Big data applications in operations/supply-chain management: A literature review

Computers and Industrial Engineering

Volumn 101, Issue , 2016, Pages 528-543

  Addo Tenkorang, Richard ^a   Helo, Petri T ^a  

^a UNIVERSITY OF VAASA   (Finland)

Author keywords

Big data applications and analysis; Cloud computing; Internet of Things (IoT); Master database management; Operations supply chain management

Indexed keywords

APPLICATION PROGRAMS; CLOUD COMPUTING; DATA ACQUISITION; DIGITAL STORAGE; INTERNET OF THINGS; SUPPLY CHAIN MANAGEMENT;

BIG DATA APPLICATIONS; IMPLEMENTATION PROCESS; INDUSTRIAL MANAGERS; INTERNET OF THINGS (IOT); LITERATURE REVIEWS; MANAGEMENT DECISIONS; MASTER DATABASE; RESEARCH APPROACH;

BIG DATA;

EID: 85000765414     PISSN: 03608352     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cie.2016.09.023     Document Type: Article

References (121)

1. Addo-Tenkorang, R., Helo, P. T., Shamsuzzoha, A., Ehrs, M., & Phuong, D. (2012). Logistics & supply chain management tracking networks: Data-management system integration/interfacing issues. In Technology Management for Emerging Technologies (PICMET), 2012 Proceedings of PICMET’12: IEEE Xplore (pp. 2198–2206).
2. Affelt, A. (2015). The accidental data scientist: Big data applications and opportunities for librarians and information professionals. Information Today, Incorporated.
3. Akusok, A., Bjork, K.M., Miche, Y., Lendasse, A., High performance extreme learning machines: A complete toolbox for big data applications. Digital Object Identifier. IEEE. 3 (2015), 1011–1025.
4. Algarin, A., De la R., & Demurjain, S. A. (2013). An approach to facilitate security assurance for information sharing and exchange in big data applications. Chapter 4. Emerging Trends by ICT Security, Edited by Babak Akhgar and Arabnia Hamid. British Library Cataloguing-in-Publication Data. ISBN: 978-0-12-411474-6.
5. Amer-Yahia, S., Doan, A.H., Kleinberg, J., Crowds, clouds, and algorithms: Exploring the human side of big data applications. SIGMOD ’10 proceedings of the 2010 ACM SIGMOD international conference on management of data, 2010, ACM, New York, NY, USA, 10.1145/1807167.1807341 ISBN: 978-1-4503-0032-2, Order Number: 405102.
6. Aron, J. L. & Niemann, B. (2013). Sharing best practices for the implementation of big data applications in government and science communities. In IEEE international conference on big data (pp. 8–10). Washington, DC, USA.
7. Atzori, L., Iera, A., Morabito, G., The internet of things: A survey. Computer Networks 54:15 (2010), 2787–2805.
8. Baesens, A., Analytics in a big data world: The essential guide to data science and its applications. 2014, John Wiley & Sons, Hoboken, New Jersey.
9. Barahmand, S. & Ghandeharizadeh, S. (2014). Benchmarking correctness of operations in big data applications. In IEEE 22nd international symposium on modelling, analysis & simulation of computer and telecommunication systems (pp. 483–485). Paris, France.
10. Barbierato, E., Gribaudo, M., & Iacono, M. (2013). Modelling apache hive based applications in big data architectures. In Value Tools ’13 proceedings of the 7th international conference on performance evaluation methodologies and tools (pp. 30–38). Brussels, Belgium, Belgium.
11. Barbierato, E., Gribaudo, M., Iacono, M., Performance evaluation of NoSQL big-data applications using multi-formalism models. Future Generation Computer Systems 37 (2014), 345–353.
12. Bardhan, S., Menasce, D., A contention aware hybrid evaluator for schedulers of big data applications in computer clusters. Big data (big data), 2014 IEEE international conference, 2014, IEEE, 11–19.
13. Bhosale, N.S., Pande, S.S., A survey on recommendation systems for big data applications. Data Mining and Knowledge Engineering 7:1 (2015), 42–44.
14. Bi, Z., Cochran, D., Big data analytics with applications. Journal of Management Analytics 1:4 (2014), 249–265.
15. Boos, D., Guenter, H., Grote, G., Kinder, H., Controllable accountabilities: The internet of things and its challenges for organizations. Behaviour and Information Technology 32:5 (2013), 449–467.
16. Borovick, L. & Villars, R. L. (2012). The critical role of the network in big data applications. IDC White Paper.
17. Bu, Y., Borkar, V., Xu, G., Carey, M.J., A bloat-aware design for big data applications. ISMM ’13 Proceedings of the international symposium on memory management, 2013, ACM, New York, NY, USA 978-1-4503-2100-6, 119–130, 10.1145/2464157.2466485.
18. Casado, R. (2013). Lambdoop, a framework for easy development of big data applications. In NoSQL Matters 2013.
19. Castiglione, A., Gribaudo, M., Iacono, M., Palmieri, F., Modelling performances of concurrent big data applications. Software: Practice and Experience 45:8 (2015), 1127–1144.
20. Chan, Y., Gray, I., Wellings, A., & Audsley, N. (2014a). Exploiting multicore architectures in big data applications: The JUNIPER approach. The JUNIPER approach. In Proceedings of MULTIPROG 2014: programmability issues for heterogeneous multicores, 2014.
21. Chan, Y., Wellings, A., Gray, I., Audsley, N., On the locality of java 8 streams in real-time big data applications. Proceedings of the 12th international workshop on java technologies for real-time and embedded systems, 2014, ACM, 20.
22. Chen, J., Roth, P. C., & Chen, Y. (2013). Using pattern-models to guide SSD deployment for big data applications in HPC systems. In IEEE international conference on big data (pp. 332–337). Santa Clara, CA, USA.
23. Chen, M., Mao, S., Zhang, Y., & Leung, V. C. (2014). Big data: related technologies, challenges and future prospects. Chapter 2. Springer Briefs in Computer Science, http://dx.doi.org/10.1007/978-3-319-06245-7__2, < http://www.springer.com/978-3-319-06244-0>.
24. Chen, C.L.P., Zhang, C.Y., Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences 275 (2014), 314–347.
25. Cheptsov, A., HPC in big data age: An evaluation report for java-based data-intensive applications implemented with Hadoop and OpenMPI. Proceedings of the 21st European MPI users’ group meeting, 2014, ACM, 175.
26. Chute, C. G. (2012). Obstacles and options for big-data applications in biomedicine: The role of standards and normalizations. In IEEE international conference on bioinformatics and biomedicine (BIBM). Philadelphia, PA, USA.
27. Costa, P., Donnelly, A., Rowstron, A., & O'Shea, G. (2012). Camdoop: Exploiting in-network aggregation for big data applications. In NSDI’12 Proceedings of the 9th USENIX conference on networked systems design and implementation. USENIX Association Berkeley, CA, USA.
28. Dean, J., Ghemawat, S., MapReduce: Simplified data processing on large clusters. Communications of the ACM 51:1 (2008), 107–113.
29. Dou, W., Zhang, X., Liu, J., Chen, J., HireSome-II: Towards privacy-aware cross-cloud service composition for big data applications. IEEE Transactions on Parallel and Distributed Systems 26:2 (2015), 455–466.
30. Du, D., Li, A., Zhang, L., Li, H., Review on the applications and the handling techniques of big data in Chinese realty enterprises. Annals of Data Science 1:3–4 (2014), 339–357.
31. Düben, P., Schlachter, J., Yenugula, S., Augustine, J., Enz, C., Palem, K., & Palmer, T.N. (2015). Opportunities for energy efficient computing: A study of inexact general purpose processors for high-performance and big-data applications. In DATE ’15 Proceedings of the 2015 design, automation & test in Europe conference & exhibition (pp. 764–769). EDA Consortium San Jose, CA, USA.
32. Dugan, P., Zollweg, J., Glotin, H., Popescu, M., Risch D., LeCun, Y., & Clark, C. (2014). High performance computer acoustic data accelerator (HPC-ADA): A new system for exploring marine mammal acoustics for big data applications. In ICML 2014, workshop on machine learning for bioacoustics. Beijing, China.
33. Eichelberger, H., Schmid, K., Resource-optimizing adaptation for big data applications. Proceedings of the 18th international software product line conference: Companion volume for workshops, demonstrations and tools, Vol. 2, 2014, ACM, 10–11.
34. Feller, E., Ramakrishnan, L., Morin, C., Performance and energy efficiency of big data applications in cloud environments: A hadoop case study. Journal of Parallel and Distributed Computing 79–80 (2015), 80–89.
35. Ferrarons, J., Adhana, M., Colmenares, C., Pietrowska, S., Bentayeb, F., Darmont, J., PRIMEBALL: A parallel processing framework benchmark for big data applications in the cloud. Performance characterization and benchmarking, 2014, Springer International Publishing, 109–124.
36. Fox, G. C., Jha, S., Qiu, J., & Luckow, A. (2014). Towards an understanding of facets and exemplars of big data applications. In Proceedings of workshop: Twenty years of Beowulf.
37. Gantz, J., Reinsel, D., Extracting value from chaos. IDC, 2011, 1–12.
38. Gartner (2013). Gartner says the internet of things installed base will grow to 26 billion units by 2020. < http://www.gartner.com/newsroom/id/2636073> (Accessed on 24.10.2015).
39. Geerts, G.L., O'Leary, D.E., A supply chain of things: The EAGLET ontology for highly visible supply chains. Decision Support Systems 63:1 (2014), 3–22.
40. Gobble, M.M., The next big thing in innovation. Research Technology Management 56:1 (2013), 64–66.
41. Grant, I. (2009). Introducing apache mahout: Scalable, commercial-friendly machine learning for building intelligent applications. IBM Corporation. < http://www.crisismanagement.com.cn/templates/blue/down_list/llzt_dsj/Data-intensive%20applications,%20challenges,%20techniques%20and%20technologies%20A%20survey%20on%20Big%20Data.pdf> (Accessed on 12.6.2016).
42. Grobelnik, M. (2012). Big data tutorial < http://videolectures.net/eswc2012_grobelnik_big_data/?q=big%20data> (Assessed on 26.05.2015).
43. Gunasekaran, A., Ngai, E.W.T., Information systems in supply chain integration and management. European Journal of Operational Research 159 (2004), 269–295.
44. Hauang, G.Q., Zhong, R.Y., Tsui, K.L., Special issue on ‘Big data for service and manufacturing supply-chain management’. Editorial. International Journal of Production Economics 165 (2015), 172–173.
45. Hayler, A., Big data applications bring new database choices, challenges. , 2012 (Accessed on 22.10.2015).
46. Hong, S. (2013). Social network world and big data applications. Social network world and big data applications. Powerbook, Seoul, 2013, pp. 235–238.
47. Hu, W. C., & Kaabouch, N. (2014). Big data management, technologies, and applications. IGI global, information science reference (pp. 1–509).
48. Intel IT Centre – Peer Research (2012). Big data analytics: Intel's IT manager survey on how organizations are using big data. pp. 1–25.
49. Janusz, W., Implementation of the big data concept in organizations – Possibilities, impediments and challenges. IEEE, 2013, 985–989.
50. Jardak, C., Mähönen, P., Riihijärvi, J., Spatial big data and wireless networks: Experiences, applications, and research challenges. Network, IEEE 28:4 (2014), 26–31.
51. Jeseke, M., Grüner, M., & Weiß, F. (2013). Big data in logistics: A DHL perspective on how to move beyond the hype. White Paper; DHL Customer solutions & innovation, presented by Martine Wegner, Vice President Solutions & Innovation, 53844 Troisdorf, Germany.
52. Jia, Z., Zhou, R., Zhu, C., Wang, L., Gao, W., Shi, Y., Zhan, J., & Zhang, L. (2014). The implications of diverse applications and scalable data sets in benchmarking big data systems. Editors: Tilmann Rabl Meikel Poess Chaitanya Baru Hans-Arno Jacobsen, Springer Book Chapter: Specifying Big Data Benchmarks, Vol. 8163 of the series Lecture Notes in Computer Science. pp. 44–59.
53. Jiang, H., Ren, Z.L., Nie, L.M., Software engineering issues in mobile big data applications. Communication of the CCF 10:3 (2014), 24–28.
54. Jie, C. (2012). Cloud storage technology and applications for big data. ZTE Technology Journal < http://en.cnki.com.cn/Article_en/CJFDTOTAL-ZXTX201206016.htm> (Accessed on 22.10.2015).
55. Karuppiah, E.K., Kok, Y.K., Singh, K., A middleware framework for programmable multi-GPU-based big data applications. GPU computing and applications, 2015, Springer, Singapore, 187–206.
56. Kaushik, P. (2015). Internet of things (IoT) and real-time analytics – A marriage made in heaven. Techopedia < https://www.techopedia.com/2/31434/trends/big-data/internet-of-things-iot-and-real-time-analytics-a-marriage-made-in%20heaven?utm_campaign=newsletter&utm_medium=best&utm_source=10142015> (Accessed on 14.10.2015).
57. Kees, A., Oberländer, A., Röglinger, M., Rosemann, M., Understanding the internet of things: A conceptualisation of business-to-thing (B2T) interactions. The proceedings of twenty-third European Conference on Information Systems (ECIS), 2015, Münster, Germany, 1–16.
58. Kim, G.H., Trimi, S., Chung, J.H., Big-data applications in the government sector. Communications of the ACM 57:3 (2014), 78–85.
59. Kortuem, G., Kwasar, F., Fitton, D., Sundrammoorthy, V., Smart objects as building blocks for the Internet of Things. IEEE Internet Computing 14:1 (2010), 44–51.
60. Laub, B., Wang, C., Schwan, K., & Huneycutt, C. (2014). Towards combining online & offline management for big data applications. In The proceedings of the 11th International Conference on Autonomic Computing (ICAC ’14). June 18–20, 2014 (pp. 121–127). Philadelphia, PA, USA. ISBN 978-1-931971-11-9.
61. Li, K.C., Jiang, H., Yang, L.T., Cuzzocrea, A., (eds.) Big data: Algorithms, analytics, and applications, 2015, CRC Press.
62. Liu, Z., Research of performance test technology for big data applications. Information and automation (ICIA), 2014 IEEE international conference on, 2014, IEEE, 53–58.
63. Liu, C., Zhang, X., Liu, C., Yang, Y., Ranjan, R., Georgakopoulos, D., & Chen, J. (2013). An iterative hierarchical key exchange scheme for secure scheduling of big data applications in cloud computing. Trust, security and privacy in computing and communications (TrustCom). In 12th IEEE International conference on trust, security and privacy in computing and communications. Melbourne, VIC. Australia.
64. Lu, P., Zhang, L., Liu, X., Yao, J., Zhu, Z., Highly efficient data migration and backup for big data applications in elastic optical inter-data-centre networks. Network, IEEE 29:5 (2015), 36–42.
65. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A.H., Big data: The next frontier for innovation, competition, and productivity. 2011, McKinsey Global Institute.
66. Marcos, D.A., Rodrigo, N.C., Silvia, B., Marco, A.S.N., Rajkumar, B., Big data computing and clouds: Trends and future directions. Journal of Parallel and Distributed Computing 79–80 (2015), 3–15.
67. Mashayekhy, L., Nejad, M.M., Grosu, D., Zhang, Q., Shi, W., Energy-aware scheduling of MapReduce jobs for big data applications. IEEE Transactions on Parallel and Distributed Systems 26:10 (2014), 2720–2733.
68. Mayer-Schönberger, V. & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. Eamon Dolan/Houghton Mifflin Harcourt.
69. McKinsey Global Institute, Disruptive technologies: Advances that will transform life, business, and the global economy. , 2013 (Accessed on 24.10.2015).
70. Meng, S., Dou, W., Zhang, W., Chen, J., KASR: A keyword-aware service recommendation method on MapReduce for big data applications. IEEE Transactions on Parallel and Distributed Systems 25:12 (2014), 3221–3231.
71. Milan, P. (2015). Use big data to help procurement ’make a real difference < http://www.4cassociates.com>.
72. Min, C., Shiwen, M., Yunhao, L., Big data: A survey. Mobile Network Applications 19 (2014), 171–209.
73. Muhtaroglu, F. C. P., Tubitak-Bilgem, G. T., Demir, S., Obali, M., & Girgin, C. (2013). Business model canvas perspective on big data applications. In IEEE international conference on big data (pp. 32–37), Silicon Valley, CA. USA.
74. Nadungodage, C.H., Xia, Y., Lee, J.J., Lee, M., Park, C.S., GPU accelerated item-based collaborative filtering for big-data applications. IEEE International Conference on Big Data, 2013, 175–180.
75. Ngai, E.W.T., Gunasekaran, A., A review for mobile commerce research and applications. Decision Support Systems 43:1 (2007), 3–15.
76. Ngai, E.W.T., Hu, Y., Wong, Y.H., Chen, Y., Sun, X., The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems 50:3 (2011), 559–569, 10.1016/j.dss.2010.08.006.
77. Ngai, E.W.T., Moon, K.K.L., Riggins, F.J., Yi, C.Y., RFID research: An academic literature review (19952005) and future research directions. International Journal of Production Economics 112:2 (2008), 510–520.
78. Nguyen, K., Wang, K., Bu, Y., Fang, L., Hu, J., Xu, G., Facade: A compiler and runtime for (almost) object-bounded big data applications. ACM SIGARCH Computer Architecture News – ASPLOS’15 43:1 (2015), 675–690.
79. Park, S., Bong, K., Shin, D., Lee, J., Choi, S., & Yoo, H. J. (2015). 4.6 A1. 93TOPS/W scalable deep learning/inference processor with tetra-parallel MIMD architecture for big-data applications. In Solid-State Circuits Conference-(ISSCC), 2015 IEEE International (pp. 1–3). IEEE. Report to the president big data and privacy: A technological perspective (2014) < www.whitehouse.gov/ostp/pcast>.
80. Pentaho Business Analytics (2012) < http://www.pentaho.com/explore/pentaho-business-analytics/>.
81. Popescu, P. G., Sluşanschi, E. I., Iancu, V., & Pop, F. (2014). A new upper bound for Shannon entropy. A novel approach in modeling of big data applications. Concurrency and Computation: Practice and Experience.
82. Rajesh, K.V.N., Big data analytics: Applications and benefits. IUP Journal of Information Technology, 9(4), 2013, 41.
83. Richard, L. V., Matthew, E., & Carl, W.O. (2011). Big data: What it is and why you should care. IDC.
84. Robinson, D.P. & Tappenden, R. E. H. (2015). A flexible ADMM algorithm for big data applications. arXiv preprint arXiv: 1502.04391.
85. Rosemann, M., The internet of things – New digital capital in the hand of customers. Business Transformation Journal 9:1 (2014), 6–14.
86. Ryu, S., Book review: Big data management, technologies, and applications. Healthcare Information Research 20:1 (2014), 76–78.
87. Sagirouglu, S., Sinanc, G., Big data: A review. IEEE, 2013, 42–47.
88. Scardapane, S., Wang, D., & Panella, M. (2015). A decentralized training algorithm for Echo State Networks in distributed big data applications. Neural Networks. http://dx.doi.org/10.1016/j.neunet.2015.07.006 (in press).
89. Seo, D., Shin, S., Kim, Y., Jung, H., Song, S.K., Dynamic Hilbert Curve-based B+-Tree to manage frequently updated data in big data applications. Life Science Journal, 11(10), 2014.
90. Seref, S., Duygu, S., Big data: a review. 2013 international conference on collaboration technologies and systems, 2013, San Diego, CA, USA, 42–47.
91. Suresh, A., Gibson, G., & Ganger, G. (2012). Shingled magnetic recording for big data applications. Carnegie Mellon University Parallel Data Lab, Tech. Rep. CMU-PDL-12-105.
92. Sztandera, L. (2014). Computational intelligence in business analytics: Concepts, methods, and tools for big data applications. Pearson Education.
93. Tanakamaru, S., Doi, M., & Takeuchi, K. (2013). Unified solid-state-storage architecture with NAND flash memory and ReRAM that tolerates 32× higher BER for big-data applications. In: IEEE ISSCC Dig Tech Papers (p. 226–267).
94. Team, O. R. (2011). Big data now: Current perspectives from O'Reilly Radar. O'Reilly Media.
95. Uckelmann, D., Harrison, M., Michahelles, F., (eds.) Architecting the internet of things, 2011, Springer, Berlin, Heidelberg.
96. Vanhove, T., Van Seghbroeck, G., Wauters, T., & De Turck, F. (2015). Live data store transformation for optimizing big data applications in cloud environments. In Integrated Network Management (IM), 2015 IFIP/IEEE international symposium on (pp. 1–8). IEEE.
97. Villalpando, L.E.B., April, A., Abran, A., Performance analysis model for big data applications in cloud computing. Journal of Cloud Computing 3:1 (2014), 1–20.
98. Waller, M.A., Fawcett, S.E., Data science, predictive analytics and big data: A revolution that will transform supply chain design and management. Journal of Business Logistics 34:2 (2013), 77–84.
99. Wamba, F.S., Akter, S., Edwards, A., Chopin, G., Gnanzou, D., How ‘Big Data’ can make big impact: Findings from systematic review and longitudinal case study. International Journal of Production Economics, 2015, 10.1016/j.ijpe.2014.12.031 in press.
100. Wang, J., Crawl, D., Altintas, I., Li, W., Big data applications using workflows for data parallel computing. Computing in Science & Engineering 16:4 (2014), 11–21.
101. Wang, G., Ng, T.S., Shaikh, A., Programming your network at run-time for big data applications. Hotsdn ’12 proceedings of the first workshop on hot topics in software defined networks, 2012, ACM, New York, NY, USA 978-1-4503-1477-0, 10.1145/2342441.2342462.
102. Wei, W., Jiang, D., Xiong, J., Chen, M., Exploring opportunities for non-volatile memories in big data applications. Big Data Benchmarks, Performance Optimization, and Emerging Hardware Lecture Notes in Computer Science 8807 (2014), 209–220.
103. Weichselbraun, A., Gindl, S., Scharl, A., Enriching semantic knowledge bases for opinion mining in big data applications. Knowledge-Based Systems 69 (2014), 78–85.
104. Witlox, F., Beyond the data smog. Transport Reviews: A Transnational Transdisciplinary Journal. 35:3 (2015), 245–249, 10.1080/0144164.2015.1036505.
105. Wu, L., Yuan, L., You, J., Survey of large-scale data management systems for big data applications. Journal of Computer Science and Technology 30:1 (2015), 163–183.
106. Xhafa, F., Barolli, L., Barolli, A., Papajorgji, P., (eds.) Modeling and processing for next-generation big-data technologies: With applications and case studies, Vol. 4, 2014, Springer.
107. Xu, L. (2014). Scalable file systems and operating systems support for big data applications. In 9th Parallel Data Storage Workshop (PDSW), 2014 (pp. 25–30). New Orleans, LA. USA.
108. Yuan, L.-Y., Wu, L., You, J.H., & Chi, Y. (2013). Rubato DB: A highly scalable staged grid database system for OLTP and big data applications. In CIKM ’14 Proceedings of the 23rd ACM international conference on conference on information and knowledge management (pp. 1–10). ACM New York, NY, USA.
109. Zahavi, E., Keslassy, I., Kolodny, A., Distributed adaptive routing for big-data applications running on data center networks. Proceeding of ANCS ’12 proceedings of the eighth ACM/IEEE symposium on architectures for networking and communications systems, 2012, ACM, New York, NY, USA, 99–110.
110. Zeiler, K. (2013). Big data in logistics: A DHL perspective on how to move beyond the hype. DHL Customer Solutions & Innovation, 53844 Troisdorf, Germany.
111. Zhao, Y., Wu, J., Liu, C., Dache: A data aware caching for big-data applications using the MapReduce framework. Tsinghua Science and Technology 19:1 (2014), 39–50 ISSN ll1007-02 14ll0 5/10ll.
112. Zhong, R.Y., Huang, G.Q., Lan, S., Dai, Q.Y., Xu, C., Zhang, T., A big data approach for logistics trajectory discovery from RFID-enabled production data. International Journal of Production Economics 165 (2015), 260–272.
113. Zillner, S., Lasierra, N., Faix, W., & Neururer, S. (2014). User needs and requirements analysis for big data healthcare applications. In Proceeding of the 25th European medical informatics conference (MIE 2014). Istanbul, Turkey.
114. Zillner, S., Neururer, S., Technology roadmap development for big data healthcare applications. KI-Künstliche Intelligenz, 2014, 1–11.
115. Zimmermann, A., Pretz, M., Donald, G., Firesmith, G., Ilia, P., & El-Sheikh, E. (2013). Towards service-oriented enterprise architectures for big data applications in the cloud. In Proceedings of the 17th IEEE international enterprise distributed object computing conference workshops. Vancouver, BC, Canada.
116. Zou, H., Yu, Y., Tang, W., & Chen, H.W.M. (2014b). Improving I/O performance with adaptive data compression for big data applications. In Parallel & Distributed Processing Symposium Workshops (IPDPSW), 2014 IEEE International (pp. 1228–1237). Phoenix, AZ, USA. ISBN: 978-1-4799-4117-9.
117. Zou, H., Yu, Y., Tang, W., Chen, H.W.M., Flexanalytics: A flexible data analytics framework for big data applications with I/O performance improvement. Big Data Research 1 (2014), 4–13.
118. Harzing, A. W. (2007). Publish or Perish. Available from < http://www.harzing.com/pop.htm> (Accessed and utilized on 18th October 2015 at 11:24 pm).
119. Kourouthanassis, P., Roussos, G., Developing consumer friendly pervasive retail systems. IEEE Pervasive Computing 2:2 (2003), 32–39.
120. Rui, M. E. & Chunming, R. (2011). Using mahout for clustering Wikipedias latest articles: A comparison between k-means and fuzzy c-means in the cloud. In 2011 IEEE third international conference on cloud computing technology and science (CloudCom) (pp. 565–569).
121. Rui, M. E., Chunming R., & Rui, P. (2011). K-means clustering in the cloud – A mahout test. In 2011 IEEE Workshops of International Conference on Advanced Information Networking and Applications (WAINA) (pp. 514–519).