Big Data Applications in Engineering and Science

  • Kok-Leong Ong
  • Daswin De Silva
  • Yee Ling Boo
  • Ee Hui Lim
  • Frank Bodi
  • Damminda Alahakoon
  • Simone Leao


Research to solve engineering and science problems commonly require the collection and complex analysis of a vast amount of data. This makes them a natural exemplar of big data applications. For example, data from weather stations, high resolution images from CT scans, or data captured by astronomical instruments all easily showcase one or more big data characteristics, i.e., volume, velocity, variety and veracity. These big data characteristics present computational and analytical challenges that need to be overcame in order to deliver engineering solutions or make scientific discoveries. In this chapter, we catalogued engineering and science problems that carry a big data angle. We will also discuss the research advances for these problems and present a list of tools available to the practitioner. A number of big data application exemplars from the past works of the authors are discussed with further depth, highlighting the association of the specific problem and its big data characteristics. The overview from these various perspectives will provide the reader an up-to-date audit of big data developments in engineering and science.


  1. 1.
    Gómez I, Caselles V, Estrela MJ (2014) Real-time weather forecasting in the Western Mediterranean Basin: An application of the RAMS model. Atmos Res 139:71–89CrossRefGoogle Scholar
  2. 2.
    Agerri R, Artola X, Beloki Z, Rigau G, Soroa A (2014) Big data for natural language processing: a streaming approach. Knowledge-Based SystemsGoogle Scholar
  3. 3.
    Ahrens J, Hendrickson B, Long G, Miller S, Ross R, Williams D (2011) Data-intensive science in the us doe: case studies and future challenges. Comput Sci Eng 13(6):14–24CrossRefGoogle Scholar
  4. 4.
    Al-Jarrah OY, Yoo PD, Muhaidat S, Karagiannidis GK, Taha K (2015) Efficient machine learning for big data: a review. Big Data Res 2(3):87–93. Big data, analytics, and high-performance computingGoogle Scholar
  5. 5.
    Alahakoon D, Yu X (2015) Smart electricity meter data intelligence for future energy Systems: a survey. IEEE Trans Ind Inf 99Google Scholar
  6. 6.
    Amato A, Di Martino B, Venticinque S (2014) Big data processing for pervasive environment in cloud computing. In: International conference on intelligent networking and collaborative systems (INCoS). IEEE, pp 598–603Google Scholar
  7. 7.
    Banisar D, Parmar S, De Silva L, Excell C (2012) Moving from principles to rights: rio 2012 and access to information, public participation and justice. Sustainable Development Law & Policy 12(3):8–14Google Scholar
  8. 8.
    Baumann P, Mazzetti P, Ungar J, Barbera R, Barboni D, Beccati A, Bigagli L, Boldrini E, Bruno R, Calanducci A, Campalani P, Clements O, Dumitru A, Grant M, Herzig P, Kakaletris G, Laxton J, Koltsida P, Lipskoch K, Mahdiraji AR, Mantovani S, Merticariu V, Messina A, Misev D, Natali S, Nativi S, Oosthoek J, Pappalardo M, Passmore J, Rossi AP, Rundo F, Sen M, Sorbera V, Sullivan D, Torrisi M, Trovato L, Veratelli MG, Wagner S (2014) Big data analytics for earth sciences: the EarthServer approach. Digital Earth 1–27. doi:10.1080/17538947.2014.1003106
  9. 9.
    Bellazzi R, Larizza C, Magni P, Montani S, Stefanelli M (2000) Intelligent analysis of clinical time series: an application in the diabetes mellitus domain. Artif Intell Med 20(1):37–57CrossRefGoogle Scholar
  10. 10.
    Bhardwaj R, Sethi A, Nambiar R (2014) Big data in genomics: an overview. In: IEEE international conference on big data. IEEE, pp 45–49Google Scholar
  11. 11.
    Bhattacharya M, Islam R, Abawajy J (2014) Evolutionary optimization: a big data perspective. J Netw Comput Appl 59:416–426CrossRefGoogle Scholar
  12. 12.
    Bhogal I, Choksi J (2015) Handling big data using NoSQL. In: Advanced information networking and applications workshops. IEEECrossRefGoogle Scholar
  13. 13.
    Borne K (2009) Scientific data mining in astronomy. Taylor & Francis/CRC, Boca Raton, pp 91–114Google Scholar
  14. 14.
    Borne K, Accomazzi A, Bloom J, Brunner R, Burke D, Butler N, Chernoff DF, Connolly B, Connolly A, Connors A, Cutler C, Desai S, Djorgovski G, Feigelson E, Finn LS, Freeman P, Graham M, Gray N, Graziani C, Guinan EF, Hakkila J, Jacoby S, Jefferys W, Kashyap R, Kelly B, Knuth K, Lamb DQ, Lee H, Loredo T, Mahabal A, Mateo M, McCollum B, A. Muench, Pesenson M, Petrosian V, Primini F, Protopapas P, Ptak A, Quashnock J, Raddick MJ, Rocha G, Ross N, Rottler L, Scargle J, Siemiginowska A, Song I, Szalay A, Tyson JA, Vestrand T, Wallin J, Wandelt B, Wasserman IM, Way M, Weinberg M, Zezas A, Anderes E, Babu J, Becla J, Berger J, Bickel PJ, Clyde M, Davidson I, van Dyk D, Eastman T, Efron B, Genovese C, Gray A, Jang W, Kolaczyk ED, Kubica J, Loh JM, Meng X-L, Moore A, Morris R, Park T, Pike R, Rice J, Richards J, Ruppert D, Saito N, Schafer C, Stark PB, Stein M, Sun J, Wang D, Wang Z, Wasserman L, Wegman EJ, Willett R, Wolpert R, Woodroofe M (2009) Astroinformatics: a twenty first century approach to astronomy. In: Astro2010: the astronomy and astrophysics decadal survey. ArXiv Astrophysics e-prints, vol. 2010, p. 6PGoogle Scholar
  15. 15.
    Bostrm H, Andler SF, Brohede M, Johansson R, Karlsson A, Van Laere J, Niklasson L, Nilsson M, Persson A, Ziemke T (2007) On the definition of information fusion as a field of research. Technical Report, University of SkövdeGoogle Scholar
  16. 16.
    Bryant RE (2011) Data-intensive scalable computing for scientific applications. Comput Sci Eng 13(6):25–33CrossRefGoogle Scholar
  17. 17.
    Bunyavanich S, Schadt EE (2015) Systems biology of asthma and allergic diseases: a multiscale approach. J Allergy Clin Immunol 135(1):31–42CrossRefGoogle Scholar
  18. 18.
    Chang F-J, Chiang Y-M, Tsai M-J, Shieh M-C, Hsu K-L, Sorooshian S (2014) Watershed rainfall forecasting using neuro-fuzzy networks with the assimilation of multi-sensor information. J Hydrol 508:374–384CrossRefGoogle Scholar
  19. 19.
    Chawla NV, Davis DA (2013) Bringing big data to personalised healthcare: a patient-centered framework. J Gen Intern Med 28(3):660–665CrossRefGoogle Scholar
  20. 20.
    Chen CLP, Zhang C-Y(2014) Data-intensive applications, challenges, techniques and technologies: A survey on big data. Inf Sci 275:314–347Google Scholar
  21. 21.
    Chen H, Chiang RHL, Storey VC (2012) Business intelligence and analytics: from big data to big impact. MIS Q 36(4):1165–1188Google Scholar
  22. 22.
    Chen H, Compton S, Hsiao O (2013) DiabeticLink: a health big data system for patient empowerment and personalised healthcare. Springer, Heidelberg, pp 71–83Google Scholar
  23. 23.
    Chute CG, Beck SA, Fisk TB, Mohr DN (2010) The enterprise data trust at mayo clinic: a semantically integrated warehouse of biomedical data. J Am Med Inform Assoc 17(2): 131–135CrossRefGoogle Scholar
  24. 24.
    Coen MH (1999) Cross-modal clustering. In: Proceedings of the national conference on artificial intelligence, vol 20. AAAI/MIT, Menlo Park/Cambridge/London, p 932Google Scholar
  25. 25.
    Conrad C, Hilchey K (2011) A review of citizen science and community-based environmental monitoring: issues and opportunities. Environ Monit Assess 176(1–4):273–291CrossRefGoogle Scholar
  26. 26.
    Cottrill CD, Derrible S (2015) Leveraging big data for the development of transport sustainability indicators. J Urban Technol 22(1):45–64CrossRefGoogle Scholar
  27. 27.
    de Souza RS, Ciardi B (2015) AMADA: analysis of multidimensional astronomical datasets. Astron Comput 12:100–108CrossRefGoogle Scholar
  28. 28.
    Dieleman S, Willett KW, Dambre J (2015) Rotation-invariant convolutional neural networks for galaxy morphology prediction. Mon Not R Astron Soc 450(2):1441–1459CrossRefGoogle Scholar
  29. 29.
    Dobre C, Xhafa F (2014) Intelligent services for big data science. Futur Gener Comput Syst 37:267–281CrossRefGoogle Scholar
  30. 30.
    Duan L, Xiong Y (2015) Big data analytics and business analytics. J Manag Anal 2(1):1–21Google Scholar
  31. 31.
    Dutta H, Giannella C, Borne K, Kargupta H (2007) Distributed top-k outlier detection from astronomy catalogs using the demac system. In: SIAM international conference on data mining. Society for Industrial and Applied MathematicsCrossRefGoogle Scholar
  32. 32.
    Fairfield J, Shtein H (2014) Big data, big problems: emerging issues in the ethics of data science and journalism. J Mass Media Ethics 29(1):38–51CrossRefGoogle Scholar
  33. 33.
    Faizrahnemoon M, Schlote A, Maggi L, Crisostomi E, Shorten R (2015) A big data model for multi-modal public transportation with application to macroscopic control and optimisation. Control 88(11):2354–2368 (just-accepted)Google Scholar
  34. 34.
    Fan J, Liu H (2013) Statistical analysis of big data on pharmacogenomics. Adv Drug Deliv Rev 65(7):987–1000CrossRefGoogle Scholar
  35. 35.
    Fan S, Lau RYK, Zhao JL (2015) Demystifying big data analytics for business intelligence through the lens of marketing mix. Big Data Res 2(1):28–32CrossRefGoogle Scholar
  36. 36.
    Goldman J, Shilton K, Burke J, Estrin D, Hansen M, Ramanathan N, Reddy S, Samanta V, Srivastava M, West R (2009) Participatory sensing: a citizen-powered approach to illuminating the patterns that shape our world. ReportGoogle Scholar
  37. 37.
    Goshtasby AA, Nikolov S (2007) Image fusion: advances in the state of the art. Inf Fusion 8(2):114–118CrossRefGoogle Scholar
  38. 38.
  39. 39.
    Guo H (2014) Digital earth: big earth data. Int J Digital Earth 7(1):1–27CrossRefGoogle Scholar
  40. 40.
    Hashem IAT, Yaqoob I, Anuar NB, Mokhtar S, Gani A, Khan SU (2015) The rise of big data on cloud computing: Review and open research issues. Inf Syst 47:98–115CrossRefGoogle Scholar
  41. 41.
    Heer J, Mackinlay J, Stolte C, Agrawala M (2008) Graphical histories for visualization: Supporting analysis, communication, and evaluation. IEEE Trans Vis Comput Graph 14(6): 1189–1196CrossRefGoogle Scholar
  42. 42.
    Hey AJG, Tansley S, Tolle KM (2009) The fourth paradigm: data-intensive scientific discovery, vol 1. Microsoft Research, RedmondGoogle Scholar
  43. 43.
    Horn M, Mirzatuny M (2013) Mining big data to transform electricity. Springer, New York pp 47–58Google Scholar
  44. 44.
    Hu H, Correll M, Kvecher L, Osmond M, Clark J, Bekhash A, Schwab G, Gao D, Gao J, Kubatin V (2011) Dw4tr: a data warehouse for translational research. J Biomed Inform 44(6):1004–1019CrossRefGoogle Scholar
  45. 45.
    Huang J, Niu L, Zhan J, Peng X, Bai J, Cheng S (2014) Technical aspects and case study of big data based condition monitoring of power apparatuses. In: IEEE PES Asia-Pacific power and energy engineering conference (APPEEC). IEEE, pp 1–4Google Scholar
  46. 46.
    Jacobs A (2009) The pathologies of big data. Commun ACM 52(8):36–44CrossRefGoogle Scholar
  47. 47.
    Jagadish HV (2015) Big data and science: myths and reality. Big Data Res 2(2):49–52. Visions on Big DataGoogle Scholar
  48. 48.
    Jelinek HF, Wilding C, Tinely P (2006) An innovative multi-disciplinary diabetes complications screening program in a rural community: a description and preliminary results of the screening. Aust J Prim. Health 12(1):14–20CrossRefGoogle Scholar
  49. 49.
    Ji C, Li Y, Qiu W, Awada U, Li K (2012) Big data processing in cloud computing environments. In: Proceedings of the international symposium on parallel architectures, algorithms and networks, I-SPANCrossRefGoogle Scholar
  50. 50.
    Keh H-C, Hui L, Chou K-Y, Cheng Y-C, Yu P-Y, Huang N-C (2014) Big data generation: application of mobile healthcare. Springer, Switzerland pp 735–743Google Scholar
  51. 51.
    Keravnou ET (1997) Temporal abstraction of medical data: deriving periodicity. Springer, Berlin, pp 61–79Google Scholar
  52. 52.
    Khaleghi B, Khamis A, Karray FO, Razavi SN (2013) Multisensor data fusion: a review of the state-of-the-art. Inf Fusion 14(1):28–44CrossRefGoogle Scholar
  53. 53.
    Krmer M, Senner I (2015) A modular software architecture for processing of big geospatial data in the cloud. Comput Graph 49:69–81CrossRefGoogle Scholar
  54. 54.
    Lane N, Miluzzo E, Lu H, Peebles D, Choudhury T, Campbell A (2010) A survey of mobile phone sensing. IEEE Commun 48(9):140–150CrossRefGoogle Scholar
  55. 55.
    Laney D (2001) 3d data management: controlling data volume, velocity and variety. Technical Report, META Group Research NoteGoogle Scholar
  56. 56.
    Leao S, Peerson A, Elkadhi H (2012) Effects of exposure to traffic noise on health. In: Proceedings of the 5th healthy cities: working together to achieve liveable cities conference, GeelongGoogle Scholar
  57. 57.
    Leao S, Ong K-L, Krezel A (2014) 2loud?: community mapping of exposure to traffic noise with mobile phones. Environ Monit Assess 186(10):6193–6202CrossRefGoogle Scholar
  58. 58.
    Leung CK.-S., Jiang F (2014) A data science solution for mining interesting patterns from uncertain big data. In: 2014 IEEE fourth international conference onBig data and cloud computing (BdCloud), IEEE, pp 235–242.Google Scholar
  59. 59.
    Li X, Plale B, Vijayakumar N, Ramachandran R, Graves S, Conover H (2008) Real-time storm detection and weather forecast activation through data mining and events processing. Earth Sci Inf 1(2):49–57CrossRefGoogle Scholar
  60. 60.
    Li L, Su X, Wang Y, Lin Y, Li Z, Li Y (2015) Robust causal dependence mining in big data network and its application to traffic flow predictions. Transp Res C Emerg Technol 58(B):292–307CrossRefGoogle Scholar
  61. 61.
    Lim EH, Bodi F (2012) Managing the complexity of a telecommunication power systems equipment replacement program. In: 2012 IEEE 34th international telecommunications energy conference (INTELEC), pp 1–9Google Scholar
  62. 62.
    Lowe HJ, Ferris TA, Hernandez PM, Weber SC (2009) STRIDE–An integrated standards-based translational research informatics platform. In: AMIA Annual Symposium Proceedings, vol. 2009, American Medical Informatics Association, p 391Google Scholar
  63. 63.
    Ludwig N, Feuerriegel S, Neumann D (2015) Putting big data analytics to work: feature selection for forecasting electricity prices using the lasso and random forests. J Decis Syst 24(1):19–36CrossRefGoogle Scholar
  64. 64.
    Mahrt M, Scharkow M (2013) The value of big data in digital media research. J Broadcast Electron Media 57(1):20–33CrossRefGoogle Scholar
  65. 65.
    Miyoshi T, Kondo K, Imamura T (2014) The 10,240-member ensemble Kalman filtering with an intermediate AGCM. Geophys Res Lett 41(14):5264–5271. doi:10.1002/2014GL060863 CrossRefGoogle Scholar
  66. 66.
    Nativi S, Mazzetti P, Santoro M, Papeschi F, Craglia M, Ochiai O (2015) Big data challenges in building the global earth observation system of systems. Environ Model Softw 68:1–26CrossRefGoogle Scholar
  67. 67.
    Nguyen BV, Burstein F, Fisher J (2014) Improving service of online health information provision: a case of usage-driven design for health information portals. Inf Syst Front 17(3):493–511CrossRefGoogle Scholar
  68. 68.
    Nurmi D, Wolski R, Grzegorczyk C, Obertelli G, Soman S, Youseff L, Zagorodnov D (2009) The eucalyptus open-source cloud-computing system. In: 9th IEEE/ACM international symposium on cluster computing and the grid, 2009 (CCGRID’09), IEEE, pp 124–131Google Scholar
  69. 69.
    Oberg AL, McKinney BA, Schaid DJ, Pankratz VS, Kennedy RB, Poland GA (2015) Lessons learned in the analysis of high-dimensional data in vaccinomics. Vaccine 33(40):5262–5270CrossRefGoogle Scholar
  70. 70.
    ODriscoll A, Daugelaite J, Sleator RD (2013) big data, hadoop and cloud computing in genomics. J Biomed Inform 46(5):774–781CrossRefGoogle Scholar
  71. 71.
    Ong K-L, Leao S, Krezel A (2014) Participatory sensing and education: helping the community mitigate sleep disturbance from traffic noise. Pervasive Comput Commun 10(4):419–441CrossRefGoogle Scholar
  72. 72.
    Palmieri F, Fiore U, Ricciardi S, Castiglione A (2015) Grasp-based resource re-optimization for effective big data access in federated clouds. Futur Gener Comput Syst 54:168–179CrossRefGoogle Scholar
  73. 73.
    Peise E, Fabregat-Traver D, Bientinesi P (2014) High performance solutions for big-data GWAS. Parallel Comput 42:75–87CrossRefGoogle Scholar
  74. 74.
    Perkins S, Questiaux J, Finniss S, Tyler R, Blyth S, Kuttel MM (2014) Scalable desktop visualisation of very large radio astronomy data cubes. New Astron 30:1–7CrossRefGoogle Scholar
  75. 75.
    Pez DG, Aparicio F, De Buenaga M, Ascanio JR (2014) Chronic patients monitoring using wireless sensors and big data processing. In: 2014 Eighth International Conference on Innovative mobile and internet services in ubiquitous computing (IMIS), IEEE, pp 404–408Google Scholar
  76. 76.
    Pijanowski BC, Tayyebi A, Doucette J, Pekin BK, Braun D, Plourde J (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. Environ Model Softw 51:250–268CrossRefGoogle Scholar
  77. 77.
    Prakasa A, De Silva D (2013) Development of user warrant ontology for improving online health information provision. In: 24th Australasian conference on information systems (ACIS), RMIT University, pp 1–12Google Scholar
  78. 78.
    Procter R, Vis F, Voss A (2013) Reading the riots on twitter: methodological innovation for the analysis of big data. Int J Soc Res Methodol 16(3):197–214CrossRefGoogle Scholar
  79. 79.
    Raghupathi W, Raghupathi V (2014) Big data analytics in healthcare: promise and potential. Health Inf Sci Syst 2(1):3CrossRefGoogle Scholar
  80. 80.
    Schraml J (1978) On-line and real-time processing in radio astronomy. Comput Phys Commun 15(5):347–349CrossRefGoogle Scholar
  81. 81.
    Schroeder R, Taylor T (2015) Big data and wikipedia research: social science knowledge across disciplinary divides. Inf Commun Soc 18(9):1039–1056CrossRefGoogle Scholar
  82. 82.
    Schutter A, Shamir L (2015) Galaxy morphology an unsupervised machine learning approach. Astron Comput 12:60–66CrossRefGoogle Scholar
  83. 83.
    Sen A, Banerjee A, Sinha AP, Bansal M (2012) Clinical decision support: Converging toward an integrated architecture. J Biomed Inform 45(5):1009–1017CrossRefGoogle Scholar
  84. 84.
    Shahar Y (1994) A knowledge-based method for temporal abstraction of clinical data. Ph.D. Dissertation program in medical information sciences, Stanford University School of Medicine, StanfordGoogle Scholar
  85. 85.
    Shahrokni H, Levihn F, Brandt N (2014) Big meter data analysis of the energy efficiency potential in Stockholm’s building stock. Energy Build 78:153–164CrossRefGoogle Scholar
  86. 86.
    Sharshar S, Allart L, Chambrin M-C (2005) A new approach to the abstraction of monitoring data in intensive care. Springer, Berlin, pp 13–22Google Scholar
  87. 87.
    Shi Q, Abdel-Aty M (2015) Big data applications in real-time traffic operation and safety monitoring and improvement on urban expressways. Transp Res C Emerg TechnolGoogle Scholar
  88. 88.
    Sittig DF, Wright A, Osheroff JA, Middleton B, Teich JM, Ash JS, Campbell E, Bates DW (2008) Grand challenges in clinical decision support. J Biomed Inform 41(2):387–392CrossRefGoogle Scholar
  89. 89.
    Sivaraman E, Manickachezian R (2014) High performance and fault tolerant distributed file system for big data storage and processing using hadoop. In: 2014 international conference on intelligent computing applications (ICICA), IEEE, pp 32–36Google Scholar
  90. 90.
    Song J, Guo C, Wang Z, Zhang Y, Yu G, Pierson J-M (2014) HaoLap: a hadoop based OLAP system for big data. J Syst Softw 102:167–181CrossRefGoogle Scholar
  91. 91.
    Stacey M, McGregor C (2007) Temporal abstraction in intelligent clinical data analysis: a survey. Artif Intell Med 39(1):1–24CrossRefGoogle Scholar
  92. 92.
    Steed CA, Ricciuto DM, Shipman G, Smith B, Thornton PE, Wang D, Shi X, Williams DN (2013) Big data visual analytics for exploratory earth system simulation analysis. Comput Geosci 61:71–82CrossRefGoogle Scholar
  93. 93.
    Stevens KB, Pfeiffer DU (2015) Sources of spatial animal and human health data: casting the net wide to deal more effectively with increasingly complex disease problems. Spatial and Spatio-temporal Epidemiology 13:15–29CrossRefGoogle Scholar
  94. 94.
    Sullivan K, Uccellini L (2013) Service assessment: Hurricane/post-tropical cyclone sandy. National oceanic and atmospheric administration, National Weather Service, May 2013Google Scholar
  95. 95.
    Szalay A (2011) Extreme data-intensive scientific computing. Comput Sci Eng 13(6):34–41MathSciNetCrossRefGoogle Scholar
  96. 96.
    Tang W, Feng W (2014) Parallel map projection of vector-based big spatial data: coupling cloud computing with graphics processing units. Comput Environ Urban Syst. Google Scholar
  97. 97.
    Torra V (2003) On some aggregation operators for numerical information. Springer, Berlin, pp 9–26MATHGoogle Scholar
  98. 98.
    Valds JJ, Bonham-Carter G (2006) Time dependent neural network models for detecting changes of state in complex processes: applications in earth sciences and astronomy. Neural Netw 19(2):196–207. Earth Sciences and Environmental Applications of Computational Intelligence.Google Scholar
  99. 99.
    Valle ED, Ceri S, van Harmelen F, Fensel D (2009) It’s a streaming world! reasoning upon rapidly changing information. IEEE Intell Syst 24(6):83–89CrossRefGoogle Scholar
  100. 100.
    Valverde MC, Araujo E, Velho HC (2014) Neural network and fuzzy logic statistical downscaling of atmospheric circulation-type specific weather pattern for rainfall forecasting. Appl Soft Comput 22:681–694CrossRefGoogle Scholar
  101. 101.
    Van Wart J, Grassini P, Yang H, Claessens L, Jarvis A, Cassman KG (2015) Creating long-term weather data from thin air for crop simulation modeling. Agric For Meteorol 209–210: 49–58CrossRefGoogle Scholar
  102. 102.
    Wang L, Tao J, Ranjan R, Marten H, Streit A, Chen J, Chen D (2013) G-hadoop: mapreduce across distributed data centers for data-intensive computing. Futur Gener Comput Syst 29(3):739–750CrossRefGoogle Scholar
  103. 103.
    Wang M, Wang J, Tian F (2014) City intelligent energy and transportation network policy based on the big data analysis. Procedia Comput Sci 32:85–92CrossRefGoogle Scholar
  104. 104.
    Ward RM, Schmieder R, Highnam G, Mittelman D (2013) Big data challenges and opportunities in high-throughput sequencing. Syst Biomed 1(1):29–34CrossRefGoogle Scholar
  105. 105.
    Wen X, Gu G, Li Q, Gao Y, Zhang X (2012) Comparison of open-source cloud management platforms: openstack and opennebula. In: 2012 9th international conference onFuzzy systems and knowledge discovery (FSKD), IEEE, pp 2457–2461Google Scholar
  106. 106.
    WHO Regional Office for Europe (2010) Burden of disease from environmental noise: practical guidance. Report, World Health OrganisationGoogle Scholar
  107. 107.
    Wisniewski MF, Kieszkowski P, Zagorski BM, Trick WE, Sommers M, Weinstein RA, Chicago Antimicrobial Resistance Project (2003) Development of a clinical data warehouse for hospital infection control. J Am Med Inform Assoc 10(5):454–462CrossRefGoogle Scholar
  108. 108.
    Wright A, Sittig DF (2008) A four-phase model of the evolution of clinical decision support architectures. Int J Med Inform 77(10):641–649CrossRefGoogle Scholar
  109. 109.
    Yang Y, Lin H, Guo Z, Jiang J (2007) A data mining approach for heavy rainfall forecasting based on satellite image sequence analysis. Comput Geosci 33(1):20–30CrossRefGoogle Scholar
  110. 110.
    Yang J-J, Li J, Mulder J, Wang Y, Chen S, Wu H, Wang Q, Pan H (2015) Emerging information technologies for enhanced healthcare. Comput Ind 69:3–11CrossRefGoogle Scholar
  111. 111.
    Yao, JT, Raghavan VV, Wu Z (2008) Web information fusion: a review of the state of the art. Inf Fusion 9(4):446–449CrossRefGoogle Scholar
  112. 112.
    Yu J, Jiang F, Zhu T (2013) RTIC-C: a big data system for massive traffic information mining. In: 2013 international conference on cloud computing and big data (CloudCom-Asia), IEEE, pp 395–402Google Scholar
  113. 113.
    Zhang Y, Zhao Y (2015) Astronomy in the big data era. Data Sci J 14(11):1–9Google Scholar
  114. 114.
    Zhang X, Li D, Cheng M, Zhang P (2014) Electricity consumption pattern recognition based on the big data technology to support the peak shifting potential analysis. In: IEEE PES Asia-Pacific power and energy engineering conference, pp 1–5Google Scholar
  115. 115.
    Zheng H, Zhang Y (2008) Feature selection for high-dimensional data in astronomy. Adv Space Res 41(12):1960–1964CrossRefGoogle Scholar
  116. 116.
    Zheng J, Li Z, Dagnino A (2014) Speeding up processing data from millions of smart meters. In: Proceedings of the 5th ACM/SPEC international conference on performance engineering, ACM, pp 27–37Google Scholar
  117. 117.
    Zheng Y, Capra L, Wolfson O, Yang H (2014) Urban computing: concepts, methodologies, and applications. ACM Trans Intell Syst Technol (TIST) 5(3):38Google Scholar
  118. 118.
    Zikopoulos P, Eaton C (2011) Understanding big data: analytics for enterprise class Hadoop and streaming data. McGraw-Hill Osborne Media, New YorkGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Kok-Leong Ong
    • 1
  • Daswin De Silva
    • 1
  • Yee Ling Boo
    • 2
  • Ee Hui Lim
    • 3
  • Frank Bodi
    • 3
  • Damminda Alahakoon
    • 1
  • Simone Leao
    • 4
  1. 1.La Trobe Business School, ASSCLa Trobe UniversityMelbourneAustralia
  2. 2.School of Business IT and LogisticsRMIT UniversityMelbourneAustralia
  3. 3.Thiess Services Pty LtdBurwoodAustralia
  4. 4.School of Built EnvironmentUniversity of SalfordLancashireUK

Personalised recommendations