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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).”
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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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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
Value co-creation and customer loyalty
Cossío‐Silva, F., Revilla‐Camacho, M., Vega‐Vázquez, M., & Florencio, B. P. (2015) · Journal of Business Research · doi:10.1016/j.jbusres.2015.10.028 · Cited by 445
A review and future direction of agile, business intelligence, analytics and data science
Larson, D., & Chang, V. (2016) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2016.04.013 · Cited by 427
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Luan, T. H., Gao, L., Li, Z., Xiang, Y., Wei, G., & Sun, L. (2015) · arXiv · doi:10.48550/arxiv.1502.01815 · Cited by 398
A Survey of Evolutionary Algorithms for Decision-Tree Induction
Barros, R. C., Basgalupp, M. P., de Carvalho, A. C. P. L. F., & Freitas, A. A. (2011) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2011.2157494 · Cited by 347
Turning Big data into tiny data: Constant-size coresets for k -means, PCA and projective clustering
Feldman, D., Schmidt, M., & Sohler, C. (2013) · ACM-SIAM Symposium on Discrete Algorithms (SODA) · doi:10.1137/1.9781611973105.103 · Cited by 346
Poisson Noise Reduction with Non-local PCA
Salmon, J., Harmany, Z. T., Deledalle, C., & Willett, R. M. (2013) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-013-0435-6 · Cited by 312
Pang, Z., Chen, Q., Han, W., & Zheng, L. (2012) · Information Systems Frontiers · doi:10.1007/s10796-012-9374-9 · Cited by 303
A survey on indexing techniques for big data: taxonomy and performance evaluation
Gani, A., Siddiqa, A., Shamshirband, S., & Nasaruddin, F. H. (2015) · Knowledge and Information Systems · doi:10.1007/s10115-015-0830-y · Cited by 257
The Emerging "Big Dimensionality"
Zhai, Y., Ong, Y.-S., & Tsang, I. W.-H. (2014) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2014.2326099 · Cited by 251
On Density-Based Data Streams Clustering Algorithms: A Survey
Amini, A., Wah, T. Y., & Saboohi, H. (2014) · Journal of Computer Science and Technology · doi:10.1007/s11390-014-1416-y · Cited by 207
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Kryszkiewicz, M., Cornelis, C., Ciucci, D., Medina, J., Motoda, H., & Raś, Z. W. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-08729-0 · Cited by 187
Xia, W., Jiang, H., Feng, D., & Hua, Y. (2011) · Cited by 187
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Chang, V. I. C., Walters, R. J., & Wills, G. B. (2013) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2013.01.005 · Cited by 181
Self-citationVervliet, N., Debals, O., Sorber, L., & Lathauwer, L. D. (2014) · IEEE Signal Processing Magazine · doi:10.1109/msp.2014.2329429 · Cited by 179
The Business Intelligence as a Service in the Cloud
Chang, V. I. C. (2014) · Future Generation Computer Systems · doi:10.1016/j.future.2013.12.028 · Cited by 178
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Hsieh, C., Sustik, M. A., Dhillon, I. S., Ravikumar, P., & Poldrack, R. A. (2013) · neural information processing systems · Cited by 154
Tradeoffs in scalable data routing for deduplication clusters
Dong, W., Douglis, F., Li, K., Patterson, H., Reddy, S., & Shilane, P. (2011) · doi:10.5555/1960475.1960477 · Cited by 138
Jiang, P., Winkley, J., Zhao, C. H., Munnoch, R., Min, G., & Yang, L. (2014) · IEEE Systems Journal · doi:10.1109/jsyst.2014.2308324 · Cited by 135
A model to compare cloud and non-cloud storage of Big Data
Chang, V. I. C., & Wills, G. B. (2015) · Future Generation Computer Systems · doi:10.1016/j.future.2015.10.003 · Cited by 133
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Verhoef, P. C., Kooge, E., & Walk, N. (2016) · Cited by 121
The BAU GIS system using open source mapwindow
Sadoun, B., Al‐Bayari, O., Al-Azizi, J. I., & Rawashdeh, S. A. (2015) · Human-centric Computing and Information Sciences · doi:10.1186/s13673-014-0018-6 · Cited by 106
Business process analytics using a big data approach
Vera-Baquero, A., Colomo-Palacios, R., & Molloy, O. (2013) · IT Professional · doi:10.1109/mitp.2013.60 · Cited by 101
Cloud computing: A value creation model
Chou, D. C. (2014) · Computer Standards & Interfaces · doi:10.1016/j.csi.2014.10.001 · Cited by 91
A spatiotemporal compression based approach for efficient big data processing on Cloud
Yang, C., Zhang, X., Zhong, C., Liu, C., Pei, J., Ramamohanarao, K., & Chen, J. (2014) · Journal of Computer and System Sciences · doi:10.1016/j.jcss.2014.04.022 · Cited by 85
Thrane, C. (2019) · Routledge eBooks · doi:10.4324/9780429443756 · Cited by 81
Zolnowski, A., Weiss, C., & Böhmann, T. (2014) · Hawaii International Conference on System Sciences (HICSS) · doi:10.1109/hicss.2014.96 · Cited by 75
Value creation in software service platforms
Haile, N., & Altmann, J. (2015) · Future Generation Computer Systems · doi:10.1016/j.future.2015.09.029 · Cited by 72
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Leung, C. K.-S., MacKinnon, R. K., & Jiang, F. (2014) · IEEE International Congress on Big Data (BigData Congress) · doi:10.1109/bigdata.congress.2014.53 · Cited by 68
A Scalable Inline Cluster Deduplication Framework for Big Data Protection
Fu, Y., Jiang, H., & Xiao, N. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-35170-9_18 · Cited by 64
Evolving Fuzzy Rules for Anomaly Detection in Data Streams
Moshtaghi, M., Bezdek, J. C., Leckie, C. A., Karunasekera, S. A., & Palaniswami, M. S. (2014) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2014.2322385 · Cited by 60
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Li, C., Darema, F., & Chang, V. I. C. (2017) · Enterprise Information Systems · doi:10.1080/17517575.2017.1355984 · Cited by 60
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Stateczny, A., & Włodarczyk-Sielicka, M. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-08729-0_34 · Cited by 51
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Lin, M.-S., Chiu, C.-Y., Lee, Y., & Pao, H.-K. (2013) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2013.6691627 · Cited by 46
Martino, B. D., Aversa, R., Cretella, G., Espósito, A., & Kołodziej, J. (2014) · International Journal of Big Data Intelligence · doi:10.1504/ijbdi.2014.063840 · Cited by 46
A Mobile Crowdsensing Ecosystem Enabled by a Cloud-Based Publish/Subscribe Middleware
Antonić, A., Roankovic, K., Marjanović, M., Pripuic, K., & Žarko, I. P. (2014) · International Conference on Future Internet of Things and Cloud (FiCloud) · doi:10.1109/ficloud.2014.27 · Cited by 45
Characterizing the efficiency of data deduplication for big data storage management
Zhou, R., Liu, M., & Li, T. (2013) · IEEE International Symposium on Workload Characterization · doi:10.1109/iiswc.2013.6704674 · Cited by 39
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Jalali, B., & Asghari, M. H. (2014) · Optics and Photonics News · doi:10.1364/opn.25.2.000024 · Cited by 38
Ackermann, K., & Angus, S. D. (2014) · Procedia Computer Science · doi:10.1016/j.procs.2014.05.220 · Cited by 33
An influence assessment method based on co-occurrence for topologically reduced big data sets
Trovati, M., & Bessis, N. (2015) · Soft Computing · doi:10.1007/s00500-015-1621-9 · Cited by 33
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Patty, J., & Penn, E. M. (2014) · PS · doi:10.1017/s1049096514001814 · Cited by 31
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Sharma, S., Chang, V. I. C., Tim, U. S., Wong, J., & Gadia, S. K. (2016) · Cited by 28
Self-citationChuang, H.-M., & Chen, Y. (2015) · Human-centric Computing and Information Sciences · doi:10.1186/s13673-015-0028-z · Cited by 26
Outlook on moving of computing services towards the data sources
Akhbar, F., Chang, V. I. C., Yao, Y., & Muñoz, V. M. (2016) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2016.03.014 · Cited by 25
Self-citationAnalyzing Big Data with Dynamic Quantum ClusteringPreprint
Weinstein, M., Meirer, F., Hume, A., Sciau, P., Shaked, G., Hofstetter, R., Persi, E., Mehta, A., & Horn, D. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1310.2700 · Cited by 23
Missing Value Imputation with Unsupervised Kohonen Self Organizing Map
Singh, N., Zargar, A. J., Chhabra, S., & Kumar, P. (2015) · Springer eBooks · doi:10.1007/978-81-322-2550-8_7 · Cited by 22
How does customer self-construal moderate CRM value creation chain?
Qi, J., Qu, Q., & Zhou, Y. (2014) · Electronic Commerce Research and Applications · doi:10.1016/j.elerap.2014.06.003 · Cited by 16
Chang, V. I. C. (2014) · Future Generation Computer Systems · doi:10.1016/j.future.2014.08.001 · Cited by 16
Self-citationMital, M., Chang, V. I. C., Choudhary, P. K., Pani, A. K., & Sun, Z. (2016) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2016.02.011 · Cited by 16
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Grzegorowski, M., & Stawicki, S. (2015) · Annals of Computer Science and Information Systems · doi:10.15439/2015f425 · Cited by 15
Reduced Topologically Real-World Networks
Trovati, M. (2015) · International Journal of Distributed Systems and Technologies · doi:10.4018/ijdst.2015040102 · Cited by 15
Generative Models for Evolutionary Clustering
Xu, T.-B., Zhang, Z. M., Yu, P. S., & Long, B. (2012) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/2297456.2297459 · Cited by 14
Smart M2M Data Filtering Using Domain-Specific Thresholds in Domain-Agnostic Platforms
Papageorgiou, A. S., Schmidt, M., Song, J., & Kami, N. (2013) · IEEE International Congress on Big Data (BigData Congress) · doi:10.1109/bigdata.congress.2013.45 · Cited by 13
TEMPORARY REMOVAL: Cloud-based emerging services systems
Sharma, S., Chang, V. I. C., Tim, U. S., Wong, J., & Gadiad, S. (2016) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2016.03.006 · Cited by 13
Self-citationThe Concept of Pattern based Data Sharing in Big Data Environments
Rehman, M. H. U., & Batool, A. (2015) · International Journal of Database Theory and Application · doi:10.14257/ijdta.2015.8.4.02 · Cited by 12
Self-citationFrequent pattern mining in mobile devices: A feasibility study
Rehman, M. H. U., Liew, C. S., & Wah, T. Y. (2014) · International Conference on Information Technology and Multimedia (ICIMU) · doi:10.1109/icimu.2014.7066658 · Cited by 11
Self-citationCheng, Y., Jiang, P., & Peng, Y. (2014) · doi:10.1109/cibd.2014.7011524 · Cited by 11
Big Data Analytics = Machine Learning + Cloud ComputingPreprint
Wu, C., Buyya, R., & Ramamohanarao, K. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1601.03115 · Cited by 11
Big data reduction by fitting mathematical functions
Rágyanszki, A., Gerlei, K. Z., Surányi, A., Kelemen, A., Jensen, S. J. K., Csizmadia, I. G., & Viskolcz, B. (2015) · Chemical Physics Letters · doi:10.1016/j.cplett.2015.02.031 · Cited by 9
TEMPORARY REMOVAL: Distributed behavior model orchestration in cognitive internet of things solution
Li, C., Darema, F., & Chang, V. I. C. (2016) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2016.03.012 · Cited by 8
Self-citationBig Data Analytics in Mobile and Cloud Computing Environments
Rehman, M. H. U., Khan, A. U. R., & Batool, A. (2016) · Advances in systems analysis, software engineering, and high performance computing book series · doi:10.4018/978-1-5225-0287-6.ch014 · Cited by 7
Self-citationBig Data Analytics in Mobile and Cloud Computing Environments
Rehman, M. H. U., Khan, A. U. R., & Batool, A. (2017) · IGI Global eBooks · doi:10.4018/978-1-5225-1837-2.ch068 · Cited by 3
Self-citationVidgen, R. T. (2014) · Repository@Hull (Worktribe) (University of Hull) · Cited by 3
REPSense: On-line sensor data reduction while preserving data diversity for mobile sensing
Liu, G., Iwai, M., Tobe, Y., & Sezaki, K. (2013) · IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) · doi:10.1109/wimob.2013.6673417 · Cited by 2
A Spatio-Temporal Simulation Model for Incremental Clustering in Massive Moving Objects Data Set
Alberg, D. (2015) · International Journal of Database Theory and Application · doi:10.14257/ijdta.2015.8.2.02 · Cited by 1
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
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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
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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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