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Big data reduction framework for value creation in sustainable enterprises
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“The extant literature highlights a number of analytical processes and methodssuch as text analytics, audio analytics, video analytics, social media analytics, predictive analysis of data (Gandomi & Haider, 2015) and others reported of descriptive analytics, inquisitive analytics, prescriptive analytics and pre-emptive data analytics (Assunção et al., 2015;Rehman, Chang, Batool, & Teh, 2016).”
From Critical analysis of Big Data challenges and analytical methods (2017) · cites this paper as Rehman, Chang, Batool, & Teh, 2016
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“This analytics is concerned with forecasting and statistical modelling to determine the future possibilities based on supervised, unsupervised, and semi-supervised learning models (Joseph & Johnson, 2013;Rehman et al., 2016;Waller & Fawcett, 2013).”
From Critical analysis of Big Data challenges and analytical methods (2017) · cites this paper as Rehman et al., 2016
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“Descriptive analytics are the simplest form of BDA method, and involves the summarization and description of knowledge patterns using simple statistical methods, such as mean, median, mode, standard deviation, variance, and frequency measurement of specific events in BD streams (Rehman et al., 2016).”
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“Although difficult to deploy, prescriptive analytics contribute to handling the information shift and the continuous evolution of business process models (Rehman et al., 2016).”
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Metadata
- Authors
- Rehman, M. H. U., Chang, V., Batool, A., & Wah, T. Y.
- Affiliations
- University of Malaya (MY)
- Xi’an Jiaotong-Liverpool University (CN)
- Iqra University (PK)
- Corresponding author
- Rehman, Muhammad Habib ur
- Wah, Teh Ying
- Venue
- International Journal of Information Management
- Publisher
- Elsevier BV
- Published
- 2016-12
- Volume
- 36
- Issue
- 6
- Pages
- 917-928
- ISSN
- 0268-4012 (ISSN-L)
- OpenAlex
- W2407961458
- Cited by
- 279
- References
- 75
- Citations by year
- 2026: 6 · 2025: 15 · 2024: 18 · 2023: 30 · 2022: 39 · 2021: 34 · 2020: 43 · 2019: 35 · 2018: 38 · 2017: 16 · 2016: 5
- FWCI
- 56.24
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- International Journal of Information Management (publishedVersion)
- ePrints Soton (University of Southampton) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- University of Malaya Research (VOTE RP028C-14AET)
- Bright Spark Unit of University of Malaya (BSP/APP/1634/2013)
- Topics
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Physical Sciences › Computer Science › Information Systems › Big Data and Digital Economy
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Keywords
- value creation, sustainable enterprises, cloud service costs, consumer trust, consumer privacy, secure data sharing, business model canvas, technology adoption barriers, enterprise data management
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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