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Published in: The Journal of Supercomputing 4/2016

01-04-2016

Handling big data: research challenges and future directions

Authors: I. Anagnostopoulos, S. Zeadally, E. Exposito

Published in: The Journal of Supercomputing | Issue 4/2016

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Abstract

Today, an enormous amount of data is being continuously generated in all walks of life by all kinds of devices and systems every day. A significant portion of such data is being captured, stored, aggregated and analyzed in a systematic way without losing its “4V” (i.e., volume, velocity, variety, and veracity) characteristics. We review major drivers of big data today as well the recent trends and established platforms that offer valuable perspectives on the information stored in large and heterogeneous data sets. Then, we present a classification of some of the most important challenges when handling big data. Based on this classification, we recommend solutions that could address the identified challenges, and in addition we highlight cross-disciplinary research directions that need further investigation in the future.

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Footnotes
Literature
1.
2.
go back to reference Madden S (2012) From databases to big data. IEEE Internet Comput 16(3):4–6CrossRef Madden S (2012) From databases to big data. IEEE Internet Comput 16(3):4–6CrossRef
3.
go back to reference Wu X, Zhu X, Wu GQ, Ding W (2014) Data mining with big data. IEEE Trans Knowl Data Eng 26(1):97–107CrossRef Wu X, Zhu X, Wu GQ, Ding W (2014) Data mining with big data. IEEE Trans Knowl Data Eng 26(1):97–107CrossRef
4.
go back to reference Gantz J, Reinsel D (2011) Extracting value from chaos. IDC iView, pp 1–12 Gantz J, Reinsel D (2011) Extracting value from chaos. IDC iView, pp 1–12
5.
go back to reference Banaee H, Ahmed MU, Loutfi A (2013) Data mining for wearable sensors in health monitoring systems: a review of recent trends and challenges. Sensors 13(12):17472–17500CrossRef Banaee H, Ahmed MU, Loutfi A (2013) Data mining for wearable sensors in health monitoring systems: a review of recent trends and challenges. Sensors 13(12):17472–17500CrossRef
6.
go back to reference 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 47:98–115CrossRef 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 47:98–115CrossRef
7.
go back to reference Kwon O, Lee N, Shin B (2014) Data quality management, data usage experience and acquisition intention of big data analytics. Int J Inf Manag 34(3):387–394CrossRef Kwon O, Lee N, Shin B (2014) Data quality management, data usage experience and acquisition intention of big data analytics. Int J Inf Manag 34(3):387–394CrossRef
10.
go back to reference Chen H, Compton S, Hsiao O (2013) DiabeticLink: a health big data system for patient empowerment and personalized healthcare, vol 8040. In: Smart health. Springer, Berlin, pp 71–83 Chen H, Compton S, Hsiao O (2013) DiabeticLink: a health big data system for patient empowerment and personalized healthcare, vol 8040. In: Smart health. Springer, Berlin, pp 71–83
11.
go back to reference O’Driscoll A, Daugelaite J, Sleator RD (2013) Big data. Hadoop and cloud computing in genomics. J Biomed Inf 46(5):774–781 O’Driscoll A, Daugelaite J, Sleator RD (2013) Big data. Hadoop and cloud computing in genomics. J Biomed Inf 46(5):774–781
15.
go back to reference Kambatla K, Kollias G, Kumar V, Grama A (2014) Trends in big data analytics. J Parallel Distrib Comput 74(7):2561–2573 ISSN 0743–7315CrossRef Kambatla K, Kollias G, Kumar V, Grama A (2014) Trends in big data analytics. J Parallel Distrib Comput 74(7):2561–2573 ISSN 0743–7315CrossRef
16.
go back to reference Atzeni P, Bugiotti F, Rossi L (2014) Uniform access to NoSQL systems. Inf Syst 43:117–133 ISSN 0306–4379CrossRef Atzeni P, Bugiotti F, Rossi L (2014) Uniform access to NoSQL systems. Inf Syst 43:117–133 ISSN 0306–4379CrossRef
18.
go back to reference Owen S, Anil R, Dunning T, Friedman E (2011) Mahout in action. Manning Publications Co, USA ISBN: 9781935182689 Owen S, Anil R, Dunning T, Friedman E (2011) Mahout in action. Manning Publications Co, USA ISBN: 9781935182689
19.
go back to reference Prakashbhai PA, Pandey HM (2014) Inference patterns from Big Data using aggregation, filtering and tagging—a survey. In: 5th international conference The next generation information technology summit (confluence), September 2014, pp 66–71 Prakashbhai PA, Pandey HM (2014) Inference patterns from Big Data using aggregation, filtering and tagging—a survey. In: 5th international conference The next generation information technology summit (confluence), September 2014, pp 66–71
20.
go back to reference Hu H, Wen Y, Chua TS, Li X (2014) Toward scalable systems for big data analytics: a technology tutorial. IEEE Access 2:652–687CrossRef Hu H, Wen Y, Chua TS, Li X (2014) Toward scalable systems for big data analytics: a technology tutorial. IEEE Access 2:652–687CrossRef
21.
go back to reference Che D, Safran M, Peng Z (2013) From big data to big data mining: challenges, issues, and opportunities. In: Lecture notes in computer science, vol 7827, pp 1–15 Che D, Safran M, Peng Z (2013) From big data to big data mining: challenges, issues, and opportunities. In: Lecture notes in computer science, vol 7827, pp 1–15
22.
go back to reference Tan W, Blake MB, Saleh I, Dustdar S (2013) Social-network-sourced big data analytics. IEEE Internet Comput 7(5):62–69CrossRef Tan W, Blake MB, Saleh I, Dustdar S (2013) Social-network-sourced big data analytics. IEEE Internet Comput 7(5):62–69CrossRef
23.
go back to reference Lin J, Kolcz A (2012) Large-scale machine learning at twitter. In: Proceedings of the 2012 ACM SIGMOD international conference on management of data (SIGMOD ’12). ACM, New York, pp 793–804 Lin J, Kolcz A (2012) Large-scale machine learning at twitter. In: Proceedings of the 2012 ACM SIGMOD international conference on management of data (SIGMOD ’12). ACM, New York, pp 793–804
24.
go back to reference Liu J, Liu F, Ansari N (2014) Monitoring and analyzing big traffic data of a large-scale cellular network with Hadoop. IEEE Netw 28(4):32–39CrossRef Liu J, Liu F, Ansari N (2014) Monitoring and analyzing big traffic data of a large-scale cellular network with Hadoop. IEEE Netw 28(4):32–39CrossRef
25.
go back to reference Marchal S, Francois J, State R, Engel T (2014) Phishstorm: detecting phishing with streaming analytics. IEEE Trans Netw Serv Manag 11(4):458–471CrossRef Marchal S, Francois J, State R, Engel T (2014) Phishstorm: detecting phishing with streaming analytics. IEEE Trans Netw Serv Manag 11(4):458–471CrossRef
26.
go back to reference Ma C, Zhang HH, Wang X (2014) Machine learning for Big Data analytics in plants. Trends Plant Sci 19(12):798–808CrossRef Ma C, Zhang HH, Wang X (2014) Machine learning for Big Data analytics in plants. Trends Plant Sci 19(12):798–808CrossRef
27.
go back to reference Chandola V, Sukumar SR, Schryver JC (2013) Knowledge discovery from massive healthcare claims data. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining (KDD ’13). ACM, New York, pp 1312–1320 Chandola V, Sukumar SR, Schryver JC (2013) Knowledge discovery from massive healthcare claims data. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining (KDD ’13). ACM, New York, pp 1312–1320
29.
go back to reference Reda K, Febretti A, Knoll A, Aurisano J, Leigh J, Johnson AE, Papka ME, Hereld M (2013) Visualizing large, heterogeneous data in hybrid-reality environments. IEEE Comput Graph Appl 33(4):38–48CrossRef Reda K, Febretti A, Knoll A, Aurisano J, Leigh J, Johnson AE, Papka ME, Hereld M (2013) Visualizing large, heterogeneous data in hybrid-reality environments. IEEE Comput Graph Appl 33(4):38–48CrossRef
30.
go back to reference Philip Chen CL, Zhang CY (2014) Data-intensive applications, challenges, techniques and technologies: a survey on Big Data. Inf Sci 275:314–347CrossRef Philip Chen CL, Zhang CY (2014) Data-intensive applications, challenges, techniques and technologies: a survey on Big Data. Inf Sci 275:314–347CrossRef
31.
go back to reference Jagadish HV, Gehrke J, Labrinidis A, Papakonstantinou Y, Patel JM, Ramakrishnan R, Shahabi C (2014) Big data and its technical challenges. Commun ACM 57(7):86–94CrossRef Jagadish HV, Gehrke J, Labrinidis A, Papakonstantinou Y, Patel JM, Ramakrishnan R, Shahabi C (2014) Big data and its technical challenges. Commun ACM 57(7):86–94CrossRef
32.
go back to reference Labrinidis A, Jagadish HV (2012) Challenges and opportunities with big data. Proc VLDB Endow 5(12):2032–2033CrossRef Labrinidis A, Jagadish HV (2012) Challenges and opportunities with big data. Proc VLDB Endow 5(12):2032–2033CrossRef
33.
go back to reference Buneman P, Khanna S, Tan W (2000) Data provenance: some basic issues. In: Proceedings of foundations of software technology and theoretical computer science (FST TCS 2000). LNCS, vol 1974, pp 87–93 Buneman P, Khanna S, Tan W (2000) Data provenance: some basic issues. In: Proceedings of foundations of software technology and theoretical computer science (FST TCS 2000). LNCS, vol 1974, pp 87–93
34.
go back to reference Price S, Flach PA (2013) A Higher-order data flow model for heterogeneous Big Data. In: 2013 IEEE international conference on big data, October 2013, pp 569–574 Price S, Flach PA (2013) A Higher-order data flow model for heterogeneous Big Data. In: 2013 IEEE international conference on big data, October 2013, pp 569–574
35.
go back to reference Xindong W, Xingquan Z, Gong-Qing W, Wei D (2014) Data mining with big data. IEEE Trans Knowl Data Eng 26(1):97–107CrossRef Xindong W, Xingquan Z, Gong-Qing W, Wei D (2014) Data mining with big data. IEEE Trans Knowl Data Eng 26(1):97–107CrossRef
36.
go back to reference Davis K, Patterson D (2012) Ethics of big data, O’Reilly. ISBN 978-1-4493-1179-7 Davis K, Patterson D (2012) Ethics of big data, O’Reilly. ISBN 978-1-4493-1179-7
37.
38.
go back to reference Michael K, Miller KW (2013) Big data: new opportunities and new challenges. IEEE Comput 46(6):22–24CrossRef Michael K, Miller KW (2013) Big data: new opportunities and new challenges. IEEE Comput 46(6):22–24CrossRef
39.
go back to reference Kupwade PH, Seshadri R (2014) Big data security and privacy issues in healthcare. In: 2014 IEEE international congress on big data, pp 762–765 Kupwade PH, Seshadri R (2014) Big data security and privacy issues in healthcare. In: 2014 IEEE international congress on big data, pp 762–765
40.
go back to reference Volkovs M, Fei C, Szlichta J, Miller RJ (2014) Continuous data cleaning. In: 2014 IEEE 30th international conference on data engineering (ICDE), pp 244–255 Volkovs M, Fei C, Szlichta J, Miller RJ (2014) Continuous data cleaning. In: 2014 IEEE 30th international conference on data engineering (ICDE), pp 244–255
41.
go back to reference Wang J, Song Z, Li Q, Yu J, Chen F (2014) Semantic-based intelligent data clean framework for big data. In: 2014 international conference on security, pattern analysis, and cybernetics (SPAC), pp 448–453 Wang J, Song Z, Li Q, Yu J, Chen F (2014) Semantic-based intelligent data clean framework for big data. In: 2014 international conference on security, pattern analysis, and cybernetics (SPAC), pp 448–453
42.
go back to reference Stonebraker M, Bruckner D, Ilyas I, Beskales G, Cherniack M, Zdonik S, Pagan A, Xu S (2013) Data curation at scale: the data tamer system. In: Proceedings of biennial ACM conference on innovative data systems research (CIDR’13), Alisomar Stonebraker M, Bruckner D, Ilyas I, Beskales G, Cherniack M, Zdonik S, Pagan A, Xu S (2013) Data curation at scale: the data tamer system. In: Proceedings of biennial ACM conference on innovative data systems research (CIDR’13), Alisomar
43.
go back to reference Bansal SK (2014) Towards a semantic extract-transform-load (ETL) framework for big data integration. In: 2014 IEEE international congress on big data (BigData Congress), pp 522–529 Bansal SK (2014) Towards a semantic extract-transform-load (ETL) framework for big data integration. In: 2014 IEEE international congress on big data (BigData Congress), pp 522–529
44.
go back to reference Kadadi A, Agrawal R, Nyamful C, Atiq R (2014) Challenges of data integration and interoperability in big data. In: 2014 IEEE international conference on big data (Big Data), pp 38–40 Kadadi A, Agrawal R, Nyamful C, Atiq R (2014) Challenges of data integration and interoperability in big data. In: 2014 IEEE international conference on big data (Big Data), pp 38–40
45.
go back to reference Dong XL, Srivastava D (2013) Big data integration. In: 2013 IEEE 29th international conference on data engineering (ICDE), pp 1245–1248 Dong XL, Srivastava D (2013) Big data integration. In: 2013 IEEE 29th international conference on data engineering (ICDE), pp 1245–1248
46.
go back to reference Sowe SK, Zettsu K (2013) The architecture and design of a community-based cloud platform for curating big data. In: 2013 international conference on cyber-enabled distributed computing and knowledge discovery (CyberC), pp 171–178 Sowe SK, Zettsu K (2013) The architecture and design of a community-based cloud platform for curating big data. In: 2013 international conference on cyber-enabled distributed computing and knowledge discovery (CyberC), pp 171–178
47.
go back to reference O’Leary DE (2014) Embedding AI and crowdsourcing in the big data lake. IEEE Intell Syst 29(5):70–73CrossRef O’Leary DE (2014) Embedding AI and crowdsourcing in the big data lake. IEEE Intell Syst 29(5):70–73CrossRef
48.
go back to reference Dean J, Ghemawat S (2008) MapReduce: simplified data processing on large clusters. ACM Commun 51(1):107–113CrossRef Dean J, Ghemawat S (2008) MapReduce: simplified data processing on large clusters. ACM Commun 51(1):107–113CrossRef
49.
go back to reference Chang F, Dean J, Ghemawat S, Hsieh WC, Wallach DA, Burrows M, Chandra T, Fikes A, Gruber RE (2008) Bigtable: a distributed storage system for structured data. ACM Trans Comput Syst 26(2):1–26 Chang F, Dean J, Ghemawat S, Hsieh WC, Wallach DA, Burrows M, Chandra T, Fikes A, Gruber RE (2008) Bigtable: a distributed storage system for structured data. ACM Trans Comput Syst 26(2):1–26
50.
go back to reference Kumar KA, Quamar A, Deshpande A, Khuller S (2014) SWORD: workload-aware data placement and replica selection for cloud data management systems. VLDB J 23(6):845–870CrossRef Kumar KA, Quamar A, Deshpande A, Khuller S (2014) SWORD: workload-aware data placement and replica selection for cloud data management systems. VLDB J 23(6):845–870CrossRef
51.
go back to reference Wang Z, Zhu W, Chen X, Sun L, Liu J, Chen M, Cui P, Yang S (2013) Propagation-based social-aware multimedia content distribution. ACM Trans Multimed Comput Commun Appl (TOMM) 9(1):52:1–52:20 Wang Z, Zhu W, Chen X, Sun L, Liu J, Chen M, Cui P, Yang S (2013) Propagation-based social-aware multimedia content distribution. ACM Trans Multimed Comput Commun Appl (TOMM) 9(1):52:1–52:20
52.
go back to reference Wang Z, Zhu W, Chen M, Sun L, Yang S (2015) CPCDN: content delivery powered by context and user intelligence. IEEE Trans Multimed 17(1):92–103CrossRef Wang Z, Zhu W, Chen M, Sun L, Yang S (2015) CPCDN: content delivery powered by context and user intelligence. IEEE Trans Multimed 17(1):92–103CrossRef
53.
go back to reference Menglan H, Jun L, Yang W, Veeravalli B (2014) Practical resource provisioning and caching with dynamic resilience for cloud-based content distribution networks. IEEE Trans Parall Distrib Syst 25(8):2169–2179CrossRef Menglan H, Jun L, Yang W, Veeravalli B (2014) Practical resource provisioning and caching with dynamic resilience for cloud-based content distribution networks. IEEE Trans Parall Distrib Syst 25(8):2169–2179CrossRef
54.
go back to reference Suto K, Nishiyama H, Kato N, Nakachi T, Fujii T, Takahara A (2014) Toward integrating overlay and physical networks for robust parallel processing architecture. IEEE Netw 28(4):40–45CrossRef Suto K, Nishiyama H, Kato N, Nakachi T, Fujii T, Takahara A (2014) Toward integrating overlay and physical networks for robust parallel processing architecture. IEEE Netw 28(4):40–45CrossRef
55.
go back to reference Jiayi L, Rosenberg C, Simon G, Texier G (2014) Optimal delivery of rate-adaptive streams in underprovisioned networks. IEEE J Select Areas Commun 32(4):706–718CrossRef Jiayi L, Rosenberg C, Simon G, Texier G (2014) Optimal delivery of rate-adaptive streams in underprovisioned networks. IEEE J Select Areas Commun 32(4):706–718CrossRef
56.
go back to reference Fiore S, D’Anca A, Elia D, Palazzo C, Foster I, Williams D, Aloisio G (2014) Ophidia: a full software stack for scientific data analytics. In: 2014 international conference on high performance computing & simulation (HPCS), pp 343–350 Fiore S, D’Anca A, Elia D, Palazzo C, Foster I, Williams D, Aloisio G (2014) Ophidia: a full software stack for scientific data analytics. In: 2014 international conference on high performance computing & simulation (HPCS), pp 343–350
57.
go back to reference Bhandarkar SM, Arabnia HR, Smith JW (1995) A reconfigurable architecture for image processing and computer vision. Int J Pattern Recognit Artif Intell (IJPRAI) 9(2):201–229. (Special issue on VLSI Algorithms and Architectures for Computer Vision. Image Processing, Pattern Recognition and AI) Bhandarkar SM, Arabnia HR, Smith JW (1995) A reconfigurable architecture for image processing and computer vision. Int J Pattern Recognit Artif Intell (IJPRAI) 9(2):201–229. (Special issue on VLSI Algorithms and Architectures for Computer Vision. Image Processing, Pattern Recognition and AI)
58.
go back to reference Heinze T, Pappalardo V, Jerzak Z, Fetzer C (2014) Auto-scaling techniques for elastic data stream processing. In: 2014 IEEE 30th international conference on data engineering workshops (ICDEW), pp 296–302 Heinze T, Pappalardo V, Jerzak Z, Fetzer C (2014) Auto-scaling techniques for elastic data stream processing. In: 2014 IEEE 30th international conference on data engineering workshops (ICDEW), pp 296–302
59.
go back to reference Hsiang HW, Tse CY, Chien MW (2014) Multiple two-phase data processing with mapreduce. In: 2014 IEEE 7th international conference on cloud computing (CLOUD), pp 352–359 Hsiang HW, Tse CY, Chien MW (2014) Multiple two-phase data processing with mapreduce. In: 2014 IEEE 7th international conference on cloud computing (CLOUD), pp 352–359
60.
go back to reference Arif Wani M, Arabnia HR (2003) Parallel edge-region-based segmentation algorithm targeted at reconfigurable multi-ring network. J Supercomput 25(1):43–63CrossRefMATH Arif Wani M, Arabnia HR (2003) Parallel edge-region-based segmentation algorithm targeted at reconfigurable multi-ring network. J Supercomput 25(1):43–63CrossRefMATH
61.
go back to reference Mokhtari R, Stumm M (2014) BigKernel—high performance CPU-GPU communication pipelining for big data-style applications. In: 2014 IEEE 28th international parallel and distributed processing symposium, pp 819–828 Mokhtari R, Stumm M (2014) BigKernel—high performance CPU-GPU communication pipelining for big data-style applications. In: 2014 IEEE 28th international parallel and distributed processing symposium, pp 819–828
62.
go back to reference Chatterjee A, Radhakrishnan S, Sekharan CN (2014) Connecting the dots: triangle completion and related problems on large data sets using GPUs. In: 2014 IEEE international conference on big data (Big Data), pp 1–8 Chatterjee A, Radhakrishnan S, Sekharan CN (2014) Connecting the dots: triangle completion and related problems on large data sets using GPUs. In: 2014 IEEE international conference on big data (Big Data), pp 1–8
63.
go back to reference Shahar Y (1997) A framework for knowledge-based temporal abstraction. Elsevier Artif Intell 90(1–2):79–133CrossRefMATH Shahar Y (1997) A framework for knowledge-based temporal abstraction. Elsevier Artif Intell 90(1–2):79–133CrossRefMATH
64.
go back to reference Tajer A, Veeravalli VV, Poor HV (2014) Outlying sequence detection in large data sets: a data-driven approach. IEEE Signal Process Mag 31(5):44–56CrossRef Tajer A, Veeravalli VV, Poor HV (2014) Outlying sequence detection in large data sets: a data-driven approach. IEEE Signal Process Mag 31(5):44–56CrossRef
65.
go back to reference Bhandarkar SM, Arabnia HR (1995) The REFINE multiprocessor: theoretical properties and algorithms. Elsevier Parall Comput 21(11):1783–1806CrossRef Bhandarkar SM, Arabnia HR (1995) The REFINE multiprocessor: theoretical properties and algorithms. Elsevier Parall Comput 21(11):1783–1806CrossRef
66.
go back to reference Bhandarkar SM, Arabnia HR (1995) The Hough transform on a reconfigurable multi-ring network. J Parall Distrib Comput 24(1):107–114CrossRef Bhandarkar SM, Arabnia HR (1995) The Hough transform on a reconfigurable multi-ring network. J Parall Distrib Comput 24(1):107–114CrossRef
67.
go back to reference Arabnia HR, Bhandarkar SM (1996) Parallel stereocorrelation on a reconfigurable multi-ring network. J Supercomput 10(3):243–270CrossRefMATH Arabnia HR, Bhandarkar SM (1996) Parallel stereocorrelation on a reconfigurable multi-ring network. J Supercomput 10(3):243–270CrossRefMATH
68.
go back to reference Vafopoulos M, Meimaris M, Anagnostopoulos I, Papantoniou A, Xidias I, Alexiou G, Vafeiadis G, Klonaras M, Loumos V (2015) Public spending as LOD: the case of Greece. Seman Web Interoperabil Usabil Applicabil Seman Web 6(2):155–164 Vafopoulos M, Meimaris M, Anagnostopoulos I, Papantoniou A, Xidias I, Alexiou G, Vafeiadis G, Klonaras M, Loumos V (2015) Public spending as LOD: the case of Greece. Seman Web Interoperabil Usabil Applicabil Seman Web 6(2):155–164
69.
go back to reference Ekbia H, Mattioli M, Kouper I, Arave G, Ghazinejad A, Bowman T, Suri VR, Tsou A, Weingart S, Sugimoto CR (2014) Big data, bigger dilemmas: a critical review. J Assoc Inf Sci Technol. Wiley, New York Ekbia H, Mattioli M, Kouper I, Arave G, Ghazinejad A, Bowman T, Suri VR, Tsou A, Weingart S, Sugimoto CR (2014) Big data, bigger dilemmas: a critical review. J Assoc Inf Sci Technol. Wiley, New York
70.
go back to reference Smith M, Szongott C, Henne B, von Voigt G (2012) Big data privacy issues in public social media. In: 6th IEEE international conference on digital ecosystems technologies (DEST), pp 1–6 Smith M, Szongott C, Henne B, von Voigt G (2012) Big data privacy issues in public social media. In: 6th IEEE international conference on digital ecosystems technologies (DEST), pp 1–6
71.
go back to reference Zhang X, Dou W, Pei J, Nepal S, Yang C, Liu C, Chen J (2015) Proximity-aware local-recoding anonymization with mapreduce for scalable big data privacy preservation in cloud. IEEE Trans Comput 64(8):2293–2307MathSciNetCrossRef Zhang X, Dou W, Pei J, Nepal S, Yang C, Liu C, Chen J (2015) Proximity-aware local-recoding anonymization with mapreduce for scalable big data privacy preservation in cloud. IEEE Trans Comput 64(8):2293–2307MathSciNetCrossRef
Metadata
Title
Handling big data: research challenges and future directions
Authors
I. Anagnostopoulos
S. Zeadally
E. Exposito
Publication date
01-04-2016
Publisher
Springer US
Published in
The Journal of Supercomputing / Issue 4/2016
Print ISSN: 0920-8542
Electronic ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-016-1677-z

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