102,256
Views
452
CrossRef citations to date
0
Altmetric
Articles

Big Data and cloud computing: innovation opportunities and challenges

, , , &
Pages 13-53 | Received 23 Jul 2016, Accepted 17 Sep 2016, Published online: 03 Nov 2016

References

  • Abbas, A., K. Bilal, L. Zhang, and S. U. Khan. 2015. “A Cloud Based Health Insurance Plan Recommendation System: A User Centered Approach.” Future Generation Computer Systems 43–44: 99–109.
  • Abolfazli, S., Z. Sanaei, E. Ahmed, A. Gani, and R. Buyya. 2014. “Cloud-Based Augmentation for Mobile Devices: Motivation, Taxonomies, and Open Challenges.” IEEE Communications Surveys Tutorials 16 (1): 337–368.
  • Abouzeid, A., K. Bajda-Pawlikowski, D. Abadi, A. Silberschatz, and A. Rasin. 2009. “HadoopDB: An Architectural Hybrid of MapReduce and DBMS Technologies for Analytical Workloads.” Proceedings of the VLDB Endowment 2 (1): 922–933.
  • Abraham, A., and M. Paprzycki. 2004. “Significance of Steganography on Data Security.” In Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04). Vol. 2, 347–351. IEEE.
  • Aghabozorgi, S., A. Seyed Shirkhorshidi, and T. Ying Wah. 2015. “Time-series Clustering – A Decade Review.” Information Systems 53 (C): 16–38.
  • Agrawal, D., P. Bernstein, E. Bertino, S. Davidson, U. Dayal, M. Franklin, J. Gehrke, et al. 2011. Challenges and Opportunities with Big Data 2011–1. Cyber Center Technical Reports. http://docs.lib.purdue.edu/cctech/1.
  • Agrawal, D., S. Das, and A. El Abbadi. 2011. “Big Data and Cloud Computing: Current State and Future Opportunities.” In Proceedings of the 14th International Conference on Extending Database Technology, 530–533. ACM.
  • Aji, A., F. Wang, H. Vo, R. Lee, Q. Liu, X. Zhang, and J. Saltz. 2013. “Hadoop GIS: A High Performance Spatial Data Warehousing System Over Mapreduce.” Proceedings of the VLDB Endowment 6: 1009–1020.
  • Alam, S., F. D. Albareti, C. A. Prieto, F. Anders, S. F. Anderson, B. H. Andrews, E. Armengaud, et al. 2015. “The Eleventh and Twelfth Data Releases of the Sloan Digital Sky Survey: Final Data from SDSS-III.” The Astrophysical Journal Supplement Series 219 (1): 1–27.
  • Alvaro, P., T. Condie, N. Conway, K. Elmeleegy, J. M. Hellerstein, and R. Sears. 2010. “Boom Analytics: Exploring Data-centric, Declarative Programming for the Cloud.” In Proceedings of the 5th European Conference on Computer Systems, 223–236. New York, NY: ACM.
  • Alyass, A., M. Turcotte, and D. Meyre. 2015. “From Big Data Analysis to Personalized Medicine for All: Challenges and Opportunities.” BMC Medical Genomics 8: 1–33.
  • Aminzadeh, N., Z. Sanaei, and S. H. Ab Hamid. 2015. “Mobile Storage Augmentation in Mobile Cloud Computing: Taxonomy, Approaches, and Open Issues.” Simulation Modelling Practice and Theory 50: 96–108.
  • Ammn, N., and M. Irfanuddin. 2013. “Big Data Challenges.” International Journal of Advanced Trends in Computer Science and Engineering 2 (1): 613–615.
  • Anderson, D. P., and G. Fedak. 2006. “The Computational and Storage Potential of Volunteer Computing.” In Proceedings of the Sixth IEEE International Symposium on Cluster Computing and the Grid, 73–80. Washington, DC: IEEE Computer Society.
  • Andreolini, M., M. Colajanni, M. Pietri, and S. Tosi. 2015. “Adaptive, Scalable and Reliable Monitoring of Big Data on Clouds.” Journal of Parallel and Distributed Computing 79–80 (C): 67–79.
  • Assunção, M. D., R. N. Calheiros, S. Bianchi, M. A. Netto, and R. Buyya. 2015. “Big Data Computing and Clouds: Trends and Future Directions.” Journal of Parallel and Distributed Computing 79–80: 3–15.
  • Aydin, G., I. R. Hallac, B. Karakus, G. Aydin, I. R. Hallac, and B. Karakus. 2015. “Architecture and Implementation of a Scalable Sensor Data Storage and Analysis System Using Cloud Computing and Big Data Technologies.” Journal of Sensors, Journal of Sensors. doi:10.1155/2015/834217.
  • Bae, B.-J., Y.-J. Kim, Y.-K. Kim, O.-K. Ha, and Y.-K. Jun. 2014. “An Intrusive Analyzer for Hadoop Systems Based on Wireless Sensor Networks.” International Journal of Distributed Sensor Networks. doi:10.1155/2014/196040.
  • Balakrishna, C. 2012. “Enabling Technologies for Smart City Services and Applications.” In 6th International Conference on Next Generation Mobile Applications, Services and Technologies (NGMAST2012), 223–227.
  • Banditwattanawong, T., M. Masdisornchote, and P. Uthayopas. 2014. “Economical and Efficient Big Data Sharing with I-Cloud.” In International Conference on Big Data and Smart Computing (BIGCOMP), 2014, 105–110.
  • Baughman, A. K., R. J. Bogdany, C. McAvoy, R. Locke, B. O’Connell, and C. Upton. 2015. “Predictive Cloud Computing with Big Data: Professional Golf and Tennis Forecasting [Application Notes].” IEEE Computational Intelligence Magazine 10 (3): 62–76.
  • Baumann, P., P. Mazzetti, J. Ungar, R. Barbera, D. Barboni, A. Beccati, L. Bigagli, et al. 2016. “Big Data Analytics for Earth Sciences: The EarthServer Approach.” International Journal of Digital Earth 9 (1): 3–29.
  • Belaud, J.-P., S. Negny, F. Dupros, D. Michéa, and B. Vautrin. 2014. “Collaborative Simulation and Scientific Big Data Analysis: Illustration for Sustainability in Natural Hazards Management and Chemical Process Engineering.” Computers in Industry 65 (3): 521–535.
  • Belissent, J. 2010. Getting Clever About Smart Cities: New Opportunities Require New Business Models. Accessed November 25, 2015. http://193.40.244.77/iot/wp-content/uploads/2014/02/getting_clever_about_smart_cities_new_opportunities.pdf.
  • Bellettini, C., M. Camilli, L. Capra, and M. Monga. 2013. “MaRDiGraS: Simplified Building of Reachability Graphs on Large Clusters.” Reachability Problems 8169: 83–95.
  • Benediktsson, J. A., J. Chanussot, and W. M. Moon. 2013. “Advances in Very-High-Resolution Remote Sensing.” Proceedings of the IEEE 101 (3): 566–569.
  • Benjamins, R., J. Contreras, O. Corcho, and A. Gomez-Perez. 2002. The Six Challenges of the Semantic Web. In Proceedings of International Semantic Web Conference (ISWC2002), Sardinia, Italia, 2002. Accessed November 25, 2015. https://wikis.gsic.uva.es/juaase/images/b/bb/Benjaminsetal.pdf.
  • Berkovich, S., and D. Liao. 2012. On Clusterization of Big Data Streams. In Proceedings of the 3rd International Conference on Computing for Geospatial Research and Applications, 9. ACM. Accessed November 25, 2015. http://portalparts.acm.org/2350000/2345316/fm/frontmatter.pdf?ip = 100.36.182.180&CFID = 791206509&CFTOKEN = 86783680.
  • Bertino, E., P. Bernstein, D. Agrawal, S. Davidson, U. Dayal, M. Franklin, J. Gehrke, et al. 2011. Challenges and Opportunities with Big Data. Accessed November 25, 2015. http://docs.lib.purdue.edu/ccpubs/445/.
  • Bertino, E., and M. Kantarcioglu. 2014. Big Data – Security with Privacy. Accessed November 25, 2015. https://www.cs.purdue.edu/homes/bertino/RFI-Response-NSF-BigData-SP-Oct16.pdf.
  • Bicer, T., D. Chiu, and G. Agrawal. 2012. Time and Cost Sensitive Data-Intensive Computing on Hybrid Clouds. In Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012), 636–643. IEEE Computer Society.
  • Bird, I. 2011. “Computing for the Large Hadron Collider.” Annual Review of Nuclear and Particle Science 61: 99–118.
  • Bo, Y., and H. Wang. 2011. The Application of Cloud Computing and the Internet of Things in Agriculture and Forestry. In International Joint Conference on Service Sciences (IJCSS), 2011, 168–172.
  • Bonomi, F., R. Milito, J. Zhu, and S. Addepalli. 2012. “Fog Computing and its Role in the Internet of Things.” Proceedings of the MCC Workshop on Mobile Cloud Computing. doi:10.1145/2342509.2342513.
  • Boyd, D., and K. Crawford. 2012. “Critical Questions for Big Data: Provocations for a Cultural, Technological, and Scholarly Phenomenon.” Information, Communication & Society 15 (5): 662–679.
  • Brunswicker, S., E. Bertino, and S. Matei. 2015. “Big Data for Open Digital Innovation – A Research Roadmap.” Big Data Research 2 (2): 53–58.
  • Bryant, R., R. H. Katz, and E. D. Lazowska. 2008. Big-data Computing: Creating Revolutionary Breakthroughs in Commerce, Science and Society. Accessed November 25, 2015. http://www.datascienceassn.org/sites/default/files/Big%20Data%20Computing%202008%20Paper.pdf.
  • Bughin, J., M. Chui, and J. Manyika. 2010. “Clouds, Big Data, and Smart Assets: Ten Tech-Enabled Business Trends to Watch.” McKinsey Quarterly 56 (1): 75–86.
  • Burtica, R., E. M. Mocanu, M. I. Andreica, and N. Ţăpuş. 2012. Practical Application and Evaluation of No-SQL Databases in Cloud Computing. In Proceedings of the 2012 IEEE International Systems Conference (SysCon), 1–6.
  • Camarinha-Matos, L. M., S. Tomic, and P. Graça, (Eds.). 2013. Technological Innovation for the Internet of Things: 4th IFIP WG 5.5/SOCOLNET. Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2013, Costa de Caparica, Portugal, April 15–17, 2013, Proceedings (Vol. 394). Springer.
  • Campa, S., M. Danelutto, M. Goli, H. González-Vélez, A. M. Popescu, and M. Torquati. 2014. “Parallel Patterns for Heterogeneous CPU/GPU Architectures: Structured Parallelism from Cluster to Cloud.” Future Generation Computer Systems, 37: 354–366.
  • Cao, G., S. Wang, M. Hwang, A. Padmanabhan, Z. Zhang, and K. Soltani. 2015. “A Scalable Framework for Spatiotemporal Analysis of Location-Based Social Media Data.” Computers, Environment and Urban Systems 51: 70–82.
  • Cao, Y., C. Yang, and D. Wong. 2009. “An Interoperable Spatiotemporal Weather Radar Data Disseminating System.” International Journal of Remote Sensing 30: 1313–1326.
  • Cary, A. 2011. Scaling Geospatial Searches in Large Spatial Databases. Accessed May 27, 2016. http://140.98.202.196/xpl/abstractReferences.jsp?reload = true&tp=&arnumber = 5576271&url = http%3A%2F%2F140.98.202.196%2Fxpls%2Ficp.jsp%3Farnumber%3D5576271.
  • Cary, A., Y. Yesha, M. Adjouadi, and N. Rishe. 2010. Leveraging Cloud Computing in Geodatabase Management. In IEEE International Conference on Granular Computing (GrC), 73–78. IEEE.
  • Castiglione, A., M. Gribaudo, M. Iacono, and F. Palmieri. 2014. “Modeling Performances of Concurrent Big Data Applications.” Software: Practice and Experience 45 (8): 1127–1144.
  • Cavoukian, A., and J. Jonas. 2012. Privacy by Design in the Age of Big Data. Information and Privacy Commissioner of Ontario, Canada. Accessed December 3 2015. https://privacybydesign.ca/content/uploads/2012/06/pbd-big_data.pdf.
  • Cheatham, M. 2015. Privacy in the Age of Big Data. In The 2015 International Conference on Collaboration Technologies and Systems (CTS), 334–335.
  • Chen, J., Y. Chen, X. Du, C. Li, J. Lu, S. Zhao, and X. Zhou. 2013. “Big Data Challenge: A Data Management Perspective.” Frontiers of Computer Science 7 (2): 157–164.
  • Chen, H., R. H. Chiang, and V. C. Storey. 2012. “Business Intelligence and Analytics: From Big Data to Big Impact.” MIS Quarterly 36 (4): 1165–1188.
  • Chen, M., S. Mao, Y. Zhang, and V. C. Leung. 2014a. Chapter 1, Big Data: Related Technologies, Challenges and Future Prospects. Heidelberg: Springer.
  • Chen, Q., L. Wang, and Z. Shang. 2008. MRGIS: A MapReduce-Enabled High Performance Workflow System for GIS, EScience, 2008. In IEEE Fourth International Conference on eScience, 2008 (eScience'08),, 646–651. IEEE.
  • Chen, Z. K., S. Q. Yang, S. Tan, H. Zhao, L. He, G. Zhang, and H. Y. Yang. 2014b. “The Data Allocation Strategy Based on Load in NoSQL Database.” Applied Mechanics and Materials 513–517: 1464–1469.
  • Chen, B. Y., H. Yuan, Q. Li, S.-L. Shaw, W. H. Lam, and X. Chen. 2015. “Spatiotemporal Data Model for Network Time Geographic Analysis in the Era of Big Data.” International Journal of Geographical Information Science 30 (6): 1041–1071.
  • Chen, C. P., and C.-Y. Zhang. 2014. “Data-Intensive Applications, Challenges, Techniques and Technologies: A Survey on Big Data.” Information Sciences 275: 314–347.
  • Chen, D., and H. Zhao. 2012. Data Security and Privacy Protection Issues in Cloud Computing. In 2012 International Conference on Computer Science and Electronics Engineering (ICCSEE), Vol.1, 647–651. IEEE.
  • Cheng, H., C. Rong, K. Hwang, W. Wang, and Y. Li. 2015. “Secure Big Data Storage and Sharing Scheme for Cloud Tenants.” China Communications 12 (6): 106–115.
  • Cheng, H., H. Yang, and C. Rong. 2012. Distributed Systems Combined with Advanced Network: Evolution, Applications and Challenges. In 2012 8th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM), 1–4. IEEE.
  • Choi, C., J. Choi, and P. Kim. 2014. “Ontology-Based Access Control Model for Security Policy Reasoning in Cloud Computing.” The Journal of Supercomputing 67 (3): 711–722.
  • Christen, P. 2014. Privacy Aspects in Big Data Integration: Challenges and Opportunities. In Proceedings of the First International Workshop on Privacy and Security of Big Data, 1–1. ACM.
  • Chung, W.-C., C.-C. Chen, J.-M. Ho, C.-Y. Lin, W.-L. Hsu, Y.-C. Wang, D. T. Lee, F. Lai, C.-W. Huang and Y.-J. Chang. 2014. “CloudDOE: A User-Friendly Tool for Deploying Hadoop Clouds and Analyzing High-Throughput Sequencing Data with MapReduce.” PLoS ONE 9 (6): e98146.
  • Church, P., A. Goscinski, and C. Lefèvre. 2015. “Exposing HPC and Sequential Applications as Services Through the Development and Deployment of a SaaS Cloud.” Future Generation Computer Systems 43–44: 24–37.
  • Coppersmith, D. 1994. “The Data Encryption Standard (DES) and Its Strength Against Attacks.” IBM Journal of Research and Development 38 (3): 243–250.
  • Coskun, V., B. Ozdenizci, and K. Ok. 2013. “A Survey on Near Field Communication (NFC) Technology.” Wireless Personal Communications 71 (3): 2259–2294.
  • Cosulschi, M., A. Cuzzocrea, and R. De Virgilio. 2013. Implementing bfs-Based Traversals of rdf Graphs over Mapreduce Efficiently. In Cluster, Cloud and Grid Computing (CCGrid), 2013 13th, May. IEEE/ACM International Symposium on, 569–574). IEEE.
  • Cukier, K. 2010. Data, Data Everywhere, Economist. Accessed November 25, 2015. http://www.economist.com/node/15557443.
  • Cuzzocrea, A., G. Fortino, and O. Rana. 2013. Managing Data and Processes in Cloud-Enabled Large-Scale Sensor Networks: State-Of-The-Art and Future Research Directions. In 2013 13th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), 583–588. IEEE.
  • Dantan, J., Y. Pollet, and S. Taibi. 2013. The GOAL Approach-A Goal-Oriented Algebraic Language. In Proceedings of the 8th International Conference on Evaluation of Novel Approaches to Software Engineering, 173–180.
  • Das, M., and S. Parthasarathy. 2009. Anomaly detection and spatio-temporal analysis of global climate system. In Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data, 142–150. ACM, June.
  • Dasgupta, A. 2013. Big Data: The Future Is in Analytics, Geospatial World. Accessed May 27, 2016. http://www.geospatialworld.net/article/big-data-the-future-is-in-analytics.
  • Dean, J., and S. Ghemawat. 2008. “MapReduce: Simplified Data Processing on Large Clusters.” Communications of the ACM 51 (1): 107–113.
  • Demirkan, H., and D. Delen. 2013. “Leveraging the Capabilities of Service-Oriented Decision Support Systems: Putting Analytics and Big Data in Cloud.” Decision Support Systems 55 (1): 412–421.
  • Denning, D. E., and P. J. Denning. 1979. “Data Security.” ACM Computing Surveys (CSUR) 11 (3): 227–249.
  • Dillon, M. 2015. Big Universe, Big Data, Astronomical Opportunity. Accessed November 25, 2015. http://www.theguardian.com/science/across-the-universe/2015/jun/25/big-universe-big-data-astronomical-opportunity.
  • Ding, J. M., Y. Jiang, Q. X. Wang, Y. L. Liu, and M. J. Li. 2013. “A Data Localization Algorithm for Distributing Column Storage System of Big Data.” Advanced Materials Research 756–759: 3089–3093.
  • Dobre, C., and F. Xhafa. 2014. “Parallel Programming Paradigms and Frameworks in Big Data Era.” International Journal of Parallel Programming 42 (5): 710–738.
  • Dong, X. H., and S. Divesh. 2015. “Big Data Integration.” Synthesis Lectures on Data Management 7 (1): 1–198.
  • Doody, P., and A. Shields. 2012. Mining Network Relationships in the Internet of Things. In Proceedings of the 2012 International Workshop on Self-aware Internet of Things, 7–12. ACM.
  • Du, F., A. Zhu, and F. Qi. 2016. “Interactive Visual Cluster Detection in Large Geospatial Datasets Based on Dynamic Density Volume Visualization.” Geocarto International 31, 597–611.
  • Duan, L., W. N. Street, and E. Xu. 2011. “Healthcare Information Systems: Data Mining Methods in the Creation of a Clinical Recommender System.” Enterprise Information Systems 5 (2): 169–181.
  • Dubey, R., and A. Gunasekaran. 2015. “Education and Training for Successful Career in Big Data and Business Analytics.” Industrial and Commercial Training 47 (4): 174–181.
  • EarthCube. 2014. EarthCube Enterprise Governance Draft Charter. Accessed November10 2015. http://workspace.earthcube.org/.
  • Edlich, S., S. Singh, and I. Pfennigstorf. 2013. Future Mobile Access for Open-Data Platforms and the BBC-DaaS System. In IS&T/SPIE Electronic Imaging, 866710–866710. International Society for Optics and Photonics.
  • Edwards, P. N. (2010). A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming, 518. Cambridge, MA: MIT Press.
  • Eisenstein, M. 2015. “Big Data: The Power of Petabytes.” Nature 527 (7576): S2–S4.
  • Eldawy, A., and M. F. Mokbel. 2013. “A Demonstration of Spatialhadoop: An Efficient Mapreduce Framework for Spatial Data.” Proceedings of the VLDB Endowment 6 (12): 1230–1233.
  • Evangelinos, C., and C. Hill. 2008. “Cloud Computing for Parallel Scientific HPC Applications: Feasibility of Running Coupled Atmosphere-Ocean Climate Models on Amazon’s EC2.” Ratio 2: 2–34.
  • Fan, J., and H. Liu. 2013. “Statistical Analysis of Big Data on Pharmacogenomics.” Advanced Drug Delivery Reviews. 65 (7): 987–1000.
  • Fang, S., L. Da Xu, Y. Zhu, J. Ahati, H. Pei, J. Yan, and Z. Liu. 2014. “An Integrated System for Regional Environmental Monitoring and Management Based on Internet of Things.” IEEE Transactions on Industrial Informatics 10 (2): 1596–1605.
  • Färber, F., S. K. Cha, J. Primsch, C. Bornhövd, S. Sigg, and W. Lehner. 2012. “SAP HANA Database: Data Management for Modern Business Applications.” ACM Sigmod Record 40 (4): 45–51.
  • Feller, E., L. Ramakrishnan, and C. Morin. 2015. “Performance and Energy Efficiency of Big Data Applications in Cloud Environments: A Hadoop Case Study.” Journal of Parallel and Distributed Computing 79–80: 80–89.
  • Feng, W. L., Y. C. Duan, M. X. Huang, L. F. Dong, X. Y. Zhou, and T. Hu. 2014. “A Research on Smart Tourism Service Mechanism Based on Context Awareness.” Applied Mechanics and Materials 519–520: 752–758.
  • Feng, D. G., M. Zhang, Y. Zhang, and Z. Xu. 2011. “Study on Cloud Computing Security.” Journal of software 22 (1): 71–83.
  • Fox, P., and J. Hendler. 2011. “Changing the Equation on Scientific Data Visualization.” Science, Washington 331 (6018): 705–708.
  • Fu, Z., J. Shu, J. Wang, Y. Liu, and S. Lee. 2015. “Privacy-Preserving Smart Similarity Search Based on Simhash over Encrypted Data in Cloud Computing.” Journal of Internet Technology 16 (3): 453–460.
  • Gantz, J., and D. Reinsel. 2011. “Extracting Value from Chaos.” IDC iView 1142 (2011): 1–12.
  • Gantz, J., and D. Reinsel. 2012. “The Digital Universe in 2020: Big Data, Bigger Digital Shadows, and Biggest Growth in the far East.” IDC iView: IDC Analyze the Future 2007: 1–16.
  • Gao, S., L. Li, W. Li, K. Janowicz, and Y. Zhang. 2014. “ Constructing Gazetteers from Volunteered Big Geo-Data Based on Hadoop.” Computers, Environment and Urban Systems. doi:10.1016/j.compenvurbsys.2014.02.004.
  • García, S., J. Luengo, and F. Herrera. 2015. “ Data Preprocessing in Data Mining.” Intelligent Systems Reference Library 72. doi:10.1007/978-3-319-10247-4 (Chapter 6).
  • Geller, G. N., and W. Turner. 2007. The Model Web: A Concept for Ecological Forecasting. In Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE International, 2469–2472. IEEE.
  • Goiri, Í, K. Le, T. D. Nguyen, J. Guitart, J. Torres, and R. Bianchini. 2012. GreenHadoop: Leveraging Green Energy in Data-Processing Frameworks. In Proceedings of the 7th ACM European Conference on Computer Systems, 57–70. ACM. http://www.cs.rutgers.edu/~ricardob/papers/eurosys12.pdf.
  • Gölzer, P., P. Cato, and M. Amberg. 2015. Data Processing Requirements of Industry 4.0-Use Cases for Big Data Applications. Data Processing. Accessed November 25, 2015. http://aisel.aisnet.org/cgi/viewcontent.cgi?article = 1060&context = ecis2015_rip.
  • Goodchild, M. F. 2007. “Citizens as Sensors: The World of Volunteered Geography.” GeoJournal 69 (4): 211–221.
  • Gopalkrishnan, V., D. Steier, H. Lewis, and J. Guszcza. 2012. Big Data, Big Business: Bridging the Gap. In Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications, 7–11. ACM.
  • Griebel, L., H. U. Prokosch, F. Köpcke, D. Toddenroth, J. Christoph, I. Leb, I. Engel, and M. Sedlmayr. 2015. “A Scoping Review of Cloud Computing in Healthcare.” BMC Medical Informatics and Decision Making 15 (1): 1–16.
  • Grimmer, J. 2015. “We Are All Social Scientists Now: How Big Data, Machine Learning, and Causal Inference Work Together.” PS: Political Science & Politics 48 (1): 80–83.
  • Grolinger, K., M. Capretz, E. Mezghani, and E. Exposito. 2013. Knowledge as a Service Framework for Disaster Data Management. In 2013 IEEE 22nd International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), 6570634.
  • Gu, R., X. Yang, J. Yan, Y. Sun, B. Wang, C. Yuan, and Y. Huang. 2014. “SHadoop: Improving MapReduce Performance by Optimizing Job Execution Mechanism in Hadoop Clusters.” Journal of Parallel and Distributed Computing 74 (3): 2166–2179.
  • Gubbi, J., R. Buyya, S. Marusic, and M. Palaniswami. 2013. “Internet of Things (IoT): A Vision, Architectural Elements, and Future Directions.” Future Generation Computer Systems 29 (7): 1645–1660.
  • Gui, Z., C. Yang, J. Xia, Q. Huang, K. Liu, Z. Li, M. Yu, M. Sun, N. Zhou, and B. Jin. 2014. “A Service Brokering and Recommendation Mechanism for Better Selecting Cloud Services.” PLoS ONE 9 (8): e105297.
  • Gui, Z., M. Yu, C. Yang, Y. Jiang, S. Chen, J. Xia, Q. Huang, et al. 2016. “ Developing Subdomain Allocation Algorithms Based on Spatial and Communicational Constraints to Accelerate Dust Storm Simulation.” PloS One 11 (4): e0152250.
  • Hammoud, M., and M. F. Sakr. 2011. Locality-Aware Reduce Task Scheduling for MapReduce. In 2011 IEEE Third International Conference on Cloud Computing Technology and Science (CloudCom), 570–576. IEEE.
  • Han, J., E. Haihong, G. Le, and J. Du. 2011. Survey on NoSQL database. In 2011 6th International Conference on Pervasive Computing and Applications (ICPCA), 363–366. IEEE.
  • Han, Q., S. Liang, and H. Zhang. 2015. “Mobile Cloud Sensing, Big Data, and 5G Networks Make an Intelligent and Smart World.” IEEE Network 29 (2): 40–45.
  • Hartog, J., Z. Fadika, E. Dede, and M. Govindaraju. 2012. Configuring a MapReduce framework for dynamic and efficient energy adaptation. In 2012 IEEE 5th International Conference on Cloud Computing (CLOUD), 914–921. IEEE.
  • Hashem, I. A. T., I. Yaqoob, N. B. Anuar, S. Mokhtar, A. Gani, and S. U. Khan. 2015. “The Rise of “Big Data” on Cloud Computing: Review and Open Research Issues.” Information Systems 47: 98–115.
  • Heffner, J. 2014. “Predictive Policing.” GEO: Geoconnexion Internal 13 (7): 20–23.
  • Hellerstein, J. M. 2010. Datalog Redux: Experience and Conjecture. In Proceedings of the Twenty-Ninth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, 1–2. ACM.
  • Hong, Z., X. Tong, W. Cao, S. Jiang, P. Chen, and S. Liu. 2015. “Rapid Three-Dimensional Detection Approach for Building Damage due to Earthquakes by the use of Parallel Processing of Unmanned Aerial Vehicle Imagery.” Journal of Applied Remote Sensing 9: 097292–097292.
  • Hori, M., E. Kawashima, and T. Yamazaki. 2010. “Application of Cloud Computing to Agriculture and Prospects in Other Fields.” Fujitsu Scientific & Technical Journal 46 (4): 446–454.
  • Hsu, M. H. 2008. “A Personalized English Learning Recommender System for ESL Students.” Expert Systems with Applications 34 (1): 683–688.
  • Hsu, C.-H., K. D. Slagter, and Y.-C. Chung. 2015. “Locality and Loading Aware Virtual Machine Mapping Techniques for Optimizing Communications in MapReduce Applications.” Future Generation Computer Systems 53: 43–54.
  • Huang, Q., G. Cervone, D. Jing, and C. Chang. 2015. DisasterMapper: A CyberGIS Framework for Disaster Management Using Social Media Data. In ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data ACM, Seattle, WA, USA.
  • Huang, M. L., L. F. Lu, and X. Zhang. 2015. “Using Arced Axes in Parallel Coordinates Geometry for High Dimensional BigData Visual Analytics in Cloud Computing.” Computing 97 (4): 425–437.
  • Huang, M., and R. Rust. 2013. “IT-Related Service: A Multidisciplinary Perspective.” Journal of Service Research 16 (3): 251–258.
  • Huang, Q., and D. W. Wong. 2016. “Activity Patterns, Socioeconomic Status and Urban Spatial Structure: What Can Social Media Data Tell Us?” International Journal of Geographical Information Science 30 (9): 1873–1898.
  • Huang, Q., and C. Xu. 2014. “A Data-Driven Framework for Archiving and Exploring Social Media Data.” Annals of GIS 20: 265–277.
  • Huang, Q., and C. Yang. 2011. “Optimizing Grid Computing Configuration and Scheduling for Geospatial Analysis: An Example with Interpolating DEM.” Computers & Geosciences 37 (2): 165–176.
  • Huang, Q., C. Yang, K. Benedict, S. Chen, A. Rezgui, and J. Xie. 2013a. “Utilize Cloud Computing to Support Dust Storm Forecasting.” International Journal of Digital Earth 6: 338–355.
  • Huang, Q., C. Yang, K. Benedict, J. Xie, A. Rezgui, J. Xia, and S. Chen. 2013b. “Using Spatiotemporal Patterns and High end Computing to Enable Dust Storm Forecasting.” International Journal of Geographical Information Science 27 (4): 765–784.
  • Hung, P. P., M. Aazam, and E.-N. Huh. 2015. “CTaG: An Innovative Approach for Optimizing Recovery Time in Cloud Environment.” Transactions on Internet and Information Systems 9 (4): 1282–1301.
  • Hung, P. P., B. Tuan-Anh, and E.-N. Huh. 2013. A Solution of Thin-thick Client Collaboration for data Distribution and Resource Allocation in Cloud Computing. In 2013 International Conference on Information Networking (ICOIN), 238–243. IEEE.
  • Indeck, R. S., and D. M. Indeck. 2012. U.S. Patent No. 8,156,101. Washington, DC: U.S. Patent and Trademark Office.
  • Internet Live Stats. 2016. Accessed 27 September 2016. http://www.internetlivestats.com/internet-users/
  • Ismail, L., and L. Khan. 2015. “Implementation and Performance Evaluation of a Scheduling Algorithm for Divisible Load Parallel Applications in a Cloud Computing Environment.” Software: Practice and Experience 45 (6): 765–781.
  • Jagadish, H. V., J. Gehrke, A. Labrinidis, Y. Papakonstantinou, J. M. Patel, R. Ramakrishnan, and C. Shahabi. 2014. “Big Data and its Technical Challenges.” Communications of the ACM 57 (7): 86–94.
  • Jalali, A., O. A. Olabode, and C. M. Bell. 2012. “Leveraging Cloud Computing to Address Public Health Disparities: An Analysis of the SPHPS.” Online Journal of Public Health Informatics 4 (3: 1–7).
  • Jam, M. R., L. M. Khanli, M. K. Akbari, E. Hormozi, and M. S. Javan. 2013. Survey on Improved Autoscaling in Hadoop into Cloud Environments. In 2013 5th Conference on Information and Knowledge Technology (IKT), 19–23. IEEE.
  • Jayalath, C., J. Stephen, and P. Eugster. 2014. “From the Cloud to the Atmosphere: Running MapReduce Across Data Centers.” IEEE Transactions on Computers 63 (1): 74–87.
  • Ji, C., Y. Li, W. Qiu, U. Awada, and K. Li. 2012. Big Data Processing in Cloud Computing Environments. In 2012 12th International Symposium on Pervasive Systems, Algorithms and Networks (ISPAN), 17–23. IEEE.
  • Jiang, H., Y. Chen, Z. Qiao, T. H. Weng, and K. C. Li. 2015. “Scaling up Mapreduce-Based Big Data Processing on Multi-GPU Systems.” Cluster Computing 18 (1): 369–383.
  • Jiang, S., L. Fang, and X. Huang. 2009. An Idea of Special Cloud Computing in Forest Pests’ Control. In Cloud Computing, 615–620. Springer Berlin Heidelberg.
  • Jiang, Y., Y. Li, C. Yang, E. M. Armstrong, T. Huang, and D. Moroni. 2016. “Reconstructing Sessions From Data Discovery and Access Logs to Build a Semantic Knowledge Base for Improving Data Discovery.” ISPRS International Journal of Geo-Information 5 (4): 54–68.
  • Jin, J., J. Gubbi, S. Marusic, and M. Palaniswami. 2014. “An Information Framework for Creating a Smart City Through Internet of Things.” IEEE Internet of Things Journal 1 (2): 112–121.
  • Joo, H., B. Hong, and S. Kim. 2012. Smart-Contents Visualization of Publishing Big Data Using NFC Technology. In Computer Applications for Graphics, Grid Computing, and Industrial Environment, 118–123. Springer.
  • Kagermann, H., J. Helbig, A. Hellinger, and W. Wahlster. 2013. Recommendations for Implementing the Strategic Initiative INDUSTRIE 4.0: Securing the Future of German Manufacturing Industry. Final Report of the Industrie 4.0 Working Group. Forschungsunion.
  • Kaisler, S., F. Armour, J. A. Espinosa, and W. Money. 2013. Big Data: Issues and Challenges Moving Forward. In 2013 46th Hawaii International Conference on System Sciences (HICSS), 995–1004.
  • Karimi, H. A. ed. 2014. Big Data: Techniques and Technologies in Geoinformatics. Boca Raton, FL: CRC Press.
  • Kaufman, L. M. 2009. “Data Security in the World of Cloud Computing.” IEEE Security & Privacy Magazine 7 (4): 61–64.
  • Kaushik, R. T., and M. Bhandarkar. 2010. Greenhdfs: Towards an Energy-Conserving, Storage-Efficient, Hybrid Hadoop Compute Cluster. In Proceedings of the USENIX Annual Technical Conference, 109.
  • Kaushik, R. T., M. Bhandarkar, and K. Nahrstedt. 2010. Evaluation and Analysis of Greenhdfs: A self-adaptive, Energy-Conserving Variant of the Hadoop Distributed File System. In 2010 IEEE Second International Conference on Cloud Computing Technology and Science (CloudCom), 274–287.
  • Kehoe, B., S. Patil, P. Abbeel, and K. Goldberg. 2015. “A Survey of Research on Cloud Robotics and Automation.” IEEE Transactions on Automation Science and Engineering 12 (2): 398–409.
  • Khan, N., I. Yaqoob, I. A. T. Hashem, Z. Inayat, W. K. Mahmoud Ali, M. Alam, M. Shiraz, and A. Gani. 2014. “Big Data: Survey, Technologies, Opportunities, and Challenges.” The Scientific World Journal 2014: 1–18.
  • Kim, C. 2014. “Theoretical Analysis of Constructing Wavelet Synopsis on Partitioned Data Sets.” Multimedia Tools and Applications 74 (7): 2417–2432.
  • Kim, S., D. Kang, J. Choi, and J. Kim. 2014. Burstiness-aware I/O scheduler for MapReduce framework on virtualized environments. In 2014 International Conference on Big Data and Smart Computing (BIGCOMP), 305–308.
  • Kim, W., H. Kim, and Y. Kim. 2013. “DataConnector: A Data Processing Framework Integrating Hadoop and aGrid Middleware OGSA-DAI for Cloud Environment.” Information – An International Interdisciplinary Journal. 16 (1B): 801–806.
  • Kim, G. H., S. Trimi, and J. H. Chung. 2014. “Big-data Applications in the Government Sector.” Communications of the ACM 57 (3): 78–85.
  • Kim, I.-H., M.-H. Tsou, and C.-C. Feng. 2015. “Design and Implementation Strategy of a Parallel Agent-Based Schelling Model.” Computers, Environment and Urban Systems 49, 30–41.
  • Knoppers, B. M., and R. Chadwick. 2014. “Human Genetic Research: Emerging Trends in Ethics.” Focus 4 (3): 416–422.
  • Kos, A., S. Tomažič, J. Salom, N. Trifunovic, M. Valero, and V. Milutinovic. 2015. “New Benchmarking Methodology and Programming Model for Big Data Processing.” International Journal of Distributed Sensor Networks 501: 1–7.
  • Kourtesis, D., J. M. Alvarez-Rodríguez, and I. Paraskakis. 2014. “Semantic-based QoS Management in Cloud Systems: Current Status and Future Challenges.” Future Generation Computer Systems 32: 307–323.
  • Kozuch, M. A., M. P. Ryan, R. Gass, S. W. Schlosser, D. O’Hallaron, J. Cipar, E. Krevat, J. López, M. Stroucken and G. R. Ganger. 2009. Tashi: Location-aware Cluster Management. In Proceedings of the 1st Workshop on Automated Control for Datacenters and Clouds, 43–48. ACM.
  • Krämer, M., and I. Senner. 2015. “A Modular Software Architecture for Processing of big Geospatial Data in the Cloud.” Computers & Graphics 49: 69–81.
  • Krogh, B. H. 2008. Cyber Physical Systems: The Need for New Models and Design Paradigms. Presentation Report. Accessed November 30, 2015. http://slideplayer.com/slide/4807731/.
  • Ku, M., E. Choi, and D. Min. 2014. “An Analysis of Performance Factors on Esper-Based Stream big Data Processing in a Virtualized Environment.” International Journal of Communication Systems 27 (6): 898–917.
  • Lampos, V., and N. Cristianini. 2010. Tracking the Flu Pandemic by Monitoring the Social Web. In 2010 2nd International Workshop on Cognitive Information Processing (CIP), 411–416. IEEE.
  • Lary, D. J., S. Woolf, F. Faruque, and J. P. LePage. 2014. “Holistics 3.0 for Health.” ISPRS International Journal of Geo-Information 3: 1023–1038.
  • LaValle, S., E. Lesser, R. Shockley, M. S. Hopkins, and N. Kruschwitz. 2013. Big data, Analytics and the Path from Insights to Value. MIT Sloan Management Review, 21. Accessed December 2, 2015. http://sloanreview.mit.edu/article/big-data-analytics-and-the-path-from-insights-to-value/.
  • Lee, S., H. Park, and Y. Shin. 2012. “Cloud Computing Availability: Multi-Clouds for big Data Service.” Convergence and Hybrid Information Technology 310: 799–806.
  • Li, Z. 2015. Optimizing Geospatial Cyberinfrastructure to Improve the Computing Capability for Climate Studies. Accessed November 25, 2015. http://digilib.gmu.edu/jspui/bitstream/handle/1920/9630/Li_gmu_0883E_10873.pdf?sequence = 1&isAllowed = y.
  • Li, Z., F. Hu, J. Schnase, D. Duffy, T. Lee, C. Yang, and M. Bowen. 2016a. “A Spatiotemporal Indexing Approach for Efficient Process of Big Array-Based Climate Data with MapReduce.” International Journal of Geographic Information Science. doi:10.1080/13658816.2015.1131830.
  • Li, W., M. Song, B. Zhou, K. Cao, and S. Gao. 2015a. “Performance Improvement Techniques for Geospatial web Services in a Cyberinfrastructure Environment–A Case Study with a Disaster Management Portal.” Computers, Environment and Urban Systems 54: 314–325.
  • Li, Z., C. Yang, Q. Huang, K. Liu, M. Sun, and J. Xia. 2014. “Building Model as a Service for Supporting Geosciences.” Computers, Environment and Urban Systems. doi:10.1016/j.compenvurbsys.2014.06.004.
  • Li, Z., C. Yang, K. Liu, H. Fei, and B. Jin. 2016b. “Automatic Scaling Hadoop in the Cloud for Efficient Process of Big Geospatial Data.” ISPRS International Journal of Geo-Information 5 (10): 173. doi:10.3390/ijgi5100173.
  • Li, Z., C. Yang, H. Wu, W. Li, and L. Miao. 2011. “An Optimized Framework for Seamlessly Integrating OGC Web Services to Support Geospatial Sciences.” International Journal of Geographical Information Science 25 (4): 595–613.
  • Li, Z., C. Yang, M. Yu, K. Liu, and M. Sun. 2015b. “Enabling Big Geoscience Data Analytics with a Cloud-Based, MapReduce-Enabled and Service-Oriented Workflow Framework.” PloS one 10 (3): e0116781.
  • Lim, N., Å Grönlund, and A. Andersson. 2015. “Cloud Computing: The Beliefs and Perceptions of Swedish School Principals.” Computers & Education 84: 90–100.
  • Lin, F. C., L. K. Chung, W. Y. Ku, L. R. Chu, and T. Y. Chou. 2013. The Framework of Cloud Computing Platform for Massive Remote Sensing Images. In 2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA), 621–628. IEEE.
  • Liu, L. 2013. “Computing Infrastructure for big Data Processing.” Frontiers of Computer Science 7 (2): 165–170.
  • Liu, X. J. 2014. “Research of Big Data Processing Platform.” In Applied Mechanics and Materials 484: 922–926.
  • Liu, Z. Z., Z. P. Jia, X. Xue, and J. Y. An. 2015. “Reliable Web Service Composition Based on QoS Dynamic Prediction.” Soft Computing 19 (5): 1409–1425.
  • Liu, X., C. Kang, L. Gong, and Y. Liu. 2016. “Incorporating Spatial Interaction Patterns in Classifying and Understanding Urban Land use.” International Journal of Geographical Information Science 30, 334–350.
  • Liu, K., C. Yang, W. Li, Z. Gui, C. Xu, and J. Xia. 2014. “Using Semantic Search and Knowledge Reasoning to Improve the Discovery of Earth Science Records: An Example with the ESIP Semantic Testbed.” International Journal of Applied Geospatial Research (IJAGR) 5 (2): 44–58.
  • Liu, K., C. Yang, W. Li, Z. Li, H. Wu, A. Rezgui, and J. Xia. 2011. The GEOSS Clearinghouse High Performance Search Engine. In 2011 19th International Conference on Geoinformatics, 1–4.
  • Lohr, S. 2012. The age of big data. New York Times, 11.
  • Lorido-Botrán, T., J. Miguel-Alonso, and J. A. Lozano. 2012. Auto-scaling Techniques for Elastic Applications in Cloud Environments. Department of Computer Architecture and Technology, University of Basque Country, Tech. Rep. EHU-KAT-IK-09, 12, 2012.
  • Luo, C., J. Zhan, Z. Jia, L. Wang, G. Lu, L. Zhang, C.-Z. Xu, and N. Sun. 2012. “Cloudrank-d: Benchmarking and Ranking Cloud Computing Systems for Data Processing Applications.” Frontiers of Computer Science 6 (4): 347–362.
  • Lushbough, C. M., E. Z. Gnimpieba, and R. Dooley. 2015. “Life Science Data Analysis Workflow Development Using the Bioextract Server Leveraging the IPlant Collaborative Cyberinfrastructure.” Concurrency and Computation: Practice and Experience 27 (2): 408–419.
  • Ma, X., Y. Wang, X. Pei, and F. Xu. 2015. “Scalable and Elastic Total Order in Content-Based Publish/Subscribe Systems.” Computer Networks 83: 297–314. doi:10.1016/j.comnet.2015.04.001.
  • Manuel, P. 2013. “A Trust Model of Cloud Computing Based on Quality of Service.” Annals of Operations Research 233: 1–12.
  • Manyika, J., M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh, and A. H. Byers. 2011. Big data: The Next Frontier for Innovation, Competition, and Productivity. Accessed November 25, 2015. http://www.citeulike.org/group/18242/article/9341321.
  • Mao, M., and M. Humphrey. 2011. Auto-scaling to Minimize Cost and Meet Application Deadlines in Cloud Workflows. In Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis, 49. ACM.
  • Marr, B. 2015. Big Data: Using SMART Big Data. Analytics and Metrics To Make Better Decisions and Improve Performance. Atrium: Wiley.
  • Massey, N., R. Jones, F. E. L. Otto, T. Aina, S. Wilson, J. M. Murphy, D. Hassell, Y. H. Yamazaki, and M. R. Allen. 2014. “weather@home-development and Validation of a Very Large Ensemble Modelling System for Probabilistic Event Attribution.” Quarterly Journal of the Royal Meteorological Society 141 (690): 1528–1545.
  • Mayer-Schönberger, V., and K. Cukier. 2013. Big Data: A Revolution That Will Transform How We Live, Work, and Think. Houghton Mifflin Harcourt. (Chapter 1).
  • McAfee, A., E. Brynjolfsson, T. H. Davenport, D. J. Patil, and D. Barton. 2012. “Big Data. The Management Revolution.” Harvard Business Review 90 (10): 61–67.
  • Megler, V. M., and D. Maier. 2012. When Big Data Leads to Lost Data. In Proceedings of the 5th Ph. D. Workshop on Information and Knowledge, 1–8. ACM.
  • Mell, P. M., and T. Grance. 2011. The NIST Definition of Cloud Computing. Special Publication 800-145, National Institute of Standards and Technology, Gaithersburg, MD. doi:10.6028/nist.sp.800-145.
  • Michael, K., and K. Miller. 2013. “Big Data: New Opportunities and New Challenges [Guest Editors” Introduction].” Computer 46 (6): 22–24.
  • Mitton, N., S. Papavassiliou, A. Puliafito, and K. S. Trivedi. 2012. “Combining Cloud and Sensors in a Smart City Environment.” EURASIP journal on Wireless Communications and Networking 2012 (1): 247–10.
  • Miyano, T., and M. Uehara. 2012. Proposal for Cloud Search Engine as a Service. In 2012 15th International Conference on Network-Based Information Systems (NBiS), 627–632. IEEE.
  • Moniruzzaman, A. B. M., and S. A. Hossain. 2013. “Nosql Database: New era of Databases for big Data Analytics-Classification, Characteristics and Comparison.” International Journal of Database Theory and Application. 6 (4): 1–14.
  • Moore, R., T. A. Prince, and M. Ellisman. 1998. “Data-intensive Computing and Digital Libraries.” Communications of the ACM 41 (11): 56–62.
  • Nadeem, A., and M. Y. Javed. 2005. A Performance Comparison of Data Encryption Algorithms. In ICICT 2005. First International Conference on Information and Communication Technologies, 2005, 84–89.
  • Najjar, M. S., and W. J. Kettinger. 2013. “Data Monetization: Lessons from a Retailer’s Journey.” MIS Quarterly Executive 12 (4): 1–13.
  • Nambiar, R., R. Chitor, and A. Joshi. 2014. Data Management – A Look Back and a Look Ahead, Specifying Big Data Benchmarks. Toronto: Springer.
  • Nasser, T., and R. S. Tariq. 2015. “Big Data Challenges.” Journal of Computer Engineering & Information Technology 4 (3): 1–10.
  • Nativi, S., P. Mazzetti, M. Santoro, F. Papeschi, M. Craglia, and O. Ochiai. 2015. “Big Data Challenges in Building the Global Earth Observation System of Systems.” Environmental Modelling & Software 68, 1–26.
  • Nazir, A., Y. M. Yassin, C. P. Kit, and E. K. Karuppiah. 2012. Evaluation of Virtual Machine Scalability on Distributed Multi/many-core Processors for Big Data Analytics. In 2012 IEEE Conference on Open Systems (ICOS), 1–6. IEEE.
  • Nguyen, P., and M. Halem. 2011. A Mapreduce Workflow System for Architecting Scientific Data Intensive Applications. In Proceedings of the 2nd International Workshop on Software Engineering for Cloud Computing, 57–63. ACM.
  • Nita, M. C., C. Chilipirea, C. Dobre, and F. Pop. 2013. A SLA-based Method for Big-data Transfers with Multi-criteria Optimization Constraints for IaaS. In 2013 11th Roedunet International Conference (RoEduNet), 1–6.
  • O’Donovan, P., K. Leahy, K. Bruton, and D. T. O’Sullivan. 2015. “Big Data in Manufacturing: A systematic Mapping Study.” Journal of Big Data 2 (1): 1–22.
  • O’Driscoll, A., V. Belogrudov, J. Carroll, K. Kropp, P. Walsh, P. Ghazal, and R. D. Sleator. 2015. “HBLAST: Parallelised Sequence Similarity–A Hadoop MapReducable Basic Local Alignment Search Tool.” Journal of Biomedical Informatics 54: 58–64.
  • Oguntimilehin, A., and E. O. Ademola. 2014. “A Review of Big Data Management, Benefits and Challenges.” A Review of Big Data Management, Benefits and Challenges 5 (6): 433–438.
  • Padgavankar, M. H., and S. R. Gupta. 2014. “Big Data Storage and Challenges.” (IJCSIT) International Journal of Computer Science and Information Technologies 5 (2): 2218–2223.
  • Padhy, R. P., M. R. Patra, and S. C. Satapathy. 2011. “RDBMS to NoSQL: Reviewing Some Next-Generation non-Relational Databases.” International Journal of Advanced Engineering Science and Technologies 11 (1): 15–30.
  • Padmanabhan, A., S. Wang, G. Cao, M. Hwang, Y. Zhao, Z. Zhang, and Y. Gao. 2013. FluMapper: An interactive CyberGIS Environment for Massive Location-Based Social Media Data Analysis. In Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery, 33. ACM.
  • Pandey, S., and S. Nepal. 2013. “Cloud Computing and Scientific Applications – Big Data, Scalable Analytics, and Beyond.” Future Generation Computer Systems 29: 1774–1776.
  • Paquette, J., and T. Tokuyasu. 2011. Hypergraph Visualization and Enrichment Statistics: How the EGAN Paradigm Facilitates Organic Discovery from Big Data. In IS&T/SPIE Electronic Imaging, 78650E–78650E. International Society for Optics and Photonics.
  • Perera, C., R. Ranjan, L. Wang, S. U. Khan, and A. Y. Zomaya. 2015. “Big Data Privacy in the Internet of Things Era.” IT Professional 17 (3): 32–39.
  • Pijanowski, B. C., A. Tayyebi, J. Doucette, B. K. Pekin, D. Braun, and J. Plourde. 2014. “A big Data Urban Growth Simulation at a National Scale: Configuring the GIS and Neural Network Based Land Transformation Model to run in a High Performance Computing (HPC) Environment.” Environmental Modelling & Software 51, 250–268.
  • Pokorny, J. 2013. “NoSQL Databases: A Step to Database Scalability in web Environment.” International Journal of Web Information Systems 9 (1): 69–82.
  • Pop, F., C. Dobre, V. Cristea, N. Bessis, F. Xhafa, and L. Barolli. 2015. “Deadline Scheduling for Aperiodic Tasks in Inter-Cloud Environments: A new Approach to Resource Management.” The Journal of Supercomputing 71 (5): 1754–1765.
  • Pumma, S., T. Achalakul, and X. Li. 2012. Automatic VM Allocation for Scientific Application. In 2012 IEEE 18th International Conference on Parallel and Distributed Systems (ICPADS), 828–833. IEEE.
  • Radke, A. M., and M. M. Tseng. 2015. “Design Considerations for Building Distributed Supply Chain Management Systems Based on Cloud Computing.” Journal of Manufacturing Science and Engineering 137 (4): 1–7.
  • Ramapriyan, H. K. 2015. The Role and Evolution of NASA’s Earth Science Data Systems. Accessed November 25, 2015. http://ntrs.nasa.gov/search.jsp?R = 20150018076.
  • Rasmussen, A., M. Conley, G. Porter, R. Kapoor, and A. Vahdat. 2012. Themis: An I/O-efficient MapReduce. In Proceedings of the Third ACM Symposium on Cloud Computing, 13. ACM.
  • Reda, K., A. Febretti, A. Knoll, J. Aurisano, J. Leigh, A. Johnson, M. E. Papka, and M. Hereld. 2013. “Visualizing Large, Heterogeneous Data in Hybrid-Reality Environments.” IEEE Computer Graphics and Applications 33(4): 38–48.
  • Redlich, R. M., and M. A. Nemzow. 2006. U.S. Patent No. 7,103,915. Washington, DC: U.S. Patent and Trademark Office.
  • Robinson, S. 2012. The Storage and Transfer Challenges of Big Data. Accessed November 25, 2015. http://sloanreview.mit.edu/article/the-storage-and-transfer-challenges-of-big-data/.
  • Röme, T. 2010. “ Autoscaling Hadoop Clusters.” MSc thesis, University of Tartu. Accessed November 25, 2015. http://lepo.it.da.ut.ee/~srirama/publications/theses/AutoscaleHadoop_Toomas.pdf.
  • Sagl, G., M. Loidl, and E. Beinat. 2012. “A Visual Analytics Approach for Extracting Spatio-Temporal Urban Mobility Information from Mobile Network Traffic.” ISPRS International Journal of Geo-Information 1: 256–271.
  • Sainio, J., J. Westerholm, and J. Oksanen. 2015. “Generating Heat Maps of Popular Routes Online from Massive Mobile Sports Tracking Application Data in Milliseconds While Respecting Privacy.” ISPRS International Journal of Geo-Information 4: 1813–1826.
  • Sandhu, R., and S. K. Sood. 2015a. “A Commercial, Benefit Driven and Secure Framework for Elearning in Cloud Computing.” Computer Applications in Engineering Education 23 (4): 499–513.
  • Sandhu, R., and S. K. Sood. 2015b. “Scheduling of Big Data Applications on Distributed Cloud Based on QoS Parameters.” Cluster Computing 18 (2): 817–828.
  • SAS. 2012. Data Visualization: Making Big Data Approachable and Valuable. White Paper. A Survey on Information Visualization: Recent Advances and Challenges. Accessed November 25, 2015. https://www.sas.com/content/dam/SAS/en_us/doc/whitepaper2/sas-data-visualization-marketpulse-106176.pdf.
  • Schaffers, H., N. Komninos, M. Pallot, B. Trousse, M. Nilsson, and A. Oliveira. 2011. “Smart Cities and the Future Internet: Towards Cooperation Frameworks for Open Innovation.” Future Internet Assembly 6656: 431–446.
  • Schlieski, T., and B. D. Johnson. 2012. “Entertainment in the age of big Data.” Proceedings of the IEEE 100: 1404–1408.
  • Schnase, J. L., D. Q. Duffy, G. S. Tamkin, D. Nadeau, J. H. Thompson, et al. 2014. “MERRA Analytic Services: Meeting the big Data Challenges of Climate Science Through Cloud-Enabled Climate Analytics-as-A-Service.” Computers, Environment and Urban Systems. doi:10.1016/j.compenvurbsys.2013.12.003.
  • Sequeira, H., P. Carreira, T. Goldschmidt, and P. Vorst. 2014. Energy Cloud: Real-time Cloud-Native Energy Management System to Monitor and Analyze Energy Consumption in Multiple Industrial Sites. In Proceedings of the 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing, 529–534. IEEE Computer Society.
  • Sfrent, A., and F. Pop. 2015. “Asymptotic Scheduling for Many Task Computing in Big Data Platforms.” Information Sciences 319: 71–91.
  • Shekhar, S., V. Gunturi, M. R. Evans, and K. Yang. 2012. Spatial Big-data Challenges Intersecting Mobility and Cloud Computing. In Proceedings of the Eleventh ACM International Workshop on Data Engineering for Wireless and Mobile Access, 1–6. ACM.
  • Shelton, T., A. Poorthuis, and M. Zook. 2015. “Social Media and the City: Rethinking Urban Socio-Spatial Inequality Using User-Generated Geographic Information.” Landscape and Urban Planning 142: 198–211.
  • Shen, Z., S. Subbiah, X. Gu, and J. Wilkes. 2011. Cloudscale: Elastic Resource Scaling for Multi-tenant Cloud Systems. In Proceedings of the 2nd ACM Symposium on Cloud Computing, 5. ACM.
  • Shook, E., M. E. Hodgson, S. Wang, B. Behzad, K. Soltani, A. Hiscox, and J. Ajayakumar. 2016. “Parallel Cartographic Modeling: A Methodology for Parallelizing Spatial Data Processing.” International Journal of Geographical Information Science 30: 2355–2376.
  • Shvachko, K., H. Kuang, S. Radia, and R. Chansler. 2010. The Hadoop Distributed File System. In 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST), 1–10. IEEE.
  • Singh, G., S. Bharathi, A. Chervenak, E. Deelman, C. Kesselman, M. Manohar, S. Patil, and L. Pearlman. 2003. A Metadata Catalog Service for Data Intensive Applications. In Supercomputing, 2003 ACM/IEEE Conference, 33–33. IEEE.
  • Slagter, K., C. H. Hsu, and Y. C. Chung. 2015. “An Adaptive and Memory Efficient Sampling Mechanism for Partitioning in MapReduce.” International Journal of Parallel Programming 43 (3): 489–507.
  • Slagter, K., C.-H. Hsu, Y.-C. Chung, and D. Zhang. 2013. “An Improved Partitioning Mechanism for Optimizing Massive Data Analysis Using MapReduce.” The Journal of Supercomputing 66 (1): 539–555.
  • Smid, M. E., and D. K. Branstad. 1988. “Data Encryption Standard: Past and Future.” Proceedings of the IEEE 76 (5): 550–559.
  • Somasundaram, T. S., K. Govindarajan, V. Venkateswaran, R. Radhika, and V. Venkatesh. 2012. CDM Server: A Data Management Framework for Data Intensive Application in Internal Private Cloud Infrastructure. In 2012 Seventh International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), 211–217. IEEE.
  • Soyata, T., R. Muraleedharan, J. Langdon, C. Funai, S. Ames, M. Kwon, and W. Heinzelman. 2012. COMBAT: Mobile-Cloud-based cOmpute/coMmunications Infrastructure for BATtlefield applications. In SPIE Defense, Security, and Sensing, 84030K–84030K. International Society for Optics and Photonics.
  • Sultan, N. 2010. “Cloud Computing for Education: A new Dawn?” International Journal of Information Management 30 (2): 109–116.
  • Sun, D. W., G. R. Chang, D. Chen, and X. W. Wang. 2014. “Profiling, Quantifying, Modeling and Evaluating Green Service Level Objectives in Cloud Computing Environments.” Chinese Journal Of Computers 36 (7): 1509–1525.
  • Sun, M., J. Li, C. Yang, G. A. Schmidt, M. Bambacus, R. Cahalan, Q. Huang, C. Xu, E. U. Noble, and Z. Li. 2012. “A Web-Based Geovisual Analytical System for Climate Studies.” Future Internet 4: 1069–1085.
  • Sun, D., G. Zhang, S. Yang, W. Zheng, S. U. Khan, and K. Li. 2015. “Re-Stream: Real-Time and Energy-Efficient Resource Scheduling in big Data Stream Computing Environments.” Information Sciences 319: 92–112. doi:10.1016/j.ins.2015.03.027.
  • Tablan, V., I. Roberts, H. Cunningham, and K. Bontcheva. 2013. “GATECloud.net: A Platform for Large-Scale, Open-Source Text Processing on the Cloud.” Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences 371 (1983): 1–13.
  • Tamura, Y., and S. Yamada. 2015. “Reliability Analysis Based on a Jump Diffusion Model with Two Wiener Processes for Cloud Computing with Big Data.” Entropy 17 (7): 4533–4546.
  • Tang, Z., L. Jiang, J. Zhou, K. Li, and K. Li. 2015. “A Self-Adaptive Scheduling Algorithm for Reduce Start Time.” Future Generation Computer Systems 43–44: 51–60.
  • Tene, O. 2011. “Privacy: The new Generations.” International Data Privacy Law 1 (1): 15–27.
  • Terry, N. 2012. Protecting Patient Privacy in the Age of Big Data. Accessed November 25, 2015. http://papers.ssrn.com/sol3/papers.cfm?abstract_id = 2153269.
  • Terry, D. B., V. Prabhakaran, R. Kotla, M. Balakrishnan, M. K. Aguilera, and H. Abu-Libdeh. 2013. Consistency-Based Service Level Agreements for Cloud Storage. In Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles, 309–324. ACM.
  • Theodoridis, E., G. Mylonas, and I. Chatzigiannakis. 2013. Developing an IoT Smart City framework. In Information, Intelligence, Systems and Applications (IISA) 2013. doi:10.1109/iisa.2013.6623710.
  • The Whitehouse. 2014. Harnessing Observations and Data about Our Earth. Accessed November 25, 2015. https://www.whitehouse.gov/blog/2014/07/18/harnessing-observations-and-data-about-our-earth.
  • Tien, J. M. 2013. “Big Data: Unleashing Information.” Journal of Systems Science and Systems Engineering 22 (2): 127–151.
  • Toole, J. L., S. Colak, B. Sturt, L. P. Alexander, A. Evsukoff, and M. C. González. 2015. “The Path Most Traveled: Travel Demand Estimation Using big Data Resources.” Transportation Research Part C: Emerging Technologies 58: 162–177.
  • Triguero, I., D. Peralta, J. Bacardit, S. García, and F. Herrera. 2015. “MRPR: A MapReduce Solution for Prototype Reduction in big Data Classification.” Neurocomputing 150: 331–345.
  • Van den Dam, R. 2013. Internet of Things: The Foundational Infrastructure for a Smarter Planet. In Internet of Things, Smart Spaces, and Next Generation Networking, 1–12. Springer Berlin Heidelberg. (Chapter 1).
  • Van Der Aalst, W. M., A. H. Ter Hofstede, B. Kiepuszewski, and A. P. Barros. 2003. “Workflow Patterns.” Distributed and Parallel Databases 14 (1): 5–51.
  • Van Der Aalst, W., and K. M. Van Hee. 2004. Workflow Management: Models, Methods, and Systems. Cambridge, MA: MIT press.
  • Vasile, M. A., F. Pop, R. I. Tutueanu, V. Cristea, and J. Kołodziej. 2014. “Resource-aware Hybrid Scheduling Algorithm in Heterogeneous Distributed Computing.” Future Generation Computer Systems 51: 61–71. doi:10.1016/j.future.2014.11.019.
  • Vatsavai, R. R., A. Ganguly, V. Chandola, A. Stefanidis, S. Klasky, and S. Shekhar. 2012. Spatiotemporal Data Mining in the Era of Big Spatial Data: Algorithms and Applications. In Proceedings of the 1st ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, 1–10. ACM.
  • Villars, R. L., C. W. Olofson, and M. Eastwood. 2011. Big Data: What It Is and Why You Should Care. White Paper, IDC.
  • Wang, M., S. B. Handurukande, and M. Nassar. 2012. RPig: A Scalable Framework for Machine Learning and Advanced Statistical Functionalities. In IEEE 4th International Conference on Cloud Computing Technology and Science (CloudCom), December 2012, 293–300. IEEE.
  • Wang, Y., Z. Liu, H. Liao, and C. Li. 2015. “Improving the Performance of GIS Polygon Overlay Computation with MapReduce for Spatial big Data Processing.” Cluster Computing 18: 507–516.
  • Wang, Y., and X. Ma. 2015. “A General Scalable and Elastic Content-Based Publish/Subscribe Service.” IEEE Transactions on Parallel and Distributed Systems 26 (8): 2100–2113.
  • Wang, Q., C. Wang, J. Li, K. Ren, and W. Lou. 2009. Enabling Public Verifiability and Data Dynamics for Storage Security in Cloud Computing. In Computer Security–ESORICS 2009, 355–370. Springer Berlin Heidelberg.
  • Whitman, R. T., M. B. Park, S. M. Ambrose, and E. G. Hoel. 2014. Spatial Indexing and Analytics on Hadoop. In Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 73–82. ACM.
  • Witayangkurn, A., T. Horanont, and R. Shibasaki. 2013. “ The Design of Large Scale Data Management for Spatial Analysis on Mobile Phone Dataset.” Asian Journal of Geoinformatics 13 (3). http://a-a-r-s.org/acrs/administrator/components/com_jresearch/files/publications/C4-3.pdf.
  • Wright, D. J., and S. Wang. 2011. “The Emergence of Spatial Cyberinfrastructure.” Proceedings of the National Academy of Sciences 108 (14): 5488–5491.
  • Wu, K., L. Chen, and Y. Li. 2015. “A Trusted-Based Cloud Computing Virtual Storage System and Key Technologies.” International Journal of Computers Communications & Control 10 (4): 579–592.
  • Wu, H., Z. Li, H. Zhang, C. Yan, and S. Shen. 2011. “Monitoring and Evaluating the Quality of Web Map Service Resources for Optimizing map Composition over the Internet to Support Decision Making.” Computers & Geosciences 37 (4): 485–494.
  • Xia, J., C. Yang, Z. Gui, K. Liu, and Z. Li. 2014. “Optimizing an Index with Spatiotemporal Patterns to Support GEOSS Clearinghouse.” International Journal of Geographical Information Science 28 (7): 1459–1481.
  • Xia, J., C. Yang, K. Liu, Z. Li, M. Sun, and M. Yu. 2015a. “Forming a Global Monitoring Mechanism and a Spatiotemporal Performance Model for Geospatial Services.” International Journal of Geographical Information Science 29 (3): 375–396.
  • Xia, J., C. Yang, K. Liu, Z. Gui, Z. Li, Q. Huang, and R. Li. 2015b. “Adopting Cloud Computing to Optimize Spatial web Portals for Better Performance to Support Digital Earth and Other Global Geospatial Initiatives.” International Journal of Digital Earth 8: 451–475.
  • Xie, J., C. Yang, B. Zhou, and Q. Huang. 2010. “High-performance Computing for the Simulation of Dust Storms.” Computers, Environment and Urban Systems 34 (4): 278–290.
  • Xing, F. X., J. Pang, and B. Q. Zhang. 2010. “Application About the Objects Internet Technology in the Modern Agricultural Production.” Agricultural Technology& Equipment 8: 16–17.
  • Xing, J., and R. E. Sieber. 2016. “A Land Use/Land Cover Change Geospatial Cyberinfrastructure to Integrate Big Data and Temporal Topology.” International Journal of Geographical Information Science 30: 573–593.
  • Xu, Z. 2012. “How Much Power Is Needed for a Billion-Thread High-Throughput Server?” Frontiers of Computer Science 6 (4): 339–346.
  • Xu, J., E. Huang, C. H. Chen, and L. H. Lee. 2015. “Simulation Optimization: A Review and Exploration in the New Era of Cloud Computing and Big Data.” Asia-Pacific Journal of Operational Research 32: 1550019-1-34.
  • Yang, C. 2011. Thinking and computing spatiotemporally to enable cloud computing and science discoveries. In 19th International Conference on Geoinformatics, June 2011, 1–6.
  • Yang, C., M. Goodchild, Q. Huang, D. Nebert, R. Raskin, Y. Xu, M. Bambacus, and D. Fay. 2011a. “Spatial Cloud Computing: How Can the Geospatial Sciences Use and Help Shape Cloud Computing?” International Journal of Digital Earth 4 (4): 305–329.
  • Yang, C., Q. Huang, Z. Gui, Z. Li, C. Xu, Y. Jiang, and J. Li. 2013. “Cloud Computing Research for Geosciences.” In Spatial Cloud Computing: A Practical Approach, edited by C. Yang, Q. Huang, Z. Li, C. Xu, and K. Liu, 295–310. Boca Raton, FL: CRC Press/Taylor & Francis.
  • Yang, C., C. Liu, X. Zhang, S. Nepal, and J. Chen. 2015a. “A Time Efficient Approach for Detecting Errors in big Sensor Data on Cloud.” IEEE Transactions on Parallel and Distributed Systems 26 (2): 329–339.
  • Yang, Y., X. Long, and B. Jiang. 2013. “K-Means Method for Grouping in Hybrid MapReduce Cluster.” Journal of Computers 8 (10): 2648–2655.
  • Yang, C., R. Raskin, M. Goodchild, and M. Gahegan. 2010. “Geospatial Cyberinfrastructure: Past, Present and Future.” Computers, Environment and Urban Systems 34 (4): 264–277.
  • Yang, C., M. Sun, K. Liu, Q. Huang, Z. Li, Z. Gui, Y. Jiang, et al. 2015b. “Contemporary computing technologies for processing big spatiotemporal data.” In Space-Time Integration in Geography and GIScience, 327–351. Springer Netherlands.
  • Yang, C., H. Wu, Q. Huang, Z. Li, and J. Li. 2011b. “Using Spatial Principles to Optimize Distributed Computing for Enabling the Physical Science Discoveries.” Proceedings of the National Academy of Sciences 108 (14): 5498–5503.
  • Yang, C., Y. Xu, and D. Nebert. 2013. “Redefining the Possibility of Digital Earth and Geosciences with Spatial Cloud Computing.” International Journal of Digital Earth 6 (4): 297–312.
  • Yang, C., X. Zhang, C. Zhong, C. Liu, J. Pei, K. Ramamohanarao, and J. Chen. 2014. “A Spatiotemporal Compression Based Approach for Efficient big Data Processing on Cloud.” Journal of Computer and System Sciences 80 (8): 1563–1583.
  • Ye, K., X. Jiang, Y. He, X. Li, H. Yan, and P. Huang. 2012. “vHadoop: A Scalable Hadoop Virtual Cluster Platform for Mapreduce-based Parallel Machine Learning with Performance Consideration.” In 2012 IEEE International Conference on Cluster Computing Workshops (CLUSTER WORKSHOPS), 152–160.
  • Yeager, P. S. 2003. “ A distributed file system for distributed conferencing system.” PhD diss., University of Florida. Accessed November 25, 2015. http://etd.fcla.edu/UF/UFE0001123/yeager_p.pdf.
  • Yee, K. P., K. Swearingen, K. Li, and M. Hearst. 2003. Faceted Metadata for Image Search and Browsing. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 401–408.
  • Yue, P., C. Zhang, M. Zhang, X. Zhai, and L. Jiang. 2015. “An SDI Approach for Big Data Analytics: The Case on Sensor Web Event Detection and Geoprocessing Workflow.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8 (10): 4720–4728.
  • Zelenkauskaite, A., and B. Simões. 2014. Big Data Through Cross-Platform Interest-Based Interactivity. In 2014 International Conference on Big Data and Smart Computing (BIGCOMP), 191–196. IEEE.
  • Zhai, Y., Y. S. Ong, and I. W. Tsang. 2014. “The Emerging “Big Dimensionality”.” IEEE Computational Intelligence Magazine 9 (3): 14–26.
  • Zhan, Z. H., X. F. Liu, Y. J. Gong, J. Zhang, H. S. H. Chung, and Y. Li. 2015. “Cloud Computing Resource Scheduling and a Survey of its Evolutionary Approaches.” ACM Computing Surveys (CSUR) 63: 1–33.
  • Zhang, F., J. Cao, S. U. Khan, K. Li, and K. Hwang. 2015a. “A Task-Level Adaptive MapReduce Framework for Real-Time Streaming Data in Healthcare Applications.” Future Generation Computer Systems 43–44: 149–160.
  • Zhang, Q., Z. Chen, and Y. Leng. 2015. “Distributed Fuzzy C-Means Algorithms for big Sensor Data Based on Cloud Computing.” International Journal of Sensor Networks 18 (1–2): 32–39.
  • Zhang, Q., Z. Chen, and L. T. Yang. 2015. “A Nodes Scheduling Model Based on Markov Chain Prediction for big Streaming Data Analysis.” International Journal of Communication Systems 28 (9): 1610–1619.
  • Zhang, X., X. Li, and J. Chen. 2012. Message from BigDataMR2012, In 2012 Second International Conference on Chairs, Cloud and Green Computing (CGC), xxix-xxix, IEEE. doi:10.1109/cgc.2012.136.
  • Zhang, X., C. Liu, S. Nepal, C. Yang, and J. Chen. 2014. Privacy Preservation Over Big Data in Cloud Systems. In Security, Privacy and Trust in Cloud Systems, 239–257. Springer Berlin Heidelberg.
  • Zhang, F., Q. M. Malluhi, T. Elsayed, S. U. Khan, K. Li, and A. Y. Zomaya. 2015b. “CloudFlow: A Data-Aware Programming Model for Cloud Workflow Applications on Modern HPC Systems.” Future Generation Computer Systems 51: 98–110. doi:10.1016/j.future.2014.10.028.
  • Zhang, M., H. Wang, Y. Lu, T. Li, Y. Guang, C. Liu, E. Edrosa, H. Li, and N. Rishe. 2015c. “TerraFly GeoCloud: An Online Spatial Data Analysis and Visualization System.” ACM Transactions on Intelligent Systems and Technology (TIST) 6: 34.
  • Zhang, L., C. Wu, Z. Li, C. Guo, M. Chen, and F. Lau. 2013. “Moving big Data to the Cloud: An Online Cost-Minimizing Approach.” IEEE Journal on Selected Areas in Communications 31 (12): 2710–2721.
  • Zhang, X., and F. Xu. 2013. Survey of Research on Big Data Storage. In 2013 12th International Symposium on Distributed Computing and Applications to Business, Engineering & Science (DCABES), 76–80. IEEE.
  • Zhao, L., L. Chen, R. Ranjan, K.-K. R. Choo, and J. He. 2015. “Geographical Information System Parallelization for Spatial big Data Processing: A Review.” Cluster Computing: 19 (1): 139–152.
  • Zhao, J., L. Wang, J. Tao, J. Chen, W. Sun, R. Ranjan, J. Kołodziej, A. Streit, and D. Georgakopoulos. 2014. “A Security Framework in G-Hadoop for big Data Computing Across Distributed Cloud Data Centres.” Journal of Computer and System Sciences 80 (5): 994–1007.
  • Zhao, H., S. Yang, Z. Chen, S. Jin, H. Yin, and L. Li. 2012. Mapreduce Model-based Optimization of Range Queries. In 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 2487–2492. IEEE.
  • Zhong, Y., J. Han, T. Zhang, Z. Li, J. Fang, and G. Chen. 2012. Towards Parallel Spatial Query Processing for Big Spatial Data. In 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum (IPDPSW), 2085–2094. IEEE.
  • Zhu, T., C. Shu, and H. Yu. 2011. Green Scheduling: A Scheduling Policy for Improving the Energy Efficiency of Fair Scheduler. In 2011 12th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), 319–326. IEEE.
  • Zikopoulos, P., and C. Eaton. 2011. Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data. McGraw-Hill Osborne Media (Chapter 2).