The DOI matches its Crossref record; 74 of the 75 references it cites were found in a registry or an index.
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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In the citing paper
“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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In the citing paper
“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).”
From Critical analysis of Big Data challenges and analytical methods (2017) · cites this paper as 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).”
From Critical analysis of Big Data challenges and analytical methods (2017) · cites this paper as Rehman et al., 2016
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1 retracted
Yaqoob, I., Chang, V. I. C., Gani, A., Mokhtar, S., Hashem, M., Ahmed, E., Anuar, N. B., & Khan, S. U. (2016) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2016.04.014 · Cited by 35
Self-citationRetractedDraper, N. R., & Smith, H. (2005) · Technometrics · doi:10.1198/tech.2005.s303 · Cited by 17,993
Data clustering: 50 years beyond K-means
Jain, A. K. (2009) · Pattern Recognition Letters · doi:10.1016/j.patrec.2009.09.011 · Cited by 10,021
Beyond the hype: Big data concepts, methods, and analytics
Gandomi, A., & Haider, M. (2015) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2014.10.007 · Cited by 4,226
Clarifying Business Models: Origins, Present, and Future of the Concept
Osterwalder, A., Pigneur, Y., & Tucci, C. L. (2005) · Communications of the Association for Information Systems · doi:10.17705/1cais.01601 · Cited by 3,354
The rise of big data on cloud computing: Review and open research issues
Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Ullah Khan, S. (2015) · Information Systems · doi:10.1016/j.is.2014.07.006 · Cited by 2,656
Neural Networks: A Systematic Introduction
Rojas, R. (1996) · Medical Entomology and Zoology · Cited by 2,142
Big data deep learning: Challenges and perspectives
Chen, X.-W., & Lin, X. (2014) · IEEE Access · doi:10.1109/access.2014.2325029 · Cited by 1,255
Aggarwal, C. C. (2013) · Cited by 832
Good practice in Bayesian network modelling
Chen, S. H., & Pollino, C. (2012) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2012.03.012 · Cited by 659
Research works printed but not linked: 2
- Konstan, 2012, Deconstructing recommender systems: how Amazon and Netflix predict your preferences and prod you to purchase, IEEE Spectrum, 49
- LaValle, 2013, Big data, analytics and the path from insights to value, MIT Sloan Management Review, 21
Cited by
Sorted by citations
Critical analysis of Big Data challenges and analytical methods
Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017) · Journal of Business Research · doi:10.1016/j.jbusres.2016.08.001 · Cited by 2,066
“Thisanalytics is concerned withforecasting and statistical modelling to determinethefuture possibilitiesbased onsupervised,unsupervised, andsemi-supervisedlearningmodels(Joseph&Johnson,2013;Rehman et al., 2016; Waller & Fawcett, 2013).”
BackgroundMethodInfluentialBig data analytics in supply chain management: A state-of-the-art literature review
Nguyen, T., Zhou, L., Spiegler, V. L. M., Ieromonachou, P., & Lin, Y. (2017) · Computers & Operations Research · doi:10.1016/j.cor.2017.07.004 · Cited by 598
Arunachalam, D., Kumar, N., & Kawalek, J. P. (2018) · Transportation Research Part E: Logistics and Transportation Review · doi:10.1016/j.tre.2017.04.001 · Cited by 535
A survey towards an integration of big data analytics to big insights for value-creation
Saggi, M. K., & Jain, S. (2018) · Information Processing & Management · doi:10.1016/j.ipm.2018.01.010 · Cited by 494
Machine Learning based Digital Twin Framework for Production Optimization in Petrochemical Industry
Min, Q., Lu, Y., Liu, Z., Su, C., & Wang, B. (2019) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.05.020 · Cited by 436
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
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