scholarly journals Text Mining in Big Data Analytics

2020 ◽  
Vol 4 (1) ◽  
pp. 1 ◽  
Author(s):  
Hossein Hassani ◽  
Christina Beneki ◽  
Stephan Unger ◽  
Maedeh Taj Mazinani ◽  
Mohammad Reza Yeganegi
Keyword(s):  
Big Data ◽  
Text Mining ◽  
Data Analytics ◽  
Big Data Analytics ◽  
The State ◽  
Journal Articles ◽  
Academic Journal ◽  
Social Media Platforms ◽  
Meeting Transcripts ◽  
Significant Patterns

Text mining in big data analytics is emerging as a powerful tool for harnessing the power of unstructured textual data by analyzing it to extract new knowledge and to identify significant patterns and correlations hidden in the data. This study seeks to determine the state of text mining research by examining the developments within published literature over past years and provide valuable insights for practitioners and researchers on the predominant trends, methods, and applications of text mining research. In accordance with this, more than 200 academic journal articles on the subject are included and discussed in this review; the state-of-the-art text mining approaches and techniques used for analyzing transcripts and speeches, meeting transcripts, and academic journal articles, as well as websites, emails, blogs, and social media platforms, across a broad range of application areas are also investigated. Additionally, the benefits and challenges related to text mining are also briefly outlined.

scholarly journals Hidden big data analytics issues in the healthcare industry

2019 ◽  
Vol 26 (2) ◽  
pp. 981-998 ◽  
Author(s):  
Kenneth David Strang ◽  
Zhaohao Sun
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Analytics ◽  
Research Method ◽  
Big Data Analytics ◽  
Big Data Analysis ◽  
Healthcare Industry ◽  
Statistical Control ◽  
Subject Matter Experts ◽  
Journal Articles

The goal of the study was to identify big data analysis issues that can impact empirical research in the healthcare industry. To accomplish that the author analyzed big data related keywords from a literature review of peer reviewed journal articles published since 2011. Topics, methods and techniques were summarized along with strengths and weaknesses. A panel of subject matter experts was interviewed to validate the intermediate results and synthesize the key problems that would likely impact researchers conducting quantitative big data analysis in healthcare studies. The systems thinking action research method was applied to identify and describe the hidden issues. The findings were similar to the extant literature but three hidden fatal issues were detected. Methodical and statistical control solutions were proposed to overcome the three fatal healthcare big data analysis issues.

scholarly journals Text Mining and Big Data Analytics for Retrospective Analysis of Clinical Texts from Outpatient Care

2015 ◽  
Vol 15 (4) ◽  
pp. 58-77 ◽  
Author(s):  
Svetla Boytcheva ◽  
Galia Angelova ◽  
Zhivko Angelov ◽  
Dimitar Tcharaktchiev
Keyword(s):  
Blood Pressure ◽  
Big Data ◽  
Text Mining ◽  
Retrospective Analysis ◽  
Outpatient Care ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Free Text ◽  
Arterial Blood ◽  
And Control

Abstract This paper presents the results of an on-going research project for knowledge extraction from large corpora of clinical narratives in Bulgarian language, approximately 100 million of outpatient care notes. Entities with numerical values are mined in the free text and the extracted information is stored in a structured format. The Algorithms for retrospective analyses and big data analytics are applied for studying the treatment and evaluating the diabetes compensation and control of arterial blood pressure.

scholarly journals On relating big data analytics to supply chain planning: towards a research agenda

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jinou Xu ◽  
Margherita Emma Paola Pero ◽  
Federica Ciccullo ◽  
Andrea Sianesi
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Journal Articles ◽  
Supply Chain Planning ◽  
Research Directions ◽  
Content Type ◽  
Body Of Knowledge ◽  
Management Issues

PurposeThis paper aims to examine how the extant publication has related big data analytics (BDA) to supply chain planning (SCP). The paper presents a conceptual model based on the reviewed articles and the dominant research gaps and outlines the research directions for future advancement.Design/methodology/approachBased on a systematic literature review, this study analysed 72 journal articles and reported the descriptive and thematic analysis in assessing the established body of knowledge.FindingsThis study reveals the fact that literature on relating BDA to SCP has an ambiguous use of BDA-related terminologies and a siloed view on SCP processes that primarily focuses on the short-term. Looking at the big data sources, the objective of adopting BDA and changes to SCP, we identified three roles of big data and BDA for SCP: supportive facilitator, source of empowerment and game-changer. It bridges the conversation between BDA technology for SCP and its management issues in organisations and supply chains according to the technology-organisation-environmental framework.Research limitations/implicationsThis paper presents a comprehensive examination of existing literature on relating BDA to SCP. The resulted themes and research opportunities will help to advance the understanding of how BDA will reshape the future of SCP and how to manage BDA adoption towards a big data-driven SCP.Originality/valueThis study is unique in its discussion on how BDA will reshape SCP integrating the technical and managerial perspectives, which have not been discussed to date.

scholarly journals Key competencies for big data analytics professions: a multimethod study

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ajax Persaud
Keyword(s):  
Big Data ◽  
Text Mining ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Educational Institutions ◽  
Market Demand ◽  
Skilled Workers ◽  
Content Type ◽  
Multimethod Approach ◽  
Skills Shortages

PurposeThis study aims to identify the precise competencies that employers are seeking for big data analytics professions and whether higher education big data programs enable students to acquire the competencies.Design/methodology/approachThis study utilizes a multimethod approach involving three data sources: online job postings, executive interviews and big data programs at universities and colleges. Text mining analysis guided by a holistic competency theoretical framework was used to derive insights into the required competencies.FindingsWe found that employers are seeking workers with strong functional and cognitive competencies in data analytics, computing and business combined with a range of social competencies and specific personality traits. The exact combination of competencies required varies with job levels and tasks. Executives clearly indicate that workers rarely possess the competencies and they have to provide additional training.Research limitations/implicationsA limitation is our inability to capture workers' perspectives to determine the extent to which they think they have the necessary competencies.Practical implicationsThe findings can be used by higher educational institutions to design programs to better meet market demand. Job seekers can use it to focus on the types of competencies they need to advance their careers. Policymakers can use it to focus policies and investments to alleviate skills shortages. Industry and universities can use it to strengthen their collaborations.Social implicationsMuch closer collaborations among public institutions, educational institutions, industry, and community organizations are needed to ensure training programs evolve with the evolving need for skills driven by dynamic technological changes.Originality/valueThis is the first study on this topic to adopt a multimethod approach incorporating the perspectives of the key stakeholders in the supply and demand of skilled workers. It is the first to employ text mining analysis guided by a holistic competency framework to derive unique insights.

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