossain ◽

Qiang (Steven) Lu ◽

S.M. Riad Shams

Keyword(s):

Big Data ◽

Literature Review ◽

Systematic Literature Review ◽

International Marketing ◽

Strategic Orientation ◽

Data Driven ◽

Future Research ◽

Content Type ◽

Strategic Orientations ◽

Marketing Activities

PurposeBig data is one of the most demanding topics in contemporary marketing research. Despite its importance, the big data-based strategic orientation in international marketing is yet to be formed conceptually. Thus, the purpose of this study is to systematically review and propose a holistic framework on big data-based strategic orientation for firms in international markets to attain a sustained firm performance.Design/methodology/approachThe study employed a systematic literature review to synthesize research rigorously. Initially, 2,242 articles were identified from the selective databases, and 45 papers were finally reported as most relevant to propose an integrative conceptual framework.FindingsThe findings of the systematic literature review revealed data-evolving, and data-driven strategic orientations are essential for performing international marketing activities that contain three primary orientations such as (1) international digital platform orientation, (2) international market orientation and (3) international innovation and entrepreneurial orientation. Eleven distinct sub-dimensions reflect these three primary orientations. These strategic orientations of international firms may lead to advanced analytics orientation to attain sustained firm performance by generating and capturing value from the marketplace.Research limitations/implicationsThe study minimizes the literature gap by forming knowledge on big data-based strategic orientation and framing a multidimensional framework for guiding managers in the context of strategic orientation for international business and international marketing activities. The current study was conducted by following only a systematic literature review exclusively in firms' overall big data-based strategic orientation concept in international marketing. Future research may extend the domain by introducing firms' category wise systematic literature review.Originality/valueThe study has proposed a holistic conceptual framework for big data-driven strategic orientation in international marketing literature through a systematic review for the first time. It has also illuminated a future research agenda that raises questions for the scholars to develop or extend theory in this area or other related disciplines.

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Improving Geospatial Big Data Analytics Approaches

Interdisciplinary Approaches to Spatial Optimization Issues - Advances in Geospatial Technologies ◽

10.4018/978-1-7998-1954-7.ch005 ◽

2021 ◽

pp. 82-90

Author(s):

Sana Rekik

Keyword(s):

Big Data ◽

Data Stream ◽

Web Search ◽

Big Data Analytics ◽

Future Events ◽

Data Stream Analysis ◽

Analysis System ◽

Changes Over Time ◽

Time Systems

The advent of geospatial big data has led to a paradigm shift where most related applications became data driven, and therefore intensive in both data and computation. This revolution has covered most domains, namely the real-time systems such as web search engines, social networks, and tracking systems. These later are linked to the high-velocity feature, which characterizes the dynamism, the fast changing and moving data streams. Therefore, the response time and speed of such queries, along with the space complexity, are among data stream analysis system requirements, which still require improvements using sophisticated algorithms. In this vein, this chapter discusses new approaches that can reduce the complexity and costs in time and space while improving the efficiency and quality of responses of geospatial big data stream analysis to efficiently detect changes over time, conclude, and predict future events.

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Big data and dynamic capabilities: a bibliometric analysis and systematic literature review

Management Decision ◽

10.1108/md-07-2018-0821 ◽

2019 ◽

Vol 57 (8) ◽

pp. 2052-2068 ◽

Cited By ~ 18

Author(s):

Riccardo Rialti ◽

Giacomo Marzi ◽

Cristiano Ciappei ◽

Donatella Busso

Keyword(s):

Big Data ◽

Literature Review ◽

Bibliometric Analysis ◽

Systematic Literature Review ◽

Dynamic Capabilities ◽

Process Management ◽

Big Data Analytics ◽

Management Decision ◽

Content Type ◽

Management Decision Making

Purpose Recently, several manuscripts about the effects of big data on organizations used dynamic capabilities as their main theoretical approach. However, these manuscripts still lack systematization. Consequently, the purpose of this paper is to systematize the literature on big data and dynamic capabilities. Design/methodology/approach A bibliometric analysis was performed on 170 manuscripts extracted from the Clarivate Analytics Web of Science Core Collection database. The bibliometric analysis was integrated with a literature review. Findings The bibliometric analysis revealed four clusters of papers on big data and dynamic capabilities: big data and supply chain management, knowledge management, decision making, business process management and big data analytics. The systematic literature review helped to clarify each clusters’ content. Originality/value To the authors’ best knowledge, minimal attention has been paid to systematizing the literature on big data and dynamic capabilities.

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A systematic literature review of data science, data analytics and machine learning applied to healthcare engineering systems

Management Decision ◽

10.1108/md-01-2020-0035 ◽

2020 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Roberto Salazar-Reyna ◽

Fernando Gonzalez-Aleu ◽

Edgar M.A. Granda-Gutierrez ◽

Jenny Diaz-Ramirez ◽

Jose Arturo Garza-Reyes ◽

...

Keyword(s):

Machine Learning ◽

Data Mining ◽

Big Data ◽

Literature Review ◽

Systematic Literature Review ◽

Data Analytics ◽

Research Area ◽

Engineering Systems ◽

Content Type ◽

Healthcare Engineering

PurposeThe objective of this paper is to assess and synthesize the published literature related to the application of data analytics, big data, data mining and machine learning to healthcare engineering systems.Design/methodology/approachA systematic literature review (SLR) was conducted to obtain the most relevant papers related to the research study from three different platforms: EBSCOhost, ProQuest and Scopus. The literature was assessed and synthesized, conducting analysis associated with the publications, authors and content.FindingsFrom the SLR, 576 publications were identified and analyzed. The research area seems to show the characteristics of a growing field with new research areas evolving and applications being explored. In addition, the main authors and collaboration groups publishing in this research area were identified throughout a social network analysis. This could lead new and current authors to identify researchers with common interests on the field.Research limitations/implicationsThe use of the SLR methodology does not guarantee that all relevant publications related to the research are covered and analyzed. However, the authors' previous knowledge and the nature of the publications were used to select different platforms.Originality/valueTo the best of the authors' knowledge, this paper represents the most comprehensive literature-based study on the fields of data analytics, big data, data mining and machine learning applied to healthcare engineering systems.

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