The DOI matches its Crossref record; 148 of the 148 references it cites were found in a registry or an index.
Big Data: A Survey
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“In addition, the combination of datasets in the big data age needs to cope with a number of challenges listed in [20,61], for example efficient data representation, redundancy reduction and data compression, spurious correlations, and noise accumulation.”
From Opening up Smart Cities: Citizen-Centric Challenges and Opportunities from GIScience (2016) · cites this paper as [20]
Citations
Cites
MapReduce: simplified data processing on large clusters
Dean, J., & Ghemawat, S. (2008) · Communications of the ACM · doi:10.1145/1327452.1327492 · Cited by 18,476
The anatomy of a large-scale hypertextual Web search engine
Brin, S., & Page, L. (1998) · Computer Networks and ISDN Systems · doi:10.1016/s0169-7552(98)00110-x · Cited by 16,004
An index to quantify an individual’s scientific research output
Hirsch, J. E. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0507655102 · Cited by 11,669
Foundations of statistical natural language processing
Manning, C. D., & Schütze, H. (1999) · Cited by 9,816
An Introduction to Multivariate Statistical Analysis
Muirhead, R. J., & Anderson, T. W. (1986) · Journal of Business and Economic Statistics · doi:10.2307/1391399 · Cited by 9,179
Top 10 algorithms in data mining
Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G. J., Ng, A., Liu, B., Yu, P. S., et al. (2008) · Knowledge and Information Systems · doi:10.1007/s10115-007-0114-2 · Cited by 5,667
(2011) · Cited by 5,203
Detecting influenza epidemics using search engine query data
Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009) · Nature · doi:10.1038/nature07634 · Cited by 4,295
Big data: a revolution that will transform how we live, work, and think
(2013) · Choice Reviews Online · doi:10.5860/choice.50-6804 · Cited by 3,657
Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing
Zaharia, M., Chowdhury, M., Das, T., Dave, A., Ma, J. H., McCauley, M., Franklin, M. J. M., Shenker, S. J., & Stoica, I. G. (2012) · Cited by 3,622
Research works printed but not linked: 3
- Big data (2008). http://www.nature.com/news/specials/bigdata/index.html
- Special online collection: dealing with big data (2011). http://www.sciencemag.org/site/special/data/
- Sallam RL, Richardson J, Hagerty J, Hostmann B (2011) Magic quadrant for business intelligence platforms. CT, Gartner Group, Stamford
Cited by
Sorted by citations
Big Data in Smart Farming – A review
Wolfert, J., Ge, L., Verdouw, C. N., & Bogaardt, M. J. (2017) · Agricultural Systems · doi:10.1016/j.agsy.2017.01.023 · Cited by 4,196
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
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
Optical Communication in Space: Challenges and Mitigation Techniques
Kaushal, H., & Kaddoum, G. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2016.2603518 · Cited by 1,927
Digital twin: values, challenges and enablers from a modeling perspective
Rasheed, A., San, O., & Kvamsdal, T. (2020) · IEEE Access · doi:10.1109/access.2020.2970143 · Cited by 1,952
Data-driven smart manufacturing
Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2018.01.006 · Cited by 1,934
Digital twin and big data towards smart manufacturing and Industry 4.0: 360 degree comparison
Qi, Q., & Tao, F. (2018) · IEEE Access · doi:10.1109/access.2018.2793265 · Cited by 1,716
Implementing Smart Factory of Industrie 4.0: An Outlook
Wang, S., Wan, J., Li, D., & Zhang, C. (2016) · International Journal of Distributed Sensor Networks · doi:10.1155/2016/3159805 · Cited by 1,527
A survey on digital twin: definitions, characteristics, applications, and design implications
Barricelli, B. R., Casiraghi, E., & Fogli, D. (2019) · IEEE Access · doi:10.1109/access.2019.2953499 · Cited by 1,480
Wang, S., Wan, J., Zhang, D., Li, D., & Zhang, C. (2016) · Computer Networks · doi:10.1016/j.comnet.2015.12.017 · Cited by 1,408
Disease Prediction by Machine Learning Over Big Data From Healthcare Communities
Chen, M., Hao, Y., Hwang, K., Wang, L., & Wang, L. (2017) · IEEE Access · doi:10.1109/access.2017.2694446 · Cited by 1,311
Self-citationMeDShare: Trust-Less Medical Data Sharing Among Cloud Service Providers via Blockchain
Xia, Q., Sifah, E. B., Asamoah, K. O., Gao, J., Du, X., & Guizani, M. (2017) · IEEE Access · doi:10.1109/access.2017.2730843 · Cited by 1,148
Big Data Analytics in Intelligent Transportation Systems: A Survey
Zhu, L., Yu, F. R., Wang, Y., Ning, B., & Tang, T. (2018) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2018.2815678 · Cited by 1,079
The role of big data in smart city
Hashem, I. A. T., Chang, V., Anuar, N. B., Adewole, K., Yaqoob, I., Gani, A., Ahmed, E., & Chiroma, H. (2016) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2016.05.002 · Cited by 1,103
Machine learning in mental health: a scoping review of methods and applications
Shatte, A. B. R., Hutchinson, D. M., & Teague, S. J. (2019) · Psychological Medicine · doi:10.1017/s0033291719000151 · Cited by 1,047
Big Data technologies: A survey
Oussous, A., Benjelloun, F.-Z., Lahcen, A. A., & Belfkih, S. (2017) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2017.06.001 · Cited by 997
Health-CPS: Healthcare Cyber-Physical System Assisted by Cloud and Big Data
Zhang⋆, Y., Meikang Qiu, A., Tsai, C., Hassan, M. M., & Alamri, A. M. (2015) · IEEE Systems Journal · doi:10.1109/jsyst.2015.2460747 · Cited by 927
A survey of machine learning for big data processing
Qiu, J., Wu, Q., Ding, G., Xu, Y., & Feng, S. (2016) · EURASIP Journal on Advances in Signal Processing · doi:10.1186/s13634-016-0355-x · Cited by 973
Can big data and predictive analytics improve social and environmental sustainability?
Dubey, R., Gunasekaran, A., Childe, S. J., Papadopoulos, T., Luo, Z., Wamba, S. F., & Roubaud, D. (2019) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2017.06.020 · Cited by 860
Factors influencing big data decision-making quality
Janssen, M., van der Voort, H., & Wahyudi, A. (2017) · Journal of Business Research · doi:10.1016/j.jbusres.2016.08.007 · Cited by 799
Internet of Things (IoT) and the Energy Sector
Motlagh, N. H., Mohammadrezaei, M., Hunt, J. D., & Zakeri, B. (2020) · Energies · doi:10.3390/en13020494 · Cited by 837
Bibri, S. E. (2018) · Sustainable Cities and Society · doi:10.1016/j.scs.2017.12.034 · Cited by 799
Geospatial big data handling theory and methods: A review and research challenges
Li, S., Dragicevic, S., Castro, F. A., Sester, M., Winter, S., Coltekin, A., Pettit, C., Jiang, B., Haworth, J., Stein, A., et al. (2016) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2015.10.012 · Cited by 749
The use of machine learning algorithms in recommender systems: a systematic review
Portugal, I., Alencar, P., & Cowan, D. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.12.020 · Cited by 743
Sun, L., Shang, Z., Xia, Y., Bhowmick, S., & Nagarajaiah, S. (2020) · Journal of Structural Engineering · doi:10.1061/(asce)st.1943-541x.0002535 · Cited by 751
Zhuang, C., Liu, J., & Xiong, H. (2018) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-018-1617-6 · Cited by 700
Behind the definition of Industry 4.0: Analysis and open questions
Culot, G., Nassimbeni, G., Orzes, G., & Sartor, M. (2020) · International Journal of Production Economics · doi:10.1016/j.ijpe.2020.107617 · Cited by 746
Leitão, P., Colombo, A. W., & Karnouskos, S. (2015) · Computers in Industry · doi:10.1016/j.compind.2015.08.004 · Cited by 674
Uncertainty in Big Data Analytics: Survey, Opportunities, and Challenges
Hariri, R. H., Fredericks, E. M., & Bowers, K. M. (2019) · Journal Of Big Data · doi:10.1186/s40537-019-0206-3 · Cited by 620
A survey on mobile edge computing
Ahmed, A., & Ahmed, E. (2016) · International Conference on Intelligent Systems and Control · doi:10.1109/isco.2016.7727082 · Cited by 611
Efficient IoT-based sensor BIG Data collection–processing and analysis in smart buildings
Plageras, A. P., Psannis, K. E., Stergiou, C. L., Wang, H., & Gupta, B. B. (2017) · Future Generation Computer Systems · doi:10.1016/j.future.2017.09.082 · Cited by 596
5G wireless backhaul networks: challenges and research advances
Ge, X., Cheng, H., Guizani, M., & Han, T. (2014) · IEEE Network · doi:10.1109/mnet.2014.6963798 · Cited by 581
Brief introduction of medical database and data mining technology in big data era
Yang, J., Li, Y., Liu, Q., Li, L., Feng, A., Wang, T., Zheng, S., Xu, A., & Lyu, J. (2020) · Journal of Evidence-Based Medicine · doi:10.1111/jebm.12373 · Cited by 603
König, I. R., Fuchs, O., Hansen, G., von Mutius, E., & Kopp, M. (2017) · European Respiratory Journal · doi:10.1183/13993003.00391-2017 · Cited by 605
Survey on blockchain for Internet of Things
Wang, X., Zha, X., Ni, W., Liu, R. P., Guo, Y. J., Niu, X., & Zheng, K. (2019) · Computer Communications · doi:10.1016/j.comcom.2019.01.006 · Cited by 567
A review of industrial wireless networks in the context of Industry 4.0
Li, X., Li, D., Wan, J., Vasilakos, A. V., Lai, C., & Wang, S. (2015) · Wireless Networks · doi:10.1007/s11276-015-1133-7 · Cited by 556
Sun, A. Y., & Scanlon, B. R. (2019) · Environmental Research Letters · doi:10.1088/1748-9326/ab1b7d · Cited by 585
Decision Tree and SVM-Based Data Analytics for Theft Detection in Smart Grid
Jindal, A., Dua, A., Kaur, K., Singh, M. K., Kumar, N., & Mishra, S. (2016) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2016.2543145 · Cited by 551
Big Data: Survey, Technologies, Opportunities, and Challenges
Khan, N., Yaqoob, I., Hashem, I. A. T., Inayat, Z., Mahmoud Ali, W. K., Alam, M., Shiraz, M., & Gani, A. (2014) · The Scientific World Journal · doi:10.1155/2014/712826 · Cited by 547
Mechanistic models for additive manufacturing of metallic components
Wei, H., Mukherjee, T., Zhang, W., Zuback, J. S., Knapp, G., De, A., & DebRoy, T. (2020) · Progress in Materials Science · doi:10.1016/j.pmatsci.2020.100703 · Cited by 551
Data Mining for the Internet of Things: Literature Review and Challenges
Chen, F., Deng, P., Wan, J., Zhang, D., Vasilakos, A. V., & Rong, X. (2015) · International Journal of Distributed Sensor Networks · doi:10.1155/2015/431047 · Cited by 530
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
Real-time big data processing for anomaly detection: A survey
Ariyaluran Habeeb, R. A., Nasaruddin, F., Gani, A., Targio Hashem, I. A., Ahmed, E., & Imran, M. (2019) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2018.08.006 · Cited by 487
Structural Health Monitoring Framework Based on Internet of Things: A Survey
Tokognon, C. A., Gao, B., Tian, G. Y., & Yan, Y. (2017) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2664072 · Cited by 454
Context-Aware Computing, Learning, and Big Data in Internet of Things: A Survey
Sezer, Ö. B., Doğdu, E., & Özbayoğlu, A. M. (2017) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2773600 · Cited by 468
A state-of the-art survey & testbed of fuzzy AHP (FAHP) applications
Sylvain Kubler, A., Robert, J., Derigent, W. J. E., Voisin, A., & Traon, Y. L. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.08.064 · Cited by 447
Smart Clothing: Connecting Human with Clouds and Big Data for Sustainable Health Monitoring
Chen, M., Ma, Y., Song, J., Lai, C., & Hu, B. (2016) · Mobile Networks and Applications · doi:10.1007/s11036-016-0745-1 · Cited by 446
Self-citationA Manufacturing Big Data Solution for Active Preventive Maintenance
Wan, J., Tang, S., Li, D., Wang, S., Liu, C., Abbas, H., & Vasilakos, A. V. (2017) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2017.2670505 · Cited by 443
Business intelligence and big data in hospitality and tourism: a systematic literature review
Mariani, M. M., Baggio, R., Fuchs, M., & Höpken, W. (2018) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-07-2017-0461 · Cited by 502
Akpan, I. J., Udoh, E. A. P., & Adebisi, B. (2020) · Journal of Small Business & Entrepreneurship · doi:10.1080/08276331.2020.1820185 · Cited by 524
Revolution 4.0: Industry vs. Agriculture in a Future Development for SMEs
Zambon, I., Cecchini, M., Egidi, G., Saporito, M. G., & Colantoni, A. (2019) · Processes · doi:10.3390/pr7010036 · Cited by 457
Aceto, G., Persico, V., & Pescapè, A. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2938259 · Cited by 435
Zhang, J., Tian, G. Y., Marindra, A. M. J., Sunny, A. I., & Zhao, A. (2017) · Sensors · doi:10.3390/s17020265 · Cited by 417
Wan, J., Li, X.-M., Dai, H., Kusiak, A., Martinez-García, M. A., & Li, D. (2020) · Proceedings of the IEEE · doi:10.1109/jproc.2020.3034808 · Cited by 272
Security, privacy and trust of different layers in Internet-of-Things (IoTs) framework
Tewari, A., & Gupta, B. B. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.04.027 · Cited by 418
Big data: From beginning to future
Yaqoob, I., Hashem, I. A. T., Gani, A., Mokhtar, S., Ahmed, E., Anuar, N. B., & Vasilakos, A. V. (2016) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2016.07.009 · Cited by 410
Big data applications in operations/supply-chain management: A literature review
Addo-Tenkorang, R., & Helo, P. (2016) · Computers & Industrial Engineering · doi:10.1016/j.cie.2016.09.023 · Cited by 401
Incremental on-line learning: A review and comparison of state of the art algorithms
Losing, V., Hammer, B., & Wersing, H. (2018) · Neurocomputing · doi:10.1016/j.neucom.2017.06.084 · Cited by 382
Next-Generation Big Data Analytics: State of the Art, Challenges, and Future Research Topics
Lv, Z., Song, H. H., Val, P. B., Steed, A. J., & Jo, M. (2017) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2017.2650204 · Cited by 366
Estimating the intrinsic dimension of datasets by a minimal neighborhood information
Facco, E., D’Errico, M. N., Rodríguez, Á., & Laio, A. (2017) · Scientific Reports · doi:10.1038/s41598-017-11873-y · Cited by 360
Optimized IoT service placement in the fog
Skarlat, O., Nardelli, M., Schulte, S., Borkowski, M., & Leitner, P. (2017) · Service Oriented Computing and Applications · doi:10.1007/s11761-017-0219-8 · Cited by 357
Cortés, R., Bonnaire, X., Marin, O., & Sens, P. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.05.093 · Cited by 356
Applications of the Internet of Things (IoT) in Smart Logistics: A Comprehensive Survey
Song, Y., Yu, F. R., Zhou, L., Yang, X., & He, Z. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3034385 · Cited by 382
Baccarelli, E., Naranjo, P. G. V., Scarpiniti, M., Shojafar, M., & Abawajy, J. (2017) · IEEE Access · doi:10.1109/access.2017.2702013 · Cited by 350
Mocrii, D., Chen, Y., & Musı́lek, P. (2018) · Internet of Things · doi:10.1016/j.iot.2018.08.009 · Cited by 356
Opportunities and Challenges for Big Data in Agricultural and Environmental Analysis
Weersink, A. J., Fraser, E., Pannell, D. J., Duncan, E., & Rotz, S. (2018) · Annual Review of Resource Economics · doi:10.1146/annurev-resource-100516-053654 · Cited by 347
Wagg, D., Worden, K., Barthorpe, R. J., & Gardner, P. A. (2020) · ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B Mechanical Engineering · doi:10.1115/1.4046739 · Cited by 382
Big Data sources and methods for social and economic analyses
Blazquez-Soriano, A., & Domènech, J. (2017) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2017.07.027 · Cited by 372
Blockchain for Large-Scale Internet of Things Data Storage and Protection
Li, R., Song, T., Mei, B., Li, H., Cheng, X., & Sun, L. (2018) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2018.2853167 · Cited by 345
Big data in the policy cycle: Policy decision making in the digital era
Höchtl, J., Parycek, P., & Schöllhammer, R. (2016) · Journal of Organizational Computing and Electronic Commerce · doi:10.1080/10919392.2015.1125187 · Cited by 331
Situating Ecology as a Big-Data Science: Current Advances, Challenges, and Solutions
Farley, S. S., Dawson, A., Goring, S. J., & Williams, J. W. (2018) · BioScience · doi:10.1093/biosci/biy068 · Cited by 331
When things matter: A survey on data-centric internet of things
Qin, Y., Sheng, Q. Z., Falkner, N., Dustdar, S., Wang, H., & Vasilakos, A. V. (2016) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2015.12.016 · Cited by 331
Fog Computing for the Internet of Things
Puliafito, C., Mingozzi, E., Longo, F., Puliafito, A., & Rana, O. (2019) · ACM Trans. Internet Techn. · doi:10.1145/3301443 · Cited by 284
Construction 4.0: A Literature Review
Forcael, E., Ferrari, I., Opazo-Vega, A., & Pulido-Arcas, J. A. (2020) · Sustainability · doi:10.3390/su12229755 · Cited by 356
Big data and dynamic capabilities: a bibliometric analysis and systematic literature review
Rialti, R., Marzi, G., Ciappei, C., & Busso, D. (2019) · Management Decision · doi:10.1108/md-07-2018-0821 · Cited by 360
Smart poultry management: Smart sensors, big data, and the internet of things
Astill, J., Dara, R. A., Fraser, E., Roberts, B., & Sharif, S. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105291 · Cited by 322
A Survey on SCADA Systems: Secure Protocols, Incidents, Threats and Tactics
Pliatsios, D., Sarigiannidis, P., Lagkas, T., & Sarigiannidis, A. G. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2987688 · Cited by 320
Big Data Driven Vehicular Networks
Cheng, N., Lyu, F., Chen, J., Xu, W., Zhou, H., Zhang, S., & Shen, X. (2018) · IEEE Network · doi:10.1109/mnet.2018.1700460 · Cited by 307
–Omic and Electronic Health Record Big Data Analytics for Precision Medicine
Wu, P.-Y., Cheng, C., Kaddi, C. D., Venugopalan, J., Hoffman, R., & Wang, M. D. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2573285 · Cited by 317
Gölzer, P., & Fritzsche, A. (2017) · Production Planning & Control · doi:10.1080/09537287.2017.1375148 · Cited by 305
Big Data for Internet of Things: A Survey
Ge, M., Bangui, H., & Bühnová, B. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.04.053 · Cited by 318
A bibliometric approach to tracking big data research trends
Kalantari, A., Kamsin, A., Kamaruddin, H. S., Ebrahim, N. A., Gani, A., Ali, E., & Band, S. S. (2017) · Journal Of Big Data · doi:10.1186/s40537-017-0088-1 · Cited by 309
A novel big data analytics framework for smart cities
Osman, A. M. S. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.06.046 · Cited by 299
Evolution and future of manufacturing systems
ElMaraghy, H. A., Monostori, L., Schuh, G., & ElMaraghy, W. H. (2021) · CIRP Annals · doi:10.1016/j.cirp.2021.05.008 · Cited by 311
A survey on the role of Internet of Things for adopting and promoting Agriculture 4.0
Raj, M., Gupta, S. R., Chamola, V., Elhence, A., Garg, T., Atiquzzaman, M., & Niyato, D. T. (2021) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2021.103107 · Cited by 319
Multiobjective Cloud Workflow Scheduling: A Multiple Populations Ant Colony System Approach
Chen, Z.-G., Zhan, Z., Lin, Y., Gong, Y., Gu, T., Zhao, F., Yuan, H., Chen, X., Li, Q., & Zhang, J. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2832640 · Cited by 285
Bao, J., Liu, P., & Ukkusuri, S. V. (2018) · Accident Analysis & Prevention · doi:10.1016/j.aap.2018.10.015 · Cited by 290
Bibri, S. E. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0182-7 · Cited by 278
Big data in lean six sigma: a review and further research directions
Gupta, S., Modgil, S., & Gunasekaran, A. (2019) · International Journal of Production Research · doi:10.1080/00207543.2019.1598599 · Cited by 281
Leveraging big data analytics in healthcare enhancement: trends, challenges and opportunities
Rehman, A., Naz, S., & Razzak, I. (2021) · Multimedia Systems · doi:10.1007/s00530-020-00736-8 · Cited by 312
Xia, F., Wang, W. W., Bekele, T. M., & Liu, H. (2017) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2016.2641460 · Cited by 268
30 years of intelligence models in management and business: A bibliometric review
López-Robles, J. R., Otegi-Olaso, J. R., Porto Gómez, I., & Cobo, M. J. (2019) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.01.013 · Cited by 268
A blockchain enabled Cyber-Physical System architecture for Industry 4.0 manufacturing systems
Lee, J., Azamfar, M., & Singh, J. (2019) · Manufacturing Letters · doi:10.1016/j.mfglet.2019.05.003 · Cited by 269
Bresciani, S., Ciampi, F., Meli, F., & Ferraris, A. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2021.102347 · Cited by 273
Ag-IoT for crop and environment monitoring: Past, present, and future
Chamara, N., Islam, M. D., Bai, G., Shi, Y., & Ge, Y. (2022) · Agricultural Systems · doi:10.1016/j.agsy.2022.103497 · Cited by 291
A survey on position-based routing for vehicular ad hoc networks
Liu, J., Wan, J., Wang, Q., Deng, P., Zhou, K., & Qiao, Y. (2015) · Telecommunication Systems · doi:10.1007/s11235-015-9979-7 · Cited by 253
KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift
Losing, V., Hammer, B., & Wersing, H. (2016) · Proceedings · doi:10.1109/icdm.2016.0040 · Cited by 260
Big Data Meet Cyber-Physical Systems: A Panoramic Survey
Atat, R., Liu, L., Wu, J., Li, G., Ye, C., & Yang, Y. (2018) · IEEE Access · doi:10.1109/access.2018.2878681 · Cited by 247
Mapping mineral prospectivity through big data analytics and a deep learning algorithm
Xiong, Y., Zuo, R., & Carranza, E. J. M. (2018) · Ore Geology Reviews · doi:10.1016/j.oregeorev.2018.10.006 · Cited by 252
Big data in the public sector: Uncertainties and readiness
Klievink, B., Romijn, B.-J., Cunningham, S. W., & de Bruijn, H. (2016) · Information Systems Frontiers · doi:10.1007/s10796-016-9686-2 · Cited by 294
Metadata
- Authors
- Chen, M., Mao, S., & Liu, Y.
- Affiliations
- Huazhong University of Science and Technology (CN)
- Auburn University (US)
- Tsinghua University (CN)
- Corresponding author
- Chen, Min
- Venue
- Mobile Networks and Applications
- Published
- 2014-01-22
- Volume
- 19
- Issue
- 2
- Pages
- 171-209
- ISSN
- 1383-469X (ISSN-L)
- 1572-8153
- OpenAlex
- W2159128662
- Cited by
- 2,913
- References
- 148
- Citations by year
- 2026: 117 · 2025: 166 · 2024: 164 · 2023: 171 · 2022: 206 · 2021: 293 · 2020: 323 · 2019: 341 · 2018: 389 · 2017: 309 · 2016: 256 · 2015: 146 · 2014: 28
- FWCI
- 461.46
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Mobile Networks and Applications (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Physical Sciences › Computer Science › Information Systems › Big Data and Digital Economy
- Keywords
- big data, cloud computing, Internet of Things, data centers, Hadoop, data generation, data acquisition, data storage, data analysis, business management, social networking sites, medical applications, collective intelligence, smart grid
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 ›
Are you an author, or do you see a problem with this page? Request a review