Big data analytics in smart grids: a review

Ahmad A, Javaid N, Guizani M, Alrajeh N, Khan ZA (Oct. 2017) An accurate and fast converging short-term load forecasting model for industrial applications in a smart grid. IEEE Transactions on Industrial Informatics 13(5):2587–2596

Ahmed N, Levorato M, Li GP (2017) Residential consumer-centric demand side management. IEEE Transactions on Smart Grid. https://doi.org/10.1109/TSG.2017.2661991

Ak R, Fink O, Zio E (2016) Two machine learning approaches for short-term wind speed time-series prediction. IEEE Transactions on Neural Networks and Learning Systems 27(8):1734–1747

Alam Mollah Rezaul, Kashem M. Muttaqi, Abdesselam Bouzerdoum. Evaluating the effectiveness of a machine learning approach based on response time and reliability for islanding detection of distributed generation. IET renewable power generation (volume: 11, Issue: 11, 2017)

Allen WH, Rubaai A, Chawla R (May-June 2016) Fuzzy neural network-based health monitoring for HVAC system variable-air-volume unit. IEEE Trans Ind Appl 52(3):2513–2524

Al-Otaibi R, Jin N, Member IEEE, Wilcox T, Flach P (April 2016) Feature construction and calibration for clustering daily load curves from smart-meter data. IEEE Transactions on Industrial Informatics 12(2):645–654

Andalib-Bin-Karim C, Liang X, Khan N, Zhang H (2017) Determine Q-V characteristics of grid-connected wind farms for voltage control using a data-driven analytics approach. IEEE Trans Ind Appl 53(5)

Babakmehr M, Simões MG, Wakin MB, Harirchi F (2016) Compressive sensing-based topology identification for smart grids. IEEE Transactions on Industrial Informatics 12(2):532–543

Bahmanyar A, Jamali S, Estebsari A, Pons E, Bompard E, Patti E, Acquaviva A (2016) Emerging smart meters in electrical distribution systems: opportunities and challenges. In: 24th Iranian Conference on Electrical Engineering (ICEE). Shiraz, Iran

Baimel D, Tapuchi S, Baimel N (2016) Smart grid communication technologies- overview, research challenges and opportunities. International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM), 22-24 June 2016, Anacapri, Italy

Balouji E, Salor O (2017) Classification of power quality events using deep learning on events images. 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA), 19-20 April 2017, Shahrekord, Iran

Bauman K, Tuzhilin A, Zaczynski R (2017) Using social sensors for detecting emergency events: a case of power outages in the electrical utility industry. ACM Transactions on Management Information Systems 8(2–3)

Berges M, Goldman E, Matthews HS, Soibelman L (2009) Learning systems for electric comsumption of buildings. In: In ASCI International Workshop on Computing in Civil Engineering

Big Data analytics and energy consumption. (2016) Available: http://www.lavastorm.com/blog/2012/04/09/big-data-analytics-and-energy-consumption/

Bo P, Wan C, Dong S, Lin J, Song Y, Yi Z, Xiong J (2016) A Two-stage Pattern Recognition Method for Electric Customer Classification in Smart Grid. In: 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm), Sydney, vol. 12

Borges FAS, Fernandes RAS, Silva IN, Silva CBS (April 2016) Feature extraction and power quality disturbances classification using smart meters signals. IEEE Transactions on Industrial Informatics 12(2):824–833

Bunn DW, Farmer ED (1985) ComparativeModels for electrical load forecasting. Wiley, New York

Cai Y, Huang T, Bompard E, Cao Y, Li Y (2017) Self-Sustainable Community of Electricity Prosumers in the Emerging Distribution System. IEEE Transactions on Smart Grid, vol 8, no. 5, pp. 2207–2216

Cai Y, Chow M-Y (2009) Exploratory analysis of massive data for distribution fault diagnosis in smart grids. IEEE Conference on Power & Energy Society General Meeting, July

CEN-CENELEC-ETSI Smart Grid Working Group Reference Architecture (2012) Reference architecture for the smart grid. Tech Rep

Cheng Y, Chen K, Sun H, Zhang Y, Tao F (2018) Data and knowledge mining with big data towards smart production. Journal of Industrial Information Integration 9:1–13

Chunming T, Xi H, Shuai Z, Jiang F (2017) Big data issues in smart grid – a review. Renew Sust Energ Rev 79:1099–1107

Claessens BJ, Vrancx P, Ruelens F (2016) Convolutional neural networks for automatic state-time feature extraction in reinforcement learning applied to residential load control. IEEE Transactions on Smart Grid

Colombo AG, Costantini D, Jaarsma RJ (1985) Bayes nonparametric estimation of time-dependent failure rate. IEEE Trans Rel 34(2):109–112

Cui Q, El-Arroudi K, Jo’os G’e (2017) An Effective Feature Extraction Method in Pattern Recognition Based High Impedance Fault Detection. In: 2017 19th International Conference on Intelligent System Application to Power Systems (ISAP), San Antonio, 17-20 Sept. 2017

De Santis E, Livi L, Sadeghian A, Rizzi A (2015) Modeling and recognition of smart grid faults by a combined approach of dissimilarity learning and one-class classification. In: In Neurocomputing, vol 170, pp 368–383 ISSN 0925-2312

De Santis E, Rizzi A, Sadeghian A (2017) A Learning Intelligent System for Classification and Characterization of Localized Faults in Smart Grids. 2017 IEEE Congress on Evolutionary Computation (CEC), San Sebastian, 5-8 June 2017

Di Zhua TL, Zhang J (2018) Unsupervised tip-mining from customer reviews. Decis Support Syst 107:116–124

Dimitrovska T, Rudež U, Mihalič R (2017) Fast contingency screening based on data mining. In: IEEE EUROCON International Conference on Smart Technologies, Ohrid, 6-8 July 2017

Emani CK, Cullot N, Nicolle C (2015) Understandable Big Data: A survey. Computer Science Review 17:70–81

Executive Office of the President (2013) Economic benefits of increasing electric grid resilience to weather outages. In: USA

Fan C, Xiao F, Li Z, Wang J (2018) Unsupervised data analytics in mining big building operational data for energy efficiency enhancement: a review. In: Energy and Buildings, vol 159, pp 296–308

Ferhat UÇAR, Ömer Faruk ALÇİN, Beşir DANDIL, Fikret ATA (2016) Machine Learning based Power Quality Event Classification using Wavelet_Entropy and Basic Statisticsal Features. In: 2016 21st International Conference on Methods and Models in Automation and Robotics (MMAR), Miedzyzdroje, 29 Aug.-1 Sept.2016

Ghosh D, Ghose T, Mohanta DK (Aug. 2013) Communication feasibility analysis for smart grid with phasor measurement units. IEEE Trans. Ind. Informat. 9(3):1486–1496

Gillis JM, Alshareef SM, Morsi WG (2016) Nonintrusive load monitoring using wavelet design and machine learning. IEEE Transactions on Smart Grid 7(1):320–328

Gillis JM, Morsi WG (Nov. 2017) Non-intrusive load monitoring using semi-supervised machine learning and wavelet design. IEEE Transactions on Smart Grid 8(6):2648–2655

Granell R, Axon CJ, Wallom DCH (Nov. 2015) Impact of raw data temporal resolution using selected clustering methods on residential electricity load profiles. IEEE Trans Power Syst 30(6):3217–3224

Guerrero JI, Monedero I, Biscarri F, Biscarri J, Millán R, León C (2018) Non-technical losses reduction by improving the inspections accuracy in a power utility. IEEE Trans Power Syst, vol. 33, pp. 1209-1218

Gungor V et al (Sep. 2011) Smart grid technologies communications technologies and standards. IEEE Trans Ind Informat 7(4):529–539

Günther WA, Rezazade Mehrizi MH, Huysman M, Feldberg F (2017) Debating big data: a literature review on realizing value from big data. J Strateg Inf Syst 26:191–209

Haben S, Singleton C, Grindrod P (Jan. 2016) Analysis and clustering of residential customers energy behavioral demand using smart meter data. IEEE Transactions on Smart Grid 7(1):136–144

Hashemi F, Mohammadi M, Kargarian A (2017) Islanding detection method for microgrid based on extracted features from differential transient rate of change of frequency. IET Generation, Transmission & Distribution 11(4):891–904

He C, Lin G, Mo W (2016) A method for transient stability assessment based on pattern recognition. International Conference on Smart Grid and Clean Energy Technologies (ICSGCE), 19-22 Oct. 2016

He M, Zhang J, Vittal V (Nov. 2013) Robust online dynamic security Assesment using adaptive ensemble decision-tree learning. IEEE Trans Power Syst 28(4):4089–4098

Henao N, Agbossou K, Kelouwani S, Dubé Y, Fournier M (2017) Approach in nonintrusive type I load monitoring using subtractive clustering. IEEE Transactions on Smart Grid 8(2):812–821

Imran K, Joshua Zhexue H, Md Abdul Masud, Qingshan J (2016) Segmentation of factories on electricity consumption behaviors using load profile data. IEEE Access 4:8394–8406

Jena MK, Samantaray SR (2016) Data-mining-based intelligent differential relaying for transmission lines including UPFC and wind farms. IEEE Transactions on Neural Networks and Learning Systems 27(1):8–17

Jiang H, Dai X, Gao DW, Zhang JJ, Zhang Y, Muljadi E (Sept. 2016) Spatial-temporal Synchrophasor data characterization and analytics in smart grid fault detection identification and impact casual analysis. IEEE Transactions on Smart Grid 7(5):2525–2536

Jindal A, Dua A, Kaur K, Singh M, Kumar N, Mishra S (2016) Decision tree and SVM-based data analytics for theft detection in smart grid. IEEE Transactions on Industrial Informatics 12(3):1005–1016

Jiye Q, Zhixiang J, Mengjie S et al (2015) Scenario analysis and application research on big data in smart power distribution and consumption systems. Proceedings of the CSEE 35(8):1829–1836

Jokar P, Arianpoo N, Leung VCM (2016) Electricity theft detection in AMI using customers’ consumption patterns. IEEE Transactions on Smart Grid 7(1):216–226

Kaisler S, Amnour F, Alberto J (2012) “Big data: issues and challenges moving forward”, 46th IEEE international conference on system science, Wailea, Maui, HI, USA, 7-10 Jan. 2013

Kar S, Samantaray SR, Dadash Zadeh M (2017) Data-mining model based intelligent differential microgrid protection scheme. IEEE Syst J 11(2):1161–1169

Kekatos V, Giannakis GB, Baldick R (2014) Grid topology identification using electricity prices. In: Proc. IEEE Power Energy Soc. Gen. Meeting. National Harbor, MD, USA, pp 1–5

Keyan L, Wanxin S, Dongxia Z et al (2015) Big data application requirements and scenario analysis in smart distribution network. Proceedings of the CSEE 35(2):287–293

Khodayar M, Kaynak O, Khodayar ME (Dec. 2017) Rough deep neural architecture for short-term wind speed forecasting. IEEE Transactions on Industrial Informatics 13(6):2770–2779

Kong W, Dong ZY, Jia Y, Hill DJ, Xu Y, Zhang Y (2017) Short-term residential load forecasting based on LSTM recurrent neural network. IEEE Transactions on Smart Grid (Early Access) https://doi.org/10.1109/TSG.2017.2753802

Kong W, Dong ZY, Ma J, Hill DJ, Zhao J, Luo F (2018) An extensible approach for non-intrusive load disaggregation with smart meter data. IEEE Transactions on Smart Grid 9(4):3362–3372

Lee W, Fung G, Lam H, Chan F, Lucente M (2004) Exploration on load signatures. International Conference on Electrical Engineering (ICEE)

Li D, Jayaweera SK (Dec. 2015) Machine-learning aided optimal customer decision for an interactive smart grid. IEEE Syst J 9(4):1529–1540

Li R, Gu C, Li F, Shaddick G, Dale M (2015a) Development of low voltage network Templates_Part I_Substation clustering and classification. IEEE Trans Power Syst 30(6)

Li R, Gu C, Li F, Shaddick G, Dale M (2015b) Development of low voltage network templates—part II_ peak load estimation by Clusterwise regression. IEEE Trans Power Syst 30(6)

Li R, Li F, Smith ND (Nov. 2016b) Multi-resolution load profile clustering for smart metering data. IEEE Trans Power Syst 31(6):4473–4482

Li R, Li F, Smith ND (June 2017) Load characterization and low-order approximation for smart metering data in the spectral domain. IEEE Transactions on Industrial Informatics 13(3):976–984

Li S, Wang P, Goel L (May 2016a) A novel wavelet-based ensemble method for short-term load forecasting with hybrid neural networks and feature selection. IEEE Trans Power Syst 31(3):1788–1798

Liang Jian, Simon K. K. Ng, Gail Kendall, and John W. M. Cheng. Load Signature Study-Part I: Basic Concept Structure and Methodology. IEEE Transactions on Power Delivery ( 25: 2, 2010): 551–560

Liang J, Ng SKK, Kendall G, Cheng JWM (2010b) Load signature study-part II: disaggregation framework simulation and applications. IEEE Transactions on Power Delivery 25(2):561–569

Lim Ee-Peng, Jaideep Srivastava, Satya Prabhakar, James Richardson, Entity identification in database integration, in information sciences, 89:1–2, 1996;1–38, ISSN 0020-0255

Liu C, Sun K, Rather ZH, Chen Z, Bak CL, Thøgersen P, Lund P (2014) A Systematic Approach for Dynamic Security Assessment. IEEE Transactions on Power Systems 29(2):717–730

Liu D, Zeng L, Li C, Ma K, Chen Y, Cao Y (2018) A distributed short- term load forecasting method based on local weather information. IEEE Syst J vol. 12, pp. 208-215

Lv J, Pawlak M, Annakkage UD (2017a) Prediction of the transient stability boundary based on Nonparameteric additive modeling. IEEE Trans Power Syst 32(6):4362–4369

Lv Z, Song H, Basanta-Val P, Steed A (2017b) Analytics MJN-GBD State of the art, challenges and future research topics. IEEE Transactions on Industrial Informatics 13(4):1891–1899

Madhumita P, Tajane S, Indi B (2016) Assessment of system vulnerability for smart grid applications. IEEE International Conference on Engineering and Technology (ICETECH)

Malbasa V, Zheng C, Chen P-C, Popovic T, Kezunovic M (Nov. 2017) Voltage stability prediction using active machine learning. IEEE Transactions on Smart Grid 8(6):3117–3124

Mishra DP, Samantaray SR, Joos G (2016) A combined wavelet and data-mining based intelligent protection scheme for microgrid. IEEE Transactions on Smart Grid 7(5):2295–2304

Moreno-Munoz A, Bellido-Outeirino FJ, Siano P, Gomez-Nieto MA (2016) Mobile social media for smart grids customer engagement: emerging trends and challenges. Renew Sust Energ Rev 53:1611–1616

Munshi AA, Mohamed YA-RI (2018) Extracting and defining flexibility of residential electrical vehicle charging loads. IEEE Transactions on Industrial Informatics vol 14, pp. 448-461

Murthy DNP, Bulmer M, Eccleston JA (Dec. 2004) Weibull model selection for reliability modeling. Rel Eng Syst Safety 86(3):257–267

Nazaripouya H, Wang B, Wang Y, Chu P, Pota HR, Gadh R (2016) Univariate Time Series Prediction of Solar Power Using a Hybrid Wavelet-ARMA-NARX Prediction Method. In: 2016 IEEE/PES Transmission and Distribution Conference and Exposition (T&D), Dallas

Ni D, Benoit C, Foggia G, Bésanger Y, Senior Member IEEE, Wurt F (Jan. 2016) Neural network-based model Design for Short-Term Load Forecast in distribution systems. IEEE Trans Power Syst 31(1):72–81

Non-Cooperative Game Model Applied to an Advanced Metering Infrastructure for Non-Technical Loss Screening in Micro-Distribution Systems. IEEE Transactions on Smart Grid, vol. 5, no. 5, 2014: 2468–2469

Papadopoulos PN, Guo T, Milanović JV (2018) Probabilistic framework for online identification of dynamic behavior of power systems with renewable generation. IEEE Trans Power Syst , vol. 33, pp. 45-54

PR Newswire. (2014). “World Loses $89.3 Billion to Electricity Theft Annually, $58.7 Billion in Emerging Markets.” [Online]. Available: http://www.prnewswire.com/news-releases/world-loses-893-billion-to-electricity-theft-annually-587-billion-in-emerging-markets-300006515.html , Accessed on: Jul. 2015

Qiu J, Wang H, Lin D, He B, Zhao W, Wei X (2016) Nonparameteric Regression-based Failure Rate Model for Electric Power Equipment Using Lifecycle Data. In: 2016 IEEE/PES Transmission and Distribution Conference and Exposition (T&D), Dallas

Reinhardt A, Reinhardt D (2016) Detecting Anomalous Electrical Appliance Behavior based on Motif Transition Likelihood Matrices. In: 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm), Sydney, NSW, Australia, Sydney, NSW, Australia

Roya A, Cruz a RMO, Sabourina R, Cavalcanti GDC (2018) A study on combining dynamic selection and data preprocessing for imbalance learning. Neurocomputing 286:179–192

Sagiroglu S, Terzi R, Canbay Y, Colak I (2016) Big Data Issues in Smart Grid Systems. In: 2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA), Birmingham, pp 20–23

Shah Z, Anwar A, Mahmood AN, Tari Z, Zomaya AY (2017) A Spatio-temporal Data Summarization Paradigm for Real-time Operation of Smart Grid. IEEE Transactions on Big Data PP(99)

Sheng G, Hou H, Jiang X, Chen Y (2018) A novel association rule mining method of big data for power transformer state parameters based on probabilistic graph model. IEEE Transactions on Smart Grid, vol. 9, pp. 695-702

Shi H, Xu M, Li R (2017) Deep learning for household load forecasting – a novel pooling deep RNN. IEEE Transactions on Smart Grid, https://doi.org/10.1109/TSG.2017.2686012

Singh S, Yassine A (2017) Mining energy consumption behavior patterns for households in smart grid. IEEE Transactions on Emerging Topics in Computing

Singh S, Majumdar A (2017) Deep Sparse Coding for Non-intrusive Load Monitoring. IEEE Transactions on Smart Grid

Siryani J, Tanju B, Eveleighi TJ (2017) A machine learning decision-support system improves the internet of things’ smart meter operations. Accident Analysis and Prediction, volume 4:1056–1066

SmartGrids European Tech. Platform, Strategic Deployment Document for Europe’s Electricity Networks of the Future 6 (2010) [hereinafter E.U. SmartGrids SDD]

Sultanem F (1991) Using appliance signatures for monitoring residential loads at meter panel level. IEEE Transaction on Power Delivery 6(4)

Sun H, Wang Z, Wang J, Huang Z, Carrington NL, Liao J (2016) Data-driven power outage detection by social sensors. IEEE Transactions on Smart Grid 7(5):2516–2524

Swetapadma A, Yadav A (2016) Data-mining-based fault during power swing identification in power transmission system. IET Science, Measurement & Technology 10(2):130–139

Tang Y, Ten C-W, Wang C, Parker G (2015) Extraction of energy information from analog meters using image processing. IEEE Transactions on Smart Grid 6(4)

Teng Z, Yan Z, Dongxia Z (2014) Application Technology of big Data in smart distribution grid and its Prospect analysis. Power System Technology 38(12):3305–3312

Tong X, Kang C, Xia Q (2016) Smart metering load data compression based on load feature identification. IEEE Transactions on Smart Grid 7(5):2414–2422

Verdú SV, García MO, Senabre C, Marín AG, Franco FJG (2006) Classification filtering and identification of electrical customer load patterns through the use of self-organizing maps. IEEE Trans Power Syst 21(4):1672–1682

Wang B, Member BF, Wang Y, Liu H, Liu Y (2016b) Power system transient stability assessment based on big data and the Core vector machine. IEEE Transactions on Smart Grid 7(5):2561–2570

Wang Jian, Xiaofu Xiong, Ning Zhou, Zhe Li, Wei Wang. Early warning method for transmission line galloping based on SVM and AdaBoost bi-level classifiers. IET Generation, Transmission & Distribution (Volume: 10, Issue: 14, 11 4 2016a): 3499–3507

Wang X, McArthur S, Strachan S, Kirkwood J, Paisley B (2017a) A data analytic approach fault diagnosis and prognosis for distribution automation. IEEE Transactions on Smart Grid

Wang Y, Chen Q, Kang C, Xia Q, Luo M (2017b) Sparse and redundant representation-based smart meter data compression and pattern extraction. IEEE Trans Power Syst 32(3):2142–2151

Wei L, Zhang D, Wang X, Liu D, Wu Q. Power System Transient Stability Analysis Based on Random Matrix Theory. Proceedings of the CSEE,36 ;18 pp: 4854–4863, 2016

Wenbin W, Peng M (2017) A Data Mining Approach Combining K-Means Clustering with Bagging Neural Network for Short-term Wind Power Forecasting. IEEE Internet of Things Journal 4(4):2327–4662

Wenhao P, Zhe D, Yanping Z, Jun L (2016) An analytical method for intelligent electricity use pattern with demand response. In: 2016 China International Conference on Electricity Distribution (CICED), Xi’an

Xu X, He X, Ai Q, Qiu Caiming. A Correlation Analysis Method for Operation Status of Distribution Network Based on Random Matrix Theory. Power System Technology, Vol. 40 No. 3, pp: 781–790, 2016

Yang M, Lin Y, Han X (2015) Probabilistic Wind Generation Forecast Based on Sparse Bayesian Classification and Dempster-Shafer Theory. In: 2015 IEEE Industry Applications Society Annual Meeting, Addison

Ye R, Suganthan PN, Srikanth N (2016) A novel empirical mode decomposition with support vector regression for wind speed forecasting. IEEE Transactions on Neural Networks and Learning Systems 27(8):1793–1798

Yu C (2002) Pan Heping. Business intelligence and its key technology Application Research of Computers 9:14–16

Zanetti M, Jamhour E, Pellenz M, Penna M, Zambenedetti V, Chueiri I (2017) A tunable fraud detection system for advanced metering infrastructure using short-lived patterns. IEEE Transactions on Smart Grid

Zhan T-S, Chen S-J, Kao C-C, Kuo C-L, Chen J-L, Lin C-H (2016) Non-technical loss and power blackout detection under advanced metering infrastructure using a cooperative game based inference mechanism. IET Gener Transm Distrib 10(4):873–882

Zhang D, Li S, Sun M, O’Neill Z (July 2016b) An optimal and learning-based demand response and home energy management system. IEEE Transactions on Smart Grid 7(4):1790–1801

Zhang J, Chung CY, Wang Z, Zheng X (April 2016a) Instantaneous electromechanical dynamics monitoring in smart transmission grid. IEEE Transactions on Industrial Informatics 12(2):844–852

Zhang Y, Yan X, Dong ZY, Zhao X, Wong KP (Oct. 2017) Intelligent early warning of power system dynamic insecurity Risk_Toward optimal accuracy-earliness tradeoff. IEEE Transactions on Industrial Informatics 13(5):2544–2554

Zhang Zhen. Smart Grid in America and Europe: Similar Desires, Different Approaches. Public Utilities Fortnightly, 149, 1, 2011

Zhao J, Zhang G, Das K, Korres GN, Manousakis NM, Sinha AK, He Z (2016) Power System Real-Time Monitoring by Using PMU-based Robust State Estimation Method. IEEE Transactions on Smart Grid 7(1):300–309

Zhao T, Ziqiang Z, Yan Z, Ping L, Yingjie T. Spatio-temporal analysis and forecasting of distributed PV systems Diffusion_a Case study of shanghai using A data-driven approach. IEEE Access 5: 5135–5148, 2017

Zhong S, Tam K-S (May 2012) A frequency domain approach to characterize and Anlyze load profiles. IEEE Trans Power Syst 27(2):857–865

Zhu L, Chao L, Dong ZY, Hong C (2017) Imbalance learning machine-based power system short-term voltage stability assessment. IEEE Transactions on Industrial Informatics 13(5):2533–2543

Zhu Ting, Sheng Xiao, Qingquan Zhang, Yu Gu, Ping Yi, and Yanhua Li, “Emergent Technologies in big Data Sensing: a survey”, International Journal of Distributed Sensor Networks, Volume 2015, Article ID 902982

Zico Kolter J, Batra S, Ng AY (2010) Energy Disaggregation via Discriminative Sparse Coding. In: NIPS’10 Proceedings of the 23rd International Conference on Neural Information Processing Systems, vol 1, Vancouver, pp 1153–1161

Zikopoulos P, C. Eaton, Understanding big data: analytics for Enterprise class Hadoop and streaming data, McGraw-hill Education, 2011