AI帮你理解科学

AI 生成解读视频

AI抽取解析论文重点内容自动生成视频


pub
生成解读视频

AI 溯源

AI解析本论文相关学术脉络


Master Reading Tree
生成 溯源树

AI 精读

AI抽取本论文的概要总结


微博一下
The authors of this paper have presented a holistic view of Big Data practices and application of Big Data Analytics methods as presented in a normative slice of literature

Critical analysis of Big Data challenges and analytical methods

Journal of Business Research, (2017): 263-286

被引用759|浏览572
下载 PDF 全文
引用
微博一下

摘要

Big Data (BD), with their potential to ascertain valued insights for enhanced decision-making process, have recently attracted substantial interest from both academics and practitioners. Big Data Analytics (BDA) is increasingly becoming a trending practice that many organizations are adopting with the purpose of constructing valuable info...更多

代码

数据

0
简介
  • The magnitude of data generated and shared by businesses, public administrations numerous industrial and not-to-profit sectors, and scientific research, has increased immeasurably (Agarwal & Dhar, 2014).
  • These data include textual content, to multimedia content on a multiplicity of platforms.
  • BD is the artefact of human individual as well as collective intelligence generated and shared mainly through the technological environment, where virtually anything and everything can be documented, measured, and captured digitally, and in so doing transformed into data – a process that Mayer-Schönberger and Cukier (2013) referred to as datafication
重点内容
  • The magnitude of data generated and shared by businesses, public administrations numerous industrial and not-to-profit sectors, and scientific research, has increased immeasurably (Agarwal & Dhar, 2014)
  • In order to respond to each Q1 and, Q2 questions, we reviewed each paper to identify the Big Data (BD) challenges (Q1) and Big Data Analytics (BDA) methods (Q2) at the same time and noted the findings on a spread sheet
  • The authors of this paper have presented a holistic view of BD practices and application of BDA methods as presented in a normative slice of literature
  • Based on the findings from existing research studies, the presented research has sought to analyze, synthesize and present a comprehensive structured analysis on BD and BDA to support the signposting of future research directions
  • The systematic literature review (SLR) methodology adopted demonstrated to be a convenient tool for conducting a descriptive literature reviews, with contributions including the synthesis of core conclusions of the literature, the literature voids, and the formation of a foundation for future research
  • The findings of this structured literature review will assist both BD and BDA academics and practitioners to develop new solutions based on the challenges identified in this paper
方法
  • In an attempt to better understand and provide more detailed insights to the phenomenon of big data and bit data analytics, the authors respond to the special issue call on Big Data and Analytics in Technology and Organizational Resource Management through a SLR methodology as opposed to narrative or descriptive reviews (Tranfield, Denyer, & Smart, 2003; Kitchenham & Charters, 2007; Wang, Gunasekaran, Ngai, & Papadopoulos, 2016)
  • In support of the former approach, Lettieri, Masella, and Radaelli (2009) report that SLR is a rational, transparent and reproducible research methodology for the analysis of extant literature.
  • Analytics that help in anticipating e.g. “What is likely to happen in the future?”
结论
  • The authors of this paper have presented a holistic view of BD practices and application of BDA methods as presented in a normative slice of literature.
  • The SLR methodology adopted demonstrated to be a convenient tool for conducting a descriptive literature reviews, with contributions including the synthesis of core conclusions of the literature, the literature voids, and the formation of a foundation for future research
  • The findings of this structured literature review will assist both BD and BDA academics and practitioners to develop new solutions based on the challenges identified in this paper.
  • It is evident from the review conducted that it has significantly changed the data management landscape with scope for further profound changes
研究对象与分析
journal articles: 433
• Big Data OR Big Data Analytics OR Big Data Analysis AND Challenge OR Challenges OR Barrier OR Barriers OR Obstacle OR Obstacles OR Problem OR Problems OR Impediment OR Impediments AND Technology OR Technologies OR Technique OR Method OR Methods OR Approach OR Approaches. Through using the abovementioned list of keywords and focusing on four subject areas that is business and management, computer science, decision science, and social science; initially 433 journal articles were identified from the Scopus database and relating to articles published during the period from 1996 to 2015. However, from period 1996 until 2002, there were no papers recorded on BD and BDA in these four subject areas

articles: 434
However, from period 1996 until 2002, there were no papers recorded on BD and BDA in these four subject areas. After assessing the 434 articles (from refereed journals), 206 papers were discarded, and finally 227 papers were selected and taken forward for further interrogation. As reflected in Fig. 9, contributors from across the world have made contributions to the BD and BDA area

articles: 227
Some further articles (i.e. 206) were discarded during this stage. At the end of this process, 227 articles were considered for further investigation. • Phase II.3 – For this step, the authors followed the quality criteria matrix as adopted by Pittaway et al (2004)

articles: 227
• Phase II.3 – For this step, the authors followed the quality criteria matrix as adopted by Pittaway et al (2004). In this step, the selected 227 articles were further scanned, searching for both conceptual as well as empirical studies through the criteria highlighted in conditions 6 and 7. By doing so, all articles were grouped into two categories (i.e. BD_CH refers to BD challenges and BDA_MTH refers to BDA methods: ○ Category BD_CH was defined to incorporate all the studies as certainly pertinent because each article either reported or discussed or evaluated the BD challenges

papers: 227
By doing so, all articles were grouped into two categories (i.e. BD_CH refers to BD challenges and BDA_MTH refers to BDA methods: ○ Category BD_CH was defined to incorporate all the studies as certainly pertinent because each article either reported or discussed or evaluated the BD challenges. So for this category all the 227 papers resulted as productive. ○ Category BDA_MTH was defined for those studies that were relevant for extracting information on the types of BDA methods discussed/proposed. After thoroughly analysing the 227 articles, around 115 articles discussed or proposed some form of method for BDA

articles: 227
So for this category all the 227 papers resulted as productive. ○ Category BDA_MTH was defined for those studies that were relevant for extracting information on the types of BDA methods discussed/proposed. After thoroughly analysing the 227 articles, around 115 articles discussed or proposed some form of method for BDA. As a result of the above two categories, all 227 articles were considered applicable for responding to Q1 and Q2

articles: 227
After thoroughly analysing the 227 articles, around 115 articles discussed or proposed some form of method for BDA. As a result of the above two categories, all 227 articles were considered applicable for responding to Q1 and Q2. The applicability assessment was considered as relative, to the degree that the authors' decrees were focused on facets defined within the scope of the review process

articles: 227
The applicability assessment was considered as relative, to the degree that the authors' decrees were focused on facets defined within the scope of the review process. • Phase II.4 – Herein, beginning within the BD_CH category and followed by BDA_MTH category, the full-text version of 227 articles were thoroughly read by the first and second author. In order to save time, both the authors divided the articles among themselves and reviewed them for BD_CH (i.e. here the authors thoroughly reviewed the articles to identify the different types of BD challenges – either theorized/proposed/discussed/confronted by different sector organizations), and BDA_MTH (i.e. here the authors examined the articles thoroughly to identify the different types of methods discussed, proposed and or employed by organizations to overcome BD challenges), so as to confirm substantive relevance both conceptually and empirically as mentioned in conditions 6 and 7

articles: 227
This latter analysis was conducted descriptively, using a standard template adapted from the works of Delbufalo (2012). This descriptive investigation also produced graphs and tables designed to contain the yearly publications, geographic region of the first author and coauthor(s), type of publications, and research methods employed, for all 227 articles. 4

articles: 227
Different researchers have distinct understandings towards the data characteristics – such as some say 3Vs [volume, velocity and variety] of data (e.g. Shah, Rabhi, & Ray, 2015), others reported 4Vs [volume, velocity, variety, and variability] of data (e.g. Liao, Yin, Huang, & Sheng, 2014) and 6Vs [volume, velocity, variety, veracity, variability, and value] of data (Gandomi & Haider, 2015). In analysing the different articles reviewed in this SLR, the authors identified 7Vs – seven characteristics of data [volume (DC_VOLM) → C = 90 (39.64% of 227 articles), variety (DC_VART) → C = 59 (25.9%), veracity (DC_VERT) → C = 44 (19.4%), value (DC_VALE) → C = 30 (13.2%), velocity (DC_VELO) → C = 18 (7.9%), visualization (DC_VISU) → C = 6 (2.6%) and variability (DC_VARB) → C = 4 (1.8%)] and these features are illustrated in Fig. 4 and discussed as follows:. • Volume (e.g. large data-sets consisting of terabytes, petabytes, zettabytes of data – or even more): Large scale and the sheer volume of data is a big challenge in its own right

articles: 97
In analysing the different articles reviewed the authors identified several data processing related challenges that can be grouped into 5 steps that is data acquisition and warehousing (PC_DAW) → C = 97 (42.7%), data mining and cleansing (PC_DMC) → C = 38 (16.7%), data integration and aggregation (PC_DAI) → C = 29 (12.8%), data analysis and modelling (PC_DAM) → C = 25 (11%) and data interpretation (PC_DI) → C = 15 (6.6%). As illustrated in Fig. 5, data mining and cleansing appears to be a vital step during processing the large scale unstructured data, as 97 articles out of 227 specifically discussed and highlighted the importance of this step. • Step 1 – Data Acquisition and Warehousing: This challenge is related to acquiring data from diverse sources and storing for value generation purpose

data centers: 13
• Cost/Operational Expenditures: The constantly increasing data in all different forms has led to a rising demand for BD processing in sophisticated data centers. These are generally dispersed across different geographical regions to embed resilience and spread risk, for example Google having 13 data centers in eight countries spread across four continents (Gu, Zeng, Li, & Guo, 2015). The significant resources have been allocated to support the data intensive operations (i.e. acquisition, warehousing, mining and cleansing, aggregation and integration, processing and interpretation) – all this lead to high storage and data processing big costs (Raghavendra, Ranganathan, Talwar, Wang, & Zhu, 2008)

papers: 115
As a result, organizations need to put in significant effort to customize such BDA solutions to their individual needs, which might require integrating different data sources and setting up the software on the organization's hardware. In analysing the different articles reviewed in this SLR, a total of 115 papers out of the 227 papers analyzed discusses and proposes some form of BDA methods and tools. The extant literature highlights a number of analytical processes and methods – such as text analytics, audio analytics, video analytics, social media analytics, predictive analysis of data (Gandomi & Haider, 2015) and others reported of descriptive analytics, inquisitive analytics, prescriptive analytics and pre-emptive data analytics (Assunção et al, 2015; Rehman, Chang, Batool, & Teh, 2016)

articles: 2360
Yearly publications. Using the keywords as stated in Section 1.2, initial search resulted in 2360 articles from 1996 until 2015 based on the number of subject areas including material sciences, energy, neuroscience, chemistry, etc. However, this research focused on only four subject areas such as business and management, computer science, decision science and social science (that directly relate to the special issue theme (i.e. Big Data and Analytics in Technology and Organizational Resource Management) – and following the systematic literature review steps (explained and illustrated in Section 3 and Fig. 3, respectively) – this research resulted in 227 articles

articles: 227
Using the keywords as stated in Section 1.2, initial search resulted in 2360 articles from 1996 until 2015 based on the number of subject areas including material sciences, energy, neuroscience, chemistry, etc. However, this research focused on only four subject areas such as business and management, computer science, decision science and social science (that directly relate to the special issue theme (i.e. Big Data and Analytics in Technology and Organizational Resource Management) – and following the systematic literature review steps (explained and illustrated in Section 3 and Fig. 3, respectively) – this research resulted in 227 articles. As presented in Fig. 8, the largest number of publications were recorded for year 2015 (with C = 114, 50.2%), followed by year 2014 (with C = 63, 27.7%) and year 2013 (with C = 43, 18.9%)

articles: 2360
Fig. 8 illustrates an abrupt increase in number of journal articles in the BD and BDA research area from 2013 onwards until 2015. Even through the initial search for articles (resulting in 2360 articles), there are more articles published from 2012 (e.g. 99 articles noted) until 2015 (e.g. 1156 articles noted). Regardless, the rapid increase in the articles highlights the awareness and importance of this area among the academic community, practitioners, and even governments worldwide (see e.g. Chen, Chen et al, 2012; Chen, Chiang et al, 2012; Joseph & Johnson, 2013)

articles: 295
Regardless, the rapid increase in the articles highlights the awareness and importance of this area among the academic community, practitioners, and even governments worldwide (see e.g. Chen, Chen et al, 2012; Chen, Chiang et al, 2012; Joseph & Johnson, 2013). Despite the increase in the number of articles on BD and BDA, this research domain is still emerging (e.g. as noted from Scopus Database that from January 2016 to-date so far 295 articles have been published). With the significance of BD and BDA from a strategic perspective and the increasing number of articles, it appears that this research domain requires further in-depth conceptual as well as empirical, especially case study and survey based research studies

articles: 227
Fig. 9 highlights that the number of articles published on BD and BDA area represent 42 different geographical regions across the globe between 1996 and 2015. The total number of regions of the 227 articles is 790 as it takes into account of the geographical regions of the coauthors as well. It was considered appropriate to include the regions of the co-authors in order to avoid misrepresenting that each paper was single authored

papers: 227
Types of publications. This section categorizes the list of 227 papers based on the publication type. The authors employed a analogous list of publication types as employed by Dwivedi and Mustafee (2010)

papers: 243
Types of research methods employed. The research methods employed by the BD researchers in the selected 243 papers and were coded under different categories as suggested by Dwivedi, Kiang, Lal, and Williams (2008) and Dwivedi and Mustafee (2010). The findings suggest that although a total of 11 different types of research methods were recorded from our data analysis, the majority of studies were analytical in nature (C = 103, 45.37%)

journal articles: 227
This SLR paper has revealed the past and current state of BD and BDA research published, thereby focusing on the past trends and current patterns in BD and BDA practices. Following Tranfield et al (2003) and Kitchenham and Charters (2007) Systematic Review Approach, this paper extracted and reviewed 227 journal articles from 1996 to 2015 from the Scopus database – as a result fulfilling the aim of this literature review paper (as indicated in Section 1.1). Figs. 4 to 11 clearly indicate the past trends and current patterns in the number of articles published on BD and BDA

引用论文
  • Chen, H. Chians. R. H Sc Storey. V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. Quanerh. 35(4). 1105-11SS. Chen. G. Clien. K. Jians. D. Ooi. B. C. Shi. L. Vo. H. I. 8c Wu. S. (2012). E3: an elastic execution ensme for scalable data processins. Journo! of Information Processing. 20(1). 05–70.
    Google ScholarLocate open access versionFindings
  • Grolinget. K. Higashino. W. A. Tiw'ari. A..Sc Capretz. M. A. (2013).Data management in doud environments: NoSQL andNewSQL data stores. Journal of Cloud Computing:. idxances. Systems and.ipp!kaiions. 2(1). 1–24. Miller. H. (2013). Bia-Data in Cloud Computing: A Taxonomv of Risks. Information Research. 18(1). Tme. K. M. Washio. T. Wells, J. R. Liu. F. T. Sc. Arval. S. (2013). DEMass: a new density estimator for bis data. Knowledge and Information Svstems. 33(3). 493–524. Shen. Y. Sc VarveL, V. E. (2013). Developing data management services at the Johns Hopkins University. The Journal Of Academic Librarianship. 39(6). 5 52–5 57. Mansell. R. (2013). Employing digital crowdsourced information resources: Managing the emerging information commons. International Journal of the Commons. 7(2), 255–277. Kraska. T. (2013). Finding the needle in the big data svstems haystack. IEEE Internet Computing.
    Google ScholarLocate open access versionFindings
  • Yfldtrim, E Kim. J. Sc Kosar. T. (2013). Modelling throughput sampling size for a doud-hosted data scheduling and optimization service. Future Generation Computer Svstems. 29(7). 1795-1S07. Ouzounis. G. K, Syrris. V. & Pesaresi. M. (2013). Multiscale quality assessment of Global Human Settlement Laver scenes against reference data using statistical learning. Pattern Recognition Letters. 34(14). 1636–1647. Karacapilidis. X. Tzagarakis. M. Sc Christodonlou. S. (2013). On a meaningful exploitation of machine and human reasoning to tackle data-mtensive decision miking. Intelligent Decision Technologies. 7(3), 225–236.
    Google ScholarLocate open access versionFindings
  • Wen, D. Guo-min. G., Tian-jun, W., & XLn-ju, Y. (2013). Organization and Management of Meteorological Sensor Network Collected BigData. Information Technolog}’ Journal, 12(22), 6636–6640.
    Google ScholarLocate open access versionFindings
  • Deng, S. G, Huang, L. T., Wu, B. & Xiong. L. R. {2013). Parallel optimization for data-intensive service composition. Journal of Internet Technology, 14(5), 817–824. Procter. R., Crump. J. Karstedt. S. Voss, A, & Cantijoch,M. (2013). Reading the riots: What were the Police doing on Twitter?. Policing and Society, 23(4). 413–436. Procter. R. Vis, F. & Voss, A (2013). Reading the riots on Twitter: methodological innovation for the analysis of big data. International Journal of Social Research Methodology’, 16(3), 197–214. Qin. H. F. & Li. Z. H. (2013). Research on the Method of Big Data Analysis. Information Technology? Journal, 72(10), 1–7. Small. S. G. &. Medsker, L. (2013). Review of information extraction technologies and applications. Neural Computing and Applications. 25(3–4), 533–548.
    Google ScholarLocate open access versionFindings
  • Tan, W., Blake, M.B., Saleh, I., & Dustdar. S. (2013). Social-network-sourced big data analytics. IEEE Internet Computing, 5,62–69.
    Google ScholarLocate open access versionFindings
  • Yang, H. (2013). Solving problems of imperfect data streams by incremental decision trees. Journal of Emerging Technologies in Web Intelligence, 5(3), 322–331. Wang. W. Lu, D. Zhou.X., Zhang, B.,& Mu, J. (2013). Statistical wavelet-basedanomalv detection in big data with compressive sensing. EURASIP Journal on Wireless Communications and Networking, 2013(1), pp. 1–6.
    Google ScholarLocate open access versionFindings
  • Hu, B., Carvalho, N.,& Matsutsuka. T. (2013). Towards Big Linked Data: A Large-Scale, Distributed Semantic Data Storage. International Journal of Data Warehousing and Mining (TJDWM), 9(4), 19–43.
    Google ScholarLocate open access versionFindings
  • Jimei, L., Yuzhou. H., & Meijie, D. (2013). XBRLinthe Chinese Financial Ecosystem. IT Professional, 15(6), 36–42. Chen. J., Chen, Y.Du. X. Li, C. Lu. J. Zhao. S. & Zhou. X. (2013). Big data challenge: a data management perspective. Frontiers of Computer Science, 7(2), 157–164.
    Google ScholarLocate open access versionFindings
  • Lee, B., & Jeong,E.{2014). A design of a patient-customize dhealthcare svstenbased on the Hadoopwithtextmining{PHSHT) for an efficient disease management and pre diction. Imernaiional Journal ofSoftware Engineering & Applications, #(8), 131–150.
    Google ScholarLocate open access versionFindings
  • Faria, F. A. Dos Santos, J. A. Rocha. A. & Torres. R. D. S. (2014). A framework for selection and fusion of pattern classifiers in multimedia recognition. Pattern Recognition Letters, 39, 52–64. Chen. Z. Lu. Y., Xiao.N. and Liu. F. 2014. Ahvbrid memory built bv SSD and DRAM to support in-memorv Big Data analvtics. Knowledge and Information Systems, 41(2). 335–354. Lin. C. Y., & Liao. J. K (2014). A fob-oriented load-distribution scheme for cost-effective NameNode service in HDFS. International Journal of Web and Grid Services, 10(4). 319–337.
    Google ScholarLocate open access versionFindings
  • Antonie, A,Marjanovic,M.,Pripuzic, K., & Zarko, I. P. (2014). Amobile crowd sensing ecosystem enabledby CUPUS: doud-basedpublish’ subscribemiddleware forthe internet of things Future Generation Co mp uier Systems, 56,607–622 Ulltveit-Moe,N. (2014). A roadmap towards improving managed security services from a privacy perspective. Ethics and Information Technology, 16(3), 227–240. Fahad. A., Ashatn, N. Tari, Z.: Aamri, A., Khalil, I., Zomava, A.Y.,Foufou, S., Bouras, A (2014). A survey of clustering algorithms for big data: Taxonomy and empirical analysis. IEEE Transactions on Emerging Topics in Computing, 2(3), 267–279. Liu. S. Cui. W. Wu, Y. & Liu, M. (2014). A survey on information visualization: recent advances and challenge s. The Visual Computer. 39(12). 1373–1393. Zhang. F. Cao. J. Khan, S. U. Li. K. & Hwang. K. (2015). A task-level adaptive MapReduce framework for real-time streaming data in healthcare applications. Future Generation Computer Systems. 43. 149–160.
    Google ScholarLocate open access versionFindings
  • Hofrnan, W., & Rajagopal, M. (2014). A technical framework for data sharing. Journal of Theoretical and Applied Electronic Commerce Research, 9(3), 45–58. Kuang. L.,Hao,F. Yang. L. T. Lin. M.Luo. C. &Min, G. (2014).Atensor-basedapproachforbig data representation and dimensionality reduction. IEEE Transactions on Emerging Topics in Computing, 2(3), 280–291. Lebdaoui. I. Qrhanou. G. & Elhajfi, S. (2014). An Integration Adaptation for Real-Time Datawarehousing. Internationa! Journal of Software Engineering and its Applications, 2(11). 115–128.
    Google ScholarLocate open access versionFindings
  • AgrawaL D. (2014). Analvtics based decision making. Journal of Indian Business Research, 6(4), 332–340. Song. M. Kim, M. C. Jeong. Y. K (2014). Analysing the political landscape of 2012 Korean Pre sidential Ele ction in Twitter. IEEE Intelligent Systems. 29(2), 18–26.
    Google ScholarLocate open access versionFindings
  • Liu, C., Chen, J., Yang.L. T, Zhang, X, Yang, C,& Rao,K (2014). Authorized public auditing of dynanicbig datastorage on cloud with efficientverifiable fine-grained updates. IEEE Transactions on Parallel and Distributed Systems, 25(9), 2234–2244. Gandomi. A. & Haider. M. (2015). Bevondthe hvpe: Big data concepts, methods, and analytics. International Journal of Information Management. 35(2), 137–144. Tinati. R. Halford. S. Can. L. & Pope. C. (2014). Big data: methodological challenges and approaches for sociological analysis. Sociology.48(4), 663–681.
    Google ScholarLocate open access versionFindings
  • Yin, H. Jiang. Y. Lin. C. Luo, Y. & Liu. Y. (2014). Big data: transforming the design philosophy of future internet. IEEE Network, 28(4), 14–19. Krishnamurthv.R., & Desouza, K C.{2014). Big data analytics: The case of the social security administration. Information Polity, 19(3,4), 165–178. Diamantoulakis. P. D., Kapinas. V. M.,& Karagiannidis. G. K_ (2015). Big data analytics for dynamic energy* management in smart grids. Big Data Research, 2(3), 94–101.
    Google ScholarLocate open access versionFindings
  • Wang, Y., & Wiebe, V. J. (2014). Big Data Analvtics on the Characteristic Equilibrium of Collective Opinions in Social Networks. International Journal of Cognitive Informatics and Natural Intelligence, 8(3), 29–44.
    Google ScholarLocate open access versionFindings
  • Fernandez, A, del Rio, S., Lopez, V′., Bawakid, A, del Jesus, M. J., Benitez, J. M.,& Herrera, F. (2014). BigData with Cloud Computing: an insight on
    Google ScholarFindings
  • Bertot, J. C., Gorham, U., Jaeger, P. T., Sarin, L. C, & Choi, H. (2014). Big data, open government and e-Govemment: Issues, policies and recommendations. Information Polity, 29(1,2), 5–16. Kim. G. H., Trimi, S., Chung, J. H. (2014). Big-data applications in the government sector. Communications of the ACM, 57(3), 78–85.
    Google ScholarLocate open access versionFindings
  • Yi, X., Liu. F. Liu, J., & Jin, H. (2014) Building a network highway for big data: architecture and challenges. IEEE Network, 28(4), 5–13.
    Google ScholarLocate open access versionFindings
  • Ryiavy, S. J. Bromlev. D. & Daggett. V. (2014). DIVE: A eraph-based visual-anal’,lies framework for big data. Computer Graphics and,Applications. IEEE. 34(2). 26–37.
    Google ScholarLocate open access versionFindings
  • Sn, Y. Agrawal. G. Woodrmg. J. Myers, K. Wendelberger. J. & Ahrens. J. (2014). Effective and efficient data sampling using bitmap indices. Cluster Computing. 17(4), 10S1–1100. Guo. T. Papaioannon. T. G. 5c Aberer. K. (2014). Efficient Indexing and Query Processing of Model-View Sens or D ata in die Cloud. Big Data Research. 1. 52–65. Ellis. J. Fokoue. A. Hassanzadeh. O. Kementsietsidis. A. Srinivas. K. & Ward. M. J (2015). Exploring Big Data with Helix: Finding Needles in a Bia Haystack ACMSIGMOD Record. 43(4). 43–54.
    Google ScholarLocate open access versionFindings
  • Li, H., Lu, K, & Meng, S. (2015). Bigprovision: A provisioning framework for big data analytics. IEEE Network, 29(5), 50–56.
    Google ScholarLocate open access versionFindings
  • Zhang, S., Yin. D., Zhang, Y., & Zhou, W. (2015). Computing on Base Station Behaviour Using Erlang Measurement and Call Detail Record. IEEE Transactions on Emerging Topics in Computing, 3(3), 444–453.
    Google ScholarLocate open access versionFindings
  • Miller H J, & Goodebild, M. F. (2015). Data-driven geography. Geo Journal, 60(4), 449–461.
    Google ScholarLocate open access versionFindings
  • Cao, M., Chychyla, R., & Stewart, T. (2015). Big Data analytics in financial statement audits. Accounting Horizons, 29(2), M423–429. Wang. F. Hu, L., Zhou. D., Sun., R., Hu, J., & Zhao. K. (2015). Estimating online vacancies in real-time roadtraffic monitoring with traffic sensor data stream. AdHoc Networks, 35,3–13.
    Google ScholarLocate open access versionFindings
  • Farahat, A. K,Elgoharv, A., Gho dsi, A., & Kamel, M. S. (2015). Greedy column subset selection for large-scale data sets. Knowledge and Information Systems, 45(1), 1–34. Shah. T., Rabhi, F. & Ray, P. (2015). Investigating an ontology-based approach for Big Data analysis of inter-dependent medical and oral health conditions. Cluster Computing, 76(1), 351–367.
    Google ScholarLocate open access versionFindings
  • Neish, P. (2015). Linked data: what is it and why should you care? The Australian Library Journal, 64(1), 3–10.
    Google ScholarLocate open access versionFindings
  • Ashraf, J. Hussain, O. K_, & Hussain, F. K_ (2015). Making sense from Big RDF Data: OUSAF for measuring ontology usage. Software: Practice and Experience, −75(8), 1051–1071. La ebb e eke. C. &Picot,A. (2015). Reflections on societal andbusmessmodel transformation arising from digitization andbig data analytics: A research agenda. The Journal of Strategic Information Systems, 24(3), 149–157. Otero. C. E. & Peter, A (2015). Research Directions for Engineering Big Data Analytics Software. IEEE Intelligent Systems, 36(1), 13–19. Triguero,I.: del Rio. S., Lopez, V. Bacardit.J, Benitez, J. M.,& Herrera. F. (2015). ROSEFW-RF: the winner algorithm for the EC BDL’ 14 big data competition: an extremely imbalanced big data bioinformatics problem. Knowledge-Based Systems, 87,69–79.
    Google ScholarLocate open access versionFindings
  • Kune, R., Konugurthi, P. K_, Agarwal, A, Chillarige, R. R. & Buyya, R. (2015). The anatomy ofbig data computing. Software: Practice and Experience, 46(1), 79–105. Wang,Z., Chen, H., Fu, Y.,Liu, D., & Ban, Y. (2015). Workload balancing and adaptive resource management for the swift storage system on cloud. Future Generation Computer Systems, 51,120–131.
    Google ScholarLocate open access versionFindings
  • Hu, W., & Jia, C. (2015). A bootstrapping approach to entity linkage on the Semantic Web. Web Semantics: Science, Services and Agents on the World Wide Web,34, 1–12.
    Google ScholarLocate open access versionFindings
  • Lin, W, Dou, W., Zhou, Z. & Liu, C. (2015). A cloud-based framework for Home-diagnosis service over big medical data. Journal of Systems and Software, 102, 192–206.
    Google ScholarLocate open access versionFindings
  • Chen, Z.Xu, G. Mahalingam. V., Ge.L. Nguyen. J. Yu, W. and Lu, C.; 2015. A Cloud Computing Based Network Monitoring and Threat Detection System for Critical Infrastructures. Big Data Research. Xu. G. Yu. WT. Chen. Z. Zhang. H. Moulema. P., Fu.X. & Lu. C. (2015). Acloud computing b asedsv stanfor cyber se curitv management International Journal of Parallel, Emergent and Distributed Systems, 3 Of1). 29–45.
    Google ScholarLocate open access versionFindings
  • Smowton, C., BaHa. A, Antoruades.D.,Miller. C., Pallis, G., Dikaiakos,M.D.,&Xmg. W. (2015). A cost-effective approachto improving performance ofbig genomic data analyses in clouds. Future Generation Computer Systems. Simonet, A, Fedak, G., &Ripeanu, M. (2015). Active Data: A programming model to manage data life cycle across heterogeneous systems and infrastructures. Future Generation Computer Systems, 53,25–42.
    Google ScholarLocate open access versionFindings
  • Zhang, F, Cao, J., Hwang, K, Li. K. & Khan, S. U. (2015). Adaptive Workflow Scheduling on Cloud Computing Platforms with Iterative Ordinal Optimization. IEEE Transactions on Cloud Computing, 3(2), 156–168 Merino, J., Caballero, I., Rivas, B., Serrano, M., &. Piattini, M. (2015). A Data Quality in Use model for Big Data. Future Generation Computer Systems. □ie-Zudor,E.Ekart, A.,Kemenv, Z. Buckingham. C. Welch. P. & Monostori. L. (2015). Advancedpiedictive-analvsis-based decision support for collaborativelogistics networks.Supply Chain Management: An International Journal, 26(4), 369–388.
    Google ScholarFindings
  • Wang, Y., & Ma. X. (2015). A General scalable and elastic content-based publish1 sub scribe service. IEEE Transactions on Parallel and Distributed Systems, 26(8), 2100–2113.
    Google ScholarLocate open access versionFindings
  • Tavlor. L. & Schroeder. R. (2015). Is bigger better? Hie emergence of big data as a tool cor international development policy. Geo Journal. 30(4). 503–518. Mohebt. A. Aghabozorgi. S. Ying Wah. T. Herawan. T., Sc Yahvapour. R. (2015). Iterative big data clustering algorithms: A Review. Software: Practice and Experience. 46(1). 107–129.
    Google ScholarLocate open access versionFindings
  • Abawajy, J. (2015). Comprehensive analysis of big data variety landscape. International Journal of Parallel, Emergent and Distributed Systems, 30(1), 5–14.
    Google ScholarLocate open access versionFindings
  • Abawajy, J. H., Kelarev, A., & Chowdhury, M. (2014). Large iterative multitier ensemble classifiers for security of big data. IEEE Transactions on Emerging Topics in Computing, 2(3), 352–363.
    Google ScholarLocate open access versionFindings
  • Abdellatif, T. M., Capretz, L. F., & Ho, D. (2015). Software analytics to software practice: a systematic literature review. Proceedings of the 1st International Workshop on BIG Data Software Engineering – IEEE Press (pp. 30–36).
    Google ScholarLocate open access versionFindings
  • Agarwal, R., & Dhar, V. (2014). Editorial – big data, data science, and analytics: the opportunity and challenge for is research. Information Systems Research, 25(3), 443–448.
    Google ScholarLocate open access versionFindings
  • Akerkar, R. (2014). Big data computing. Florida, USA: CRC Press, Taylor & Francis Group.
    Google ScholarFindings
  • Al Nuaimi, E., Al Neyadi, H., Mohamed, N., & Al-Jaroodi, J. (2015). Applications of big data to smart cities. Journal of Internet Services and Applications, 6(1), 1–15.
    Google ScholarLocate open access versionFindings
  • Assunção, M. D., Calheiros, R. N., Bianchi, S., Netto, M. A., & Buyya, R. (2015). Big Data computing and clouds: trends and future directions. Journal of Parallel and Distributed Computing, 79, 3–15.
    Google ScholarLocate open access versionFindings
  • Banerjee, A., Bandyopadhyay, T., & Acharya, P. (2013). Data analytics: hyped up aspirations or true potential. Vikalpa. The Journal for Decision Makers, 38(4), 1–11.
    Google ScholarLocate open access versionFindings
  • Barbierato, E., Gribaudo, M., & Iacono, M. (2014). Performance evaluation of NoSQL bigdata applications using multi-formalism models. Future Generation Computer Systems, 37, 345–353.
    Google ScholarLocate open access versionFindings
  • Barnaghi, P., Sheth, A., & Henson, C. (2013). From data to actionable knowledge: big data challenges in the web of things. IEEE Intelligent Systems, 28(6), 6–11.
    Google ScholarLocate open access versionFindings
  • Berners-Lee, T., & Shadbolt, N. (2011). There's gold to be mined from all our data. The Times, London 1:1–2. Online Available at: http://www.thetimes.co.uk/tto/opinion/columnists/article3272618.ece [Accessed on 21st April 2016]. Bertot, J. C., Gorham, U., Jaeger, P. T., Sarin, L. C., & Choi, H. (2014). Big Data, open government and e-government:issues, policies and recommendations. Information Polity, 19(1, 2), 5–16.
    Locate open access versionFindings
  • Bhimani, A., & Willcocks, L. (2014). Digitisation, Big Data and the transformation of accounting information. Accounting and Business Research, 44(4), 469–490.
    Google ScholarLocate open access versionFindings
  • Bihani, P., & Patil, S. T. (2014). A comparative study of data analysis techniques. International Journal of Emerging Trends & Technology in Computer Science, 3(2), 95–101.
    Google ScholarLocate open access versionFindings
  • Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, communication & society, 15(5), 662–679.
    Google ScholarLocate open access versionFindings
  • Brown, B., Chui, M., & Manyika, J. (2011). Are you ready for the era of Big Data? The McKinsey Quarterly, 4, 24–35.
    Google ScholarLocate open access versionFindings
  • Cárdenas, A. A., Manadhata, P. K., & Rajan, S. P. (2013). Big Data analytics for security. IEEE Security and Privacy, 6, 74–76.
    Google ScholarLocate open access versionFindings
  • Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka, E., Jr., & Mitchell, T. (2010). Toward an architecture for never-ending language learning. Proceedings of the Conference on Association for the Advancement of Artificial Intelligence (pp. 1306–1313).
    Google ScholarLocate open access versionFindings
  • Chen, C. L. P., & Zhang, C. Y. (2014). Data-intensive applications, challenges, techniques and technologies: a survey on big data. Information Sciences, 275, 314–347.
    Google ScholarLocate open access versionFindings
  • Chen, G., Chen, K., Jiang, D., Ooi, B. C., Shi, L., Vo, H. T., & Wu, S. (2012b). E3: an elastic execution engine for scalable data processing. Journal of Information Processing, 20(1), 65–76.
    Google ScholarLocate open access versionFindings
  • Chen, H., Chiang, R. H., & Storey, V. C. (2012a). Business intelligence and analytics: From Big Data to big impact. MIS Quarterly, 36(4), 1165–1188.
    Google ScholarLocate open access versionFindings
  • Chen, J., Chen, Y., Du, X., Li, C., Lu, J., Zhao, S., & Zhou, X. (2013). Big data challenge: a data management perspective. Frontiers of Computer Science, 7(2), 157–164.
    Google ScholarLocate open access versionFindings
  • Chen, M., Mao, S., & Liu, Y. (2014). Big Data: a survey. Mobile Networks and Applications, 19(2), 171–209.
    Google ScholarLocate open access versionFindings
  • Crawford, K. (1 April, 2013). The hidden biases of big data. Harvard Business Review Blog. Available at: http://blogs.hbr.org/2013/04/the-hidden-biases-in-big-data/ (accessed 5 January 2016)
    Locate open access versionFindings
  • Cukier, K. (2010). The economist, data, data everywhere: A special report on managing information. Online Available at http://www.economist.com/node/15557443 (Accessed on 20th April 2016).
    Locate open access versionFindings
  • Davenport, T. H., & Dyché, J. (2013). Big data in big companies. International Institute for Analytics. Available Online at http://www.demonish.com/cracker/1431316877_1217a9641e/bigdata-bigcompanies-106461.pdf (Accessed 5th January 2016).
    Findings
  • Davenport, T. H., & Harris, J. G. (2007). Competing on analytics: The new science of winning. Harvard Business Press.
    Google ScholarFindings
  • David, R. J., & Han, S. K. (2004). A systematic assessment of the empirical support for transaction cost economics. Strategic Management Journal, 25(1), 39–58.
    Google ScholarLocate open access versionFindings
  • Delbufalo, E. (2012). Outcomes of inter-organizational trust in supply chain relationships: a systematic literature review and a meta-analysis of the empirical evidence. Supply Chain Management: An International Journal, 17(4), 377–402.
    Google ScholarLocate open access versionFindings
  • Demchenko, Y., Grosso, P., De Laat, C., & Membrey, P. (2013). Addressing big data issues in scientific data infrastructure. IEEE international conference on collaboration technologies and systems (CTS) (pp. 48–55).
    Google ScholarLocate open access versionFindings
  • Dobre, C., & Xhafa, F. (2014). Intelligent services for big data science. Future Generation Computer Systems, 37, 267–281.
    Google ScholarLocate open access versionFindings
  • Dwivedi, Y. K., & Mustafee, N. (2010). Profiling research published in the Journal of Enterprise Information Management. Journal of Enterprise Information Management, 23(1), 8–26.
    Google ScholarLocate open access versionFindings
  • Dwivedi, Y. K., Kiang, M., Lal, B., & Williams, M. D. (2008). Profiling research published in the Journal of Electronic Commerce Research. Journal of Electronic Commerce Research, 9(2), 77–91.
    Google ScholarLocate open access versionFindings
  • Edwards, R., & Fenwick, T. (2015). Digital analytics in professional work and learning. Studies in Continuing Education (pp. 1–15).
    Google ScholarLocate open access versionFindings
  • Eembi, N. B. C., Ishak, I. B., Sidi, F., Affendey, L. S., & Mamat, A. (2015). A systematic review on the profiling of digital news portal for Big Data veracity. Procedia Computer Science, 72, 390–397.
    Google ScholarLocate open access versionFindings
  • Frehe, V., Kleinschmidt, T., & Teuteberg, F. (2014). Big data in logistics-identifying potentials through literature, case study and expert interview analyzes. In GI-Jahrestagung, 173–186.
    Google ScholarLocate open access versionFindings
  • Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144.
    Google ScholarLocate open access versionFindings
  • Gantz, J., & Reinsel, D. (2012). The Digital Universe in 2020: Big data, bigger digital shadows, and biggest growth in the Far East. IDC – EMC Corporation. Online Available at http://www.emc.com/collateral/analyst-reports/idc-the-digital-universe-in2020.pdf (Accessed 16th January 2016).
    Findings
  • George, G., Haas, M. R., & Pentland, A. (2014). Big Data and management. Academy of Management Journal, 57(2), 321–326.
    Google ScholarLocate open access versionFindings
  • Gu, L., Zeng, D., Li, P., & Guo, S. (2015). Cost minimization for big data processing in geodistributed data centers. In Cloud Networking for Big Data (pp. 59–78). Springer International Publishing.
    Google ScholarFindings
  • Halevy, A., Rajaraman, A., & Ordille, J. (2006). Data integration: The teenage years. Proceedings of the 32nd International Conference on Very Large Data Bases (pp. 9–16).
    Google ScholarLocate open access versionFindings
  • Hargittai, E. (2015). Is bigger always better? Potential biases of big data derived from social network sites, The ANNALS of the American Academy of Political and Social Science, 659(1), 63–76.
    Google ScholarLocate open access versionFindings
  • Hasan, S., Shamsuddin, S. M., & Lopes, N. (2014). Machine learning big data framework and analytics for big data problems. International Journal of Advance Soft Computing Application, 6(2), 1–14.
    Google ScholarLocate open access versionFindings
  • Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of big data on cloud computing: Review and open research issues. Information Systems, 47, 98–115.
    Google ScholarLocate open access versionFindings
  • Intel IT Center (2012). Big Data Analytics: Intel’s IT Manager Survey on How Organizations Are Using Big Data. Available at: http://www.intel.co.za/content/www/za/en/big-data/data-insights-peer-research-report.html [Accessed 5 Jan.2016]
    Findings
  • Irani, Z. (2010). Investment evaluation within project management: an information systems perspective. Journal of the Operational Research Society, 61(6), 917–928.
    Google ScholarLocate open access versionFindings
  • Irani, Z., Ghoneim, A., & Love, P. E. (2006). Evaluating cost taxonomies for information systems management. European Journal of Operational Research, 173(3), 1103–1122.
    Google ScholarLocate open access versionFindings
  • Irani, Z., Sharif, A., Kamal, M. M., & Love, P. E. (2014). Visualising a knowledge mapping of information systems investment evaluation. Expert Systems with Applications, 41(1), 105–125.
    Google ScholarLocate open access versionFindings
  • Jiang, H., Chen, Y., Qiao, Z., Weng, T. H., & Li, K. C. (2015). Scaling up MapReduce-based big data processing on multi-GPU systems. Cluster Computing, 18(1), 369–383.
    Google ScholarLocate open access versionFindings
  • Jin, X., Wah, B. W., Cheng, X., & Wang, Y. (2015). Significance and challenges of big data research. Big Data Research, 2(2), 59–64.
    Google ScholarLocate open access versionFindings
  • Joseph, R. C., & Johnson, N. A. (2013). Big data and transformational government. IT Professional, 15(6), 43–48.
    Google ScholarLocate open access versionFindings
  • Jukić, N., Sharma, A., Nestorov, S., & Jukić, B. (2015). Augmenting data warehouses with Big Data. Information Systems Management, 32(3), 200–209.
    Google ScholarLocate open access versionFindings
  • Kaisler, S., Armour, F., Espinosa, J. A., & Money, W. (2013). Big data: Issues and challenges moving forward. 46th Hawaii International Conference on System Sciences (HICSS) (pp. 995–1004).
    Google ScholarFindings
  • Kamal, M. M., & Irani, Z. (2014). Analysing supply chain integration through systematic literature review: a normative perspective. Supply Chain Management: An International Journal, 19(5/6), 523–557.
    Google ScholarLocate open access versionFindings
  • Karacapilidis, N., Tzagarakis, M., & Christodoulou, S. (2013). On a meaningful exploitation of machine and human reasoning to tackle data-intensive decision making. Intelligent Decision Technologies, 7(3), 225–236.
    Google ScholarLocate open access versionFindings
  • Khan, M. A., Uddin, M. F., & Gupta, N. (2014). Seven Vs of Big Data understanding Big Data and extract value. Proceedings of 2014 Zone 1 Conference of the American Society for Engineering Education (ASEE Zone 1) – IEEE (pp. 1–5).
    Google ScholarLocate open access versionFindings
  • Kim, G. H., Trimi, S., & Chung, J. H. (2014). Big-data applications in the government sector. Communications of the ACM, 57(3), 78–85.
    Google ScholarLocate open access versionFindings
  • Kitchenham, B., & Charters, S. (2007). Guidelines for performing systematic review process research in software engineering. Online Available at http://www.citeulike.org/group/14013/article/7874938 (Accessed on 19th December 2015).
    Locate open access versionFindings
  • Krishnamurthy, R., & Desouza, K. C. (2014). Big data analytics: the case of the social security administration. Information Polity, 19(3/4), 165–178.
    Google ScholarLocate open access versionFindings
  • Kumar, A., Niu, F., & Ré, C. (2013). Hazy: making it easier to build and maintain big-data analytics. Communications of the ACM, 56(3), 40–49.
    Google ScholarLocate open access versionFindings
  • Kune, R., Konugurthi, P. K., Agarwal, A., Chillarige, R. R., & Buyya, R. (2016). The anatomy of big data computing. Software: Practice and Experience, 46(1), 79–105.
    Google ScholarLocate open access versionFindings
  • Labrinidis, A., & Jagadish, H. V. (2012). Challenges and opportunities with big data. Proceedings of the VLDB Endowment, 5(12), 2032–2033.
    Google ScholarLocate open access versionFindings
  • Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabasi, A., Brewer, D.,... Van Alstyne, M. (2009). ‘Computational social science’. Science, vol. 323(no. 5915), 721–723.
    Google ScholarLocate open access versionFindings
  • Lebdaoui, I., Orhanou, G., & Elhajji, S. (2014). An integration adaptation for real-time Datawarehousing. International Journal of Software Engineering and its Applications, 8(11), 115–128.
    Google ScholarLocate open access versionFindings
  • Lettieri, E., Masella, C., & Radaelli, G. (2009). Disaster management: findings from a systematic review. Disaster Prevention and Management: An International Journal, 18(2), 117–136.
    Google ScholarLocate open access versionFindings
  • Liao, Z., Yin, Q., Huang, Y., & Sheng, L. (2014). Management and application of mobile big data. International Journal of Embedded Systems, 7(1), 63–70.
    Google ScholarLocate open access versionFindings
  • Lu, R., Zhu, H., Liu, X., Liu, J. K., & Shao, J. (2014). Toward efficient and privacy-preserving computing in big data era. IEEE Network, 28(4), 46–50.
    Google ScholarLocate open access versionFindings
  • Machanavajjhala, A., & Reiter, J. P. (2012). Big privacy: protecting confidentiality in big data. XRDS: Crossroads. The ACM Magazine for Students, 19(1), 20–23.
    Google ScholarLocate open access versionFindings
  • du Mars, R. (2012). Mission impossible? Data governance process takes on big data. Online Available at http://searchdatamanagement.techtarget.com/feature/Missionimpossible-Data-governance-process-takes-on-big-data (Accessed on 9th January 2016).
    Locate open access versionFindings
  • Mayer-Schönberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. Boston, MA: Eamon Dolan/Houghton Mifflin Harcourt.
    Google ScholarFindings
  • Mishra, D., Gunasekaran, A., Papadopoulos, T., & Childe, S. J. (2016). Big Data and supply chain management: a review and bibliometric analysis. Annals of Operations Research. http://dx.doi.org/10.1007/s10479-016-2236-y.
    Locate open access versionFindings
  • MIT Technology Review (2013). The Big Data Conundrum: How to define it? Available Online at https://www.technologyreview.com/s/519851/the-big-data-conundrumhow-to-define-it/ (Accessed 19th May 2016).
    Findings
  • Office of Science and Technology Policy (OSTP), Executive Office of the President (2012O). Big data press release final 2. Available http://www.whitehouse.gov/sites/default/files/microsites/ostp/big_data_press_release_final_2.pdf (Accessed on 7th October 2015).
    Locate open access versionFindings
  • Otto, B. (2011). Organizing data governance: findings from the telecommunications industry and consequences for large service providers. Communications of the Association for Information Systems, 29(1), 45–66.
    Google ScholarLocate open access versionFindings
  • Paris, J., Donnal, J. S., & Leeb, S. B. (2014). NilmDB: the non-intrusive load monitor database. Smart Grid, IEEE Transactions on, 5(5), 2459–2467.
    Google ScholarLocate open access versionFindings
  • Phillips-Wren, G., & Hoskisson, A. (2015). An analytical journey towards big data. Journal of Decision Systems, 24(1), 87–102.
    Google ScholarLocate open access versionFindings
  • Pittaway, L., Robertson, M., Munir, K., Denyer, D., & Neely, A. (2004). Networking and innovation: a systematic review of the evidence. International Journal of Management Reviews, 5(3‐4), 137–168.
    Google ScholarLocate open access versionFindings
  • Polato, I., Ré, R., Goldman, A., & Kon, F. (2014). A comprehensive view of Hadoop research – a systematic literature review. Journal of Network and Computer Applications, 46, 1–25.
    Google ScholarLocate open access versionFindings
  • Raghavendra, R., Ranganathan, P., Talwar, V., Wang, Z., & Zhu, X. (2008). No power struggles: coordinated multi-level power management for the data center. In ACM SIGARCH Computer Architecture News, 36(1), 48–59.
    Google ScholarLocate open access versionFindings
  • Rehman, M. H., Chang, V., Batool, A., & Teh, Y. W. (2016). Big data reduction framework for value creation in sustainable enterprises. International Journal of Information Management (Accepted).
    Google ScholarLocate open access versionFindings
  • Russom, P. (2013). Managing Big Data. Available Online at: The Data Warehousing Institute. [Accessed 5th January 2016] https://tdwi.org/articles/2013/10/01/executivesummary-managing-big-data.aspx
    Findings
  • Sandhu, R., & Sood, S. K. (2014). Scheduling of big data applications on distributed cloud based on QoS parameters. Cluster Computing, 18, 1–12.
    Google ScholarLocate open access versionFindings
  • Savitz, E. (2012a). Gartner: Top 10 strategic technology trends for 2013. Online Available at http://www.forbes.com/sites/ericsavitz/2012/10/23/gartner-top-10-strategictechnology-rends-for-2013/ (Accessed on 3rd March 2016).
    Locate open access versionFindings
  • Savitz, E. (2012b). Gartner: 10 critical tech trends for the next five years. Online Available at http://www.forbes.com/sites/ericsavitz/2012/10/22/gartner-10-critical-techtrends-for-the-next-five-years/ (Accessed on 3rd March 2016)
    Locate open access versionFindings
  • Shah, T., Rabhi, F., & Ray, P. (2015). Investigating an ontology-based approach for Big Data analysis of inter-dependent medical and oral health conditions. Cluster Computing, 18(1), 351–367.
    Google ScholarLocate open access versionFindings
  • Simonet, A., Fedak, G., & Ripeanu, M. (2015). Active Data: A programming model to manage data life cycle across heterogeneous systems and infrastructures. Future Generation Computer Systems, 53, 25–42.
    Google ScholarLocate open access versionFindings
  • Sivarajah, U., Irani, Z., & Jones, S. (2014). Application of Web 2.0 technologies in E-Government: A United Kingdom case study. 2014 47th Hawaii International Conference on System Sciences (pp. 2221–2230).
    Google ScholarFindings
  • Sivarajah, U., Irani, Z., & Weerakkody, V. (2015). Evaluating the use and impact of Web 2.0 technologies in local government. Government Information Quarterly, 32(4), 473–487.
    Google ScholarLocate open access versionFindings
  • Spiess, J., T'Joens, Y., Dragnea, R., Spencer, P., & Philippart, L. (2014). Using big data to improve customer experience and business performance. Bell Labs Technical Journal, 18(4), 3–17.
    Google ScholarLocate open access versionFindings
  • Su, K., Li, J., & Fu, H. (2011). Smart city and the applications. IEEE International Conference on Electronics, Communications and Control (ICECC) (pp. 1028–1031).
    Google ScholarLocate open access versionFindings
  • Sun, N., Morris, J. G., Xu, J., Zhu, X., & Xie, M. (2014). iCARE: A framework for big databased banking customer analytics. IBM Journal of Research and Development, 58(5/ 6), 4-1.
    Google ScholarLocate open access versionFindings
  • Szongott, C., Henne, B., & von Voigt, G. (2012). Big data privacy issues in public social media. 6th IEEE international conference on digital ecosystems technologies (DEST) (pp. 1–6).
    Google ScholarLocate open access versionFindings
  • Taheri, J., Zomaya, A. Y., Siegel, H. J., & Tari, Z. (2014). Pareto frontier for job execution and data transfer time in hybrid clouds. Future Generation Computer Systems, 37, 321–334.
    Google ScholarLocate open access versionFindings
  • Ting, K. M., Washio, T., Wells, J. R., Liu, F. T., & Aryal, S. (2013). DEMass: a new density estimator for big data. Knowledge and Information Systems, 35(3), 493–524.
    Google ScholarLocate open access versionFindings
  • Tole, A. A. (2013). Big data challenges. Database Systems Journal, 4(3), 31–40.
    Google ScholarLocate open access versionFindings
  • Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British Journal of Management, 14(3), 207–222.
    Google ScholarLocate open access versionFindings
  • Van Dijck, J. (2014). Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology. Surveillance & Society, 12(2), 197–208.
    Google ScholarLocate open access versionFindings
  • Vasarhelyi, M. A., Kogan, A., & Tuttle, B. M. (2015). Big data in accounting: an overview. Accounting Horizons, 29(2), 381–396.
    Google ScholarLocate open access versionFindings
  • Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77–84.
    Google ScholarLocate open access versionFindings
  • Wang, G., Gunasekaran, A., Ngai, E. W., & Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: certain investigations for research and applications. International Journal of Production Economics, 176, 98–110.
    Google ScholarLocate open access versionFindings
  • Wang, Y., & Wiebe, V. J. (2014). Big Data Analytics on the characteristic equilibrium of collective opinions in social networks. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 8(3), 29–44.
    Google ScholarLocate open access versionFindings
  • Watson, H. J. (2014). Tutorial: big data analytics: Concepts, technologies, and applications. Communications of the Association for Information Systems, 34(1), 1247–1268.
    Google ScholarLocate open access versionFindings
  • Web, G. (2007). SensorMap for wide-area sensor webs. Embedded computing. Online Available at http://www.fengzhao.com/pubs/embcomp.pdf (Accessed on 13th March 2016)
    Locate open access versionFindings
  • Weill, P., & Ross, J. W. (2009). IT savvy: What top executives must know to go from pain to gain. Harvard Business Press.
    Google ScholarFindings
  • Xu, J. S., Zhang, E., Huang, C. -H., Chen, L. H. L., & Celik, N. (2014). Efficient multi-fidelity simulation optimization. Proceedings of 2014 winter simulation conference. GA: Savanna.
    Google ScholarLocate open access versionFindings
  • Yi, X., Liu, F., Liu, J., & Jin, H. (2014). Building a network highway for big data: architecture and challenges. IEEE Network, 28(4), 5–13.
    Google ScholarLocate open access versionFindings
  • Zaslavsky, A., Perera, C., & Georgakopoulos, D. (2012). Sensing as a service and big data. International Conference on Advances in Cloud Computing (ACC-2012), Bangalore, India (pp. 21–29).
    Google ScholarLocate open access versionFindings
  • Zhang, F., Liu, M., Gui, F., Shen, W., Shami, A., & Ma, Y. (2015a). A distributed frequent itemset mining algorithm using Spark for Big Data analytics. Cluster Computing, 18(4), 1493–1501.
    Google ScholarLocate open access versionFindings
  • Zhang, X., Hu, Y., Xie, K., Zhang, W., Su, L., & Liu, M. (2015b). An evolutionary trend reversion model for stock trading rule discovery. Knowledge-Based Systems, 79, 27–35.
    Google ScholarLocate open access versionFindings
  • Zhao, Z., Zhang, R., Cox, J., Duling, D., & Sarle, W. (2013). Massively parallel feature selection: an approach based on variance preservation. Machine Learning, 92(1), 195–220.
    Google ScholarLocate open access versionFindings
  • Zicari, R. V. (2014). Big Data: Challenges and Opportunities. (2014) In R. (Ed.), Big data computing (pp. 103–128). Florida, USA: CRC Press, Taylor & Francis Group.
    Google ScholarLocate open access versionFindings
您的评分 :
0

 

标签
评论
数据免责声明
页面数据均来自互联网公开来源、合作出版商和通过AI技术自动分析结果,我们不对页面数据的有效性、准确性、正确性、可靠性、完整性和及时性做出任何承诺和保证。若有疑问,可以通过电子邮件方式联系我们:report@aminer.cn
小科