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The state of the art and taxonomy of big data analytics: view from new big data framework

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Abstract

Big data has become a significant research area due to the birth of enormous data generated from various sources like social media, internet of things and multimedia applications. Big data has played critical role in many decision makings and forecasting domains such as recommendation systems, business analysis, healthcare, web display advertising, clinicians, transportation, fraud detection and tourism marketing. The rapid development of various big data tools such as Hadoop, Storm, Spark, Flink, Kafka and Pig in research and industrial communities has allowed the huge number of data to be distributed, communicated and processed. Big data applications use big data analytics techniques to efficiently analyze large amounts of data. However, choosing the suitable big data tools based on batch and stream data processing and analytics techniques for development a big data system are difficult due to the challenges in processing and applying big data. Practitioners and researchers who are developing big data systems have inadequate information about the current technology and requirement concerning the big data platform. Hence, the strengths and weaknesses of big data technologies and effective solutions for Big Data challenges are needed to be discussed. Hence, due to that, this paper presents a review of the literature that analyzes the use of big data tools and big data analytics techniques in areas like health and medical care, social networking and internet, government and public sector, natural resource management, economic and business sector. The goals of this paper are to (1) understand the trend of big data-related research and current frames of big data technologies; (2) identify trends in the use or research of big data tools based on batch and stream processing and big data analytics techniques; (3) assist and provide new researchers and practitioners to place new research activity in this domain appropriately. The findings of this study will provide insights and knowledge on the existing big data platforms and their application domains, the advantages and disadvantages of big data tools, big data analytics techniques and their use, and new research opportunities in future development of big data systems.

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Notes

  1. http://blogs.sas.com/content/hls/2011/10/21/how-big-is-big-data-in-healthcare/

  2. https://mahout.apache.org/.

  3. http://storm.incubator.apache.org/.

  4. http://archive.ics.uci.edu/ml

  5. https://spark.apache.org/graphx/.

References

  • Agerri R, Artola X, Beloki Z, Rigau G, Soroa A (2015) Big data for natural language processing: a streaming approach. Knowl-Based Syst 79:36–42

    Google Scholar 

  • Ahmad A, Paul A, Rathore MM (2016) An efficient divide-and-conquer approach for big data analytics in machine-to-machine communication. Neurocomputing 174:439–453

    Google Scholar 

  • Ahmad A, Khan M, Paul A, Din S, Rathore MM, Jeon G, Choi GS (2017) Toward modeling and optimization of features selection in big data based social internet of things. Future Gener Comput Syst

  • Ai W, Li K, Li K (2017) An effective hot topic detection method for microblog on spark. Appl Soft Comput

  • Amato F, Moscato V, Picariello A, Piccialli F (2017) SOS: a multimedia recommender system for online social networks. Future Gener Comput Syst

  • Apiletti D, Baralis E, Cerquitelli T, Garza P, Pulvirenti F, Michiardi P (2017) A parallel MapReduce algorithm to efficiently support itemset mining on high dimensional data. Big Data Res 10:53–69

    Google Scholar 

  • Arias J, Gamez JA, Puerta JM (2017) Learning distributed discrete Bayesian network classifiers under MapReduce with Apache spark. Knowl-Based Syst 117:16–26

    Google Scholar 

  • Aufaure MA, Chiky R, Curé O, Khrouf H, Kepeklian G (2016) From business intelligence to semantic data stream management. Future Gener Comput Syst 63:100–107

    Google Scholar 

  • Babar M, Arif F (2017) Smart urban planning using big data analytics to contend with the interoperability in Internet of Things. Future Gener Comput Syst 77:65–76

    Google Scholar 

  • Barba-González C, García-Nieto J, Nebro AJ, Cordero JA, Durillo JJ, Navas-Delgado I, Aldana-Montes JF (2017) jMetalSP: a framework for dynamic multi-objective big data optimization. Appl Soft Comput

  • Basanta-Val P, Fernández-García N, Wellings AJ, Audsley NC (2015) Improving the predictability of distributed stream processors. Future Gener Comput Syst 52:22–36

    Google Scholar 

  • Basanta-Val, P., Fernández-García, N., & Sánchez-Fernández, L. (2017). Predictable remote invocations for distributed stream processing. Future Gener Comput Syst

  • Batarseh FA, Latif EA (2016) Assessing the quality of service using big data analytics: with application to healthcare. Big Data Res 4:13–24

    Google Scholar 

  • Bechini A, Marcelloni F, Segatori A (2016) A MapReduce solution for associative classification of big data. Inf Sci 332:33–55

    Google Scholar 

  • Bei Z, Yu Z, Luo N, Jiang C, Xu C, Feng S (2018) Configuring in-memory cluster computing using random forest. Future Gener Comput Syst 79:1–15

    Google Scholar 

  • Bharti SK, Vachha B, Pradhan RK, Babu KS, Jena SK (2016) Sarcastic sentiment detection in tweets streamed in real time: a big data approach. Digital Commun Netw 2(3):108–121

    Google Scholar 

  • Carcillo F, Dal Pozzolo A, Le Borgne YA, Caelen O, Mazzer Y, Bontempi G (2018) Scarff: a scalable framework for streaming credit card fraud detection with spark. Inf Fusion 41:182–194

    Google Scholar 

  • Castiglione A, Colace F, Moscato V, Palmieri F (2017) CHIS: a big data infrastructure to manage digital cultural items. Future Gener Comput Syst

  • Chen CP, Zhang CY (2014) Data-intensive applications, challenges, techniques and technologies: a survey on big data. Inf Sci 275:314–347

    Google Scholar 

  • Chen G, Wu S, Wang Y (2015) The evolvement of big data systems: from the perspective of an information security application, 65–73

    Google Scholar 

  • Chen H, Li T, Cai Y, Luo C, Fujita H (2016) Parallel attribute reduction in dominance-based neighborhood rough set. Inf Sci 373:351–368

    MATH  Google Scholar 

  • Chen Y, Crespi N, Ortiz AM, Shu L (2017) Reality mining: a prediction algorithm for disease dynamics based on mobile big data. Inf Sci 379:82–93

    Google Scholar 

  • De Maio C, Fenza G, Loia V, Orciuoli F (2017) Distributed online temporal fuzzy concept analysis for stream processing in smart cities. J Parallel Distrib Comput 110:31–41

    MATH  Google Scholar 

  • Del Río S, López V, Benítez JM, Herrera F (2014) On the use of MapReduce for imbalanced big data using random forest. Inf Sci 285:112–137

    Google Scholar 

  • Ding L, Liu Y, Han B, Zhang S, Song B (2017) HB-file: an efficient and effective high-dimensional big data storage structure based on US-ELM. Neurocomputing 261:184–192

    Google Scholar 

  • Eiras-Franco C, Bolón-Canedo V, Ramos S, González-Domínguez J, Alonso-Betanzos A, Touriño J (2016) Multithreaded and spark parallelization of feature selection filters. J Comput Sci 17:609–619

    Google Scholar 

  • Elkano M, Galar M, Sanz J, Bustince H (2017) CHI-BD: a fuzzy rule-based classification system for big data classification problems. Fuzzy Sets Syst

  • Elsebakhi E, Lee F, Schendel E, Haque A, Kathireason N, Pathare T, Al-Ali R (2015) Large-scale machine learning based on functional networks for biomedical big data with high performance computing platforms. J Comput Sci 11:69–81

    MathSciNet  Google Scholar 

  • Fernández-Rodríguez JY, Álvarez-García JA, Fisteus JA, Luaces MR, Magaña VC (2017) Benchmarking real-time vehicle data streaming models for a Smart City. Inf Syst 72:62–76

    Google Scholar 

  • Ferranti A, Marcelloni F, Segatori A, Antonelli M, Ducange P (2017) A distributed approach to multi-objective evolutionary generation of fuzzy rule-based classifiers from big data. Inf Sci 415:319–340

    Google Scholar 

  • Fonseca A, Cabral B (2017) Prototyping a GPGPU neural network for deep-learning big data analysis. Big Data Res 8:50–56

    Google Scholar 

  • Gadiraju KK, Verma M, Davis KC, Talaga PG (2016) Benchmarking performance for migrating a relational application to a parallel implementation. Future Gener Comput Syst 63:148–156

    Google Scholar 

  • Genuer R, Poggi JM, Tuleau-Malot C, Villa-Vialaneix N (2017) Random forests for big data. Big Data Res 9:28–46

    Google Scholar 

  • Ghadiri N, Ghaffari M, Nikbakht MA (2016) BigFCM: fast, precise and scalable FCM on Hadoop. arXiv preprint arXiv:1605.03047

  • Guo J, Song B, Yu FR, Yan Z, Yang LT (2017) Object detection among multimedia big data in the compressive measurement domain under mobile distributed architecture. Future Gener Comput Syst 76:519–527

    Google Scholar 

  • He W, Wu H, Yan G, Akula V, Shen J (2015) A novel social media competitive analytics framework with sentiment benchmarks. Inf Manag 52(7):801–812

    Google Scholar 

  • Hernández ÁB, Perez MS, Gupta S, Muntés-Mulero V (2017) Using machine learning to optimize parallelism in big data applications. Future Gener Comput Syst

  • Hidalgo N, Wladdimiro D, Rosas E (2017) Self-adaptive processing graph with operator fission for elastic stream processing. J Syst Softw 127:205–216

    Google Scholar 

  • Higashino WA, Capretz MA, Bittencourt LF (2016) CEPSim: modelling and simulation of complex event processing systems in cloud environments. Future Gener Comput Syst 65:122–139

    Google Scholar 

  • Huang S, Wang B, Qiu J, Yao J, Wang G, Yu G (2016) Parallel ensemble of online sequential extreme learning machine based on map reduce. Neurocomputing 174:352–367

    Google Scholar 

  • Huang CS, Tsai MF, Huang PH, Su LD, Lee KS (2017) Distributed asteroid discovery system for large astronomical data. J Netw Comput Appl 93:27–37

    Google Scholar 

  • Iqbal R, Doctor F, More B, Mahmud S, Yousuf U (2017) Big data analytics and computational intelligence for cyber–physical systems: recent trends and state of the art applications. Future Gener Comput Syst

  • Jayasena KPN, Li L, Xie Q (2017) Multi-modal multimedia big data analyzing architecture and resource allocation on cloud platform. Neurocomputing 253:135–143

    Google Scholar 

  • Jiang R, Lu R, Choo KKR (2018) Achieving high performance and privacy-preserving query over encrypted multidimensional big metering data. Future Gener Comput Syst 78:392–401

    Google Scholar 

  • Karunaratne P, Karunasekera S, Harwood A (2017) Distributed stream clustering using micro-clusters on Apache Storm. J Parallel Distrib Comput 108:74–84

    Google Scholar 

  • Kousiouris G, Akbar A, Sancho J, Ta-shma P, Psychas A, Kyriazis D, Varvarigou T (2018) An integrated information lifecycle management framework for exploiting social network data to identify dynamic large crowd concentration events in smart cities applications. Future Gener Comput Syst 78:516–530

    Google Scholar 

  • Kovalchuk SV, Krotov E, Smirnov PA, Nasonov DA, Yakovlev AN (2018) Distributed data-driven platform for urgent decision making in cardiological ambulance control. Future Gener Comput Syst 79:144–154

    Google Scholar 

  • Kranjc J, Orač R, Podpečan V, Lavrač N, Robnik-Šikonja M (2017) ClowdFlows: online workflows for distributed big data mining. Future Gener Comput Syst 68:38–58

    Google Scholar 

  • Kumar M, Rath SK (2015) Classification of microarray using MapReduce based proximal support vector machine classifier. Knowl-Based Syst 89:584–602

    Google Scholar 

  • Liang Y, Wu D, Liu G, Li Y, Gao C, Ma ZJ, Wu W (2016) Big data-enabled multiscale serviceability analysis for aging bridges. Digit Commun Netw 2(3):97–107

    Google Scholar 

  • Lin W, Dou W, Zhou Z, Liu C (2015) A cloud-based framework for home-diagnosis service over big medical data. J Syst Softw 102:192–206

    Google Scholar 

  • Maillo J, Ramírez S, Triguero I, Herrera F (2017) kNN-IS: an iterative spark-based design of the k-nearest neighbors classifier for big data. Knowl-Based Syst 117:3–15

    Google Scholar 

  • Manco G, Ritacco E, Rullo P, Gallucci L, Astill W, Kimber D, Antonelli M (2017) Fault detection and explanation through big data analysis on sensor streams. Expert Syst Appl 87:141–156

    Google Scholar 

  • Manogaran G, Varatharajan R, Lopez D, Kumar PM, Sundarasekar R, Thota C (2017) A new architecture of internet of things and big data ecosystem for secured smart healthcare monitoring and alerting system. Future Gener Comput Syst

  • Maté A, Peral J, Ferrández A, Gil D, Trujillo J (2016) A hybrid integrated architecture for energy consumption prediction. Future Gener Comput Syst 63:131–147

    Google Scholar 

  • Mavridis I, Karatza H (2017) Performance evaluation of cloud-based log file analysis with Apache Hadoop and Apache Spark. J Syst Softw 125:133–151

    Google Scholar 

  • Mestre DG, Pires CES, Nascimento DC (2017) Towards the efficient parallelization of multi-pass adaptive blocking for entity matching. J Parallel Distrib Comput 101:27–40

    Google Scholar 

  • Mohapatra SK, Sahoo PK, Wu SL (2016a) Big data analytic architecture for intruder detection in heterogeneous wireless sensor networks. J Netw Comput Appl 66:236–249

    Google Scholar 

  • Mohapatra SK, Sahoo PK, Wu SL (2016b) Big data analytic architecture for intruder detection in heterogeneous wireless sensor networks. J Netw Comput Appl 66:236–249

    Google Scholar 

  • Nair LR, Shetty SD, Shetty SD (2017) Applying spark based machine learning model on streaming big data for health status prediction. Comput Electr Eng

  • Najafabadi MK, Mahrin MNR (2016) A systematic literature review on the state of research and practice of collaborative filtering technique and implicit feedback. Artif Intell Rev 45(2):167–201

    Google Scholar 

  • Najafabadi MK, Mohamed AH, Mahrin MNR (2017) A survey on data mining techniques in recommender systems. Soft Comput, 1–28

  • Najafabadi MK, Mahrin MNR, Chuprat S, Sarkan HM (2017b) Improving the accuracy of collaborative filtering recommendations using clustering and association rules mining on implicit data. Comput Hum Behav 67:113–128

    Google Scholar 

  • Nghiem PP, Figueira SM (2016) Towards efficient resource provisioning in MapReduce. J Parallel Distrib Comput 95:29–41

    Google Scholar 

  • Nguyen T, Larsen ME, O’Dea B, Nguyen DT, Yearwood J, Phung D, Christensen H (2017) Kernel-based features for predicting population health indices from geocoded social media data. Decis Support Syst 102:22–31

    Google Scholar 

  • Oneto L, Fumeo E, Clerico G, Canepa R, Papa F, Dambra C, Anguita D (2017) Train delay prediction systems: a big data analytics perspective. Big Data Res

  • Pedersen E, Bongo LA (2017) Large-scale biological meta-database management. Future Gener Comput Syst 67:481–489

    Google Scholar 

  • Peralta D, García S, Benitez JM, Herrera F (2017) Minutiae-based fingerprint matching decomposition: methodology for big data frameworks. Inf Sci 408:198–212

    Google Scholar 

  • Plimpton SJ, Shead T (2014) Streaming data analytics via message passing with application to graph algorithms. J Parallel Distrib Comput 74(8):2687–2698

    Google Scholar 

  • Prajapati DJ, Garg S, Chauhan NC (2017) MapReduce based multilevel consistent and inconsistent association rule detection from big data using interestingness measures. Big Data Res 9:18–27

    Google Scholar 

  • Pulgar-Rubio F, Rivera-Rivas AJ, Pérez-Godoy MD, González P, Carmona CJ, del Jesus MJ (2017) MEFASD-BD: multi-objective evolutionary fuzzy algorithm for subgroup discovery in big data environments-A MapReduce solution. Knowl-Based Syst 117:70–78

    Google Scholar 

  • Qian J, Lv P, Yue X, Liu C, Jing Z (2015) Hierarchical attribute reduction algorithms for big data using MapReduce. Knowl-Based Syst 73:18–31

    Google Scholar 

  • Rahman MN, Esmailpour A, Zhao J (2016) Machine learning with big data an efficient electricity generation forecasting system. Big Data Res 5:9–15

    Google Scholar 

  • Rathore MM, Ahmad A, Paul A, Rho S (2016) Urban planning and building smart cities based on the internet of things using big data analytics. Comput Netw 101:63–80

    Google Scholar 

  • Rathore MM, Paul A, Ahmad A, Chilamkurthi N, Hong WH, Seo H (2017) Real-time secure communication for Smart City in high-speed big data environment. Future Gener Comput Syst

  • Ruan G, Zhang H (2017) Closed-loop big data analysis with visualization and scalable computing. Big Data Res 8:12–26

    Google Scholar 

  • Sahal R, Khafagy MH, Omara FA (2017) Exploiting coarse-grained reused-based opportunities in Big Data multi-query optimization. J Comput Sci

  • Singh H, Bawa S (2017) A MapReduce-based scalable discovery and indexing of structured big data. Future Gener Comput Syst 73:32–43

    Google Scholar 

  • Singh K, Guntuku SC, Thakur A, Hota C (2014) Big data analytics framework for peer-to-peer botnet detection using random forests. Inf Sci 278:488–497

    Google Scholar 

  • Singh S, Garg R, Mishra PK (2017) Performance optimization of MapReduce-based Apriori algorithm on Hadoop cluster. Comput Electr Eng

  • Spivak A, Razumovskiy A, Nasonov D, Boukhanovsky A, Redice A (2018) Storage tier-aware replicative data reorganization with prioritization for efficient workload processing. Future Gener Comput Syst 79:618–629

    Google Scholar 

  • Sun D, Zhang G, Yang S, Zheng W, Khan SU, Li K (2015) Re-stream: real-time and energy-efficient resource scheduling in big data stream computing environments. Inf Sci 319:92–112

    MathSciNet  Google Scholar 

  • Tennant M, Stahl F, Rana O, Gomes JB (2017) Scalable real-time classification of data streams with concept drift. Future Gener Comput Syst 75:187–199

    Google Scholar 

  • Triguero I, Peralta D, Bacardit J, García S, Herrera F (2015) MRPR: a MapReduce solution for prototype reduction in big data classification. Neurocomputing 150:331–345

    Google Scholar 

  • Tripathy BK, Mittal D (2016) Hadoop based uncertain possibilistic kernelized c-means algorithms for image segmentation and a comparative analysis. Appl Soft Comput 46:886–923

    Google Scholar 

  • Tsai CW, Liu SJ, Wang YC (2017) A parallel metaheuristic data clustering framework for cloud. J Parallel Distrib Comput

  • Um JH, Lee S, Kim TH, Jeong CH, Song SK, Jung H (2016) Semantic complex event processing model for reasoning research activities. Neurocomputing 209:39–45

    Google Scholar 

  • Vennila V, Kannan AR (2016) Symmetric matrix-based predictive classifier for big data computation and information sharing in cloud. Comput Electr Eng 56:831–841

    Google Scholar 

  • Wang H, Belhassena A (2017) Parallel trajectory search based on distributed index. Inf Sci 388:62–83

    Google Scholar 

  • Wang B, Huang S, Qiu J, Liu Y, Wang G (2015) Parallel online sequential extreme learning machine based on MapReduce. Neurocomputing 149:224–232

    Google Scholar 

  • Wang H, Xu Z, Fujita H, Liu S (2016) Towards felicitous decision making: an overview on challenges and trends of big data. Inf Sci 367:747–765

    Google Scholar 

  • Wang J, He C, Liu Y, Tian G, Peng I, Xing J, Wang FL (2017a) Efficient alarm behavior analytics for telecom networks. Inf Sci 402:1–14

    Google Scholar 

  • Wang, Y., Geng, S., & Gao, H. (2017). A proactive decision support method based on deep reinforcement learning and state partition. Knowl-Based Syst

  • Xia Y, Chen J, Lu X, Wang C, Xu C (2016) Big traffic data processing framework for intelligent monitoring and recording systems. Neurocomputing 181:139–146

    Google Scholar 

  • Yuan J, Chen M, Jiang T, Li T (2017) Complete tolerance relation based parallel filling for incomplete energy big data. Knowl-Based Syst 132:215–225

    Google Scholar 

  • Zhang F, Cao J, Khan SU, Li K, Hwang K (2015) A task-level adaptive MapReduce framework for real-time streaming data in healthcare applications. Future Gener Comput Syst 43:149–160

    Google Scholar 

  • Zhang CY, Chen CP, Chen D, Ng KT (2016) MapReduce based distributed learning algorithm for restricted Boltzmann machine. Neurocomputing 198:4–11

    Google Scholar 

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Acknowledgements

This work is supported under the university Research Entity Initiatives Grant (600-RMI/DANA 5/3/REI (16/2015)). We thank IRMI (Institute of Research, Management and Innovation), UiTM for their continuous support.

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  1. Maryam Khanian Najafabadi

    Present address: Faculty of Information Technology, INTI International University & Colleges, Nilai, Malaysia

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  1. Advanced Analytics Engineering Centre, Faculty Computer and Mathematical Sciences, Universiti Teknologi MARA (UiTM), Shah Alam, Selangor, Malaysia

    Azlinah Mohamed, Yap Bee Wah, Ezzatul Akmal Kamaru Zaman & Ruhaila Maskat

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Appendix: recent contributions on big data

Appendix: recent contributions on big data

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Table 4 Focus of recent contributions on big data
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Mohamed, A., Najafabadi, M.K., Wah, Y.B. et al. The state of the art and taxonomy of big data analytics: view from new big data framework. Artif Intell Rev 53, 989–1037 (2020). https://doi.org/10.1007/s10462-019-09685-9

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