Abstract
The concept of context awareness has been in existence since the 1990s. Though initially applied exclusively in computer science, over time it has increasingly been adopted by many different application domains such as business, health and military. Contexts change continuously because of objective reasons, such as economic situation, political matter and social issues. The adoption of big data analytics by businesses is facilitating such change at an even faster rate in much complicated ways. The potential benefits of embedding contextual information into an application are already evidenced by the improved outcomes of the existing context-aware methods in those applications. Since big data is growing very rapidly, context awareness in big data analytics has become more important and timely because of its proven efficiency in big data understanding and preparation, contributing to extracting the more and accurate value of big data. Many surveys have been published on context-based methods such as context modelling and reasoning, workflow adaptations, computational intelligence techniques and mobile ubiquitous systems. However, to our knowledge, no survey of context-aware methods on big data analytics for business applications supported by enterprise level software has been published to date. To bridge this research gap, in this paper first, we present a definition of context, its modelling and evaluation techniques, and highlight the importance of contextual information for big data analytics. Second, the works in three key business application areas that are context-aware and/or exploit big data analytics have been thoroughly reviewed. Finally, the paper concludes by highlighting a number of contemporary research challenges, including issues concerning modelling, managing and applying business contexts to big data analytics.
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References
Sokol L, Chan S (2013) Context-based analytics in a big data world: better decisions. IBM Redbooks Point-of-View Publication, Armonk
Hariri N, Bamshad M, Robin B (2013) Query-driven context aware recommendation. In: ACM conference on recommender systems
Aknouche R, Asfari O, Bentayeb F, Boussaid O (2012) Integrating query context and user context in an information retrieval model based on expanded language modeling. In: Quirchmayr G, Basl J, You I, Xu L, Weippl E (eds) Multidisciplinary research and practice for information systems. Springer, Berlin
Li K, Jiang H, Yang LT, Cuzzocrea A (2015) Big data: algorithms, analytics, and applications. CRC Press, Boca Raton
Fan W, Bifet A (2013) Mining big data: current status, and forecast to the future. ACM sIGKDD Explor Newslett 14(2):1–5
Abowd GD, Dey AK, Brown PJ, Davies PJ, Smith N, Steggles P (1999) Towards a better understanding of context and context-awareness. In: Gellersen HW (ed) Handheld and ubiquitous computing. Springer, Berlin
Lorentz A (2013) With big data context is a big issue. http://www.wired.com/insights/2013/04/with-big-data-context-is-a-big-issue/. Accessed 5 May 2016
Bettini C, Brdiczka O, Henricksen K, Indulska J, Nicklas D, Ranganathan A, Riboni D (2010) A survey of context modelling and reasoning techniques. Pervasive Mob Comput 6(2):161–180
Smanchat S, Ling S, Indrawan M (2008) A survey on context-aware workflow adaptations. In: Advances in mobile computing and multimedia (MoMM)
Liu W, Li X, Huang D (2011) A survey on context-awareness. In: Computer science and service system (CSSS)
Bellavista P, Corradi A, Fanelli M, Foschini L (2012) A survey of context data distribution for mobile ubiquitous systems. ACM Comput Surv (CSUR) 44(4):24:1–24:45
Abbas A, Zhang L, Khan SU (2015) A survey on context-aware recommender systems based on computational intelligence techniques. Computing 97(7):667–690
George G, Haas MR, Pentland A (2014) Big data and management. Acad Manag J 57(2):321–326
Chen M, Mao S, Liu Y (2014) Big data: a survey. Mob Netw Appl 19(2):171–209
Rout T, Senapati MR, Garanayak M, Kamilla SK (2015) Big data and its applications: a review. In: International conference on electrical, electronics, signals, communication and optimization (EESCO)
Mishra S, Dhote V, Prajapati GS, Shukla JP (2015) Challenges in big data application: a review. Int J Comput Appl 121(19):42–46
Bibri SE, Krogstie J (2017) The core enabling technologies of big data analytics and context-aware computing for smart sustainable cities: a review and synthesis. J Big Data 4(1):38
Assunção MD, Calheiros RN, Bianchi S, Netto MA, Buyya R (2015) Big data computing and clouds: trends and future directions. J Parallel Distrib Comput 79:3–15
Uddin MF, Gupta N (2014) Seven V’s of big data understanding big data to extract value. In: Zone 1 conference of the american society for engineering education (ASEE Zone 1)
Fan J, Han F, Liu H (2014) Challenges of big data analysis. Natl Sci Rev 1(2):293–314
Russom P (2011) Big data analytics. TDWI best practices report, fourth quarter
Rajendra A (2013) Big data computing. CRC Press, Boca Raton
Sagiroglu S, Sinanc D (2013) Big data: a review. In: International conference on collaboration technologies and systems (CTS)
Manyika J, Chui M, Brown B, Bughin J, Dobbs R, Roxburgh C, Byers AH (2011) Big data: the next frontier for innovation, competition and productivity. Mckensey Global Institute, New York
Gandomi A, Haider M (2015) Beyond the hype: big data concepts methods and analytics. Int J Inf Manag 35(2):137–144
Loshin D (2013) Big data analytics: from strategic planning to enterprise integration with tools, techniques, noSQL, and Graph. Morgan Kaufmann Publishers Inc, San Francisco
B. R. Presentation (2012) The challenge of big data. Ventana Research. http://www.ventanaresearch.com/uploadedFiles/Content/Landing_Pages/Ventana_Research_Big_Data_Benchmark_Research_Presentation.pdf. Accessed 19 Aug 2015
Techrepublic.com. Tech Republic Company
Ghazal A, Rabl T, Hu M, Raab F, Poess M, Crolotte A, Jacobsen H-A (2013) Big bench: towards an industry standard benchmark for big data analytics. In: The ACM SIGMOD international conference on management of data (SIGMOD)
Elgendy N, Elragal A (2014) Big data analytics: a literature review paper. In: Perner P (ed) Advances in data mining. Applications and theoretical aspects, vol 8557. Springer, Cham, pp 214–227
Gantz J, Reinsel D (2012) The digital universe in 2020: big data, bigger digital shadows, and biggest growth in the far east. IDC iView IDC Anal Future 2007:1–16
Lee S, Park S, Lee SG (2009) A study on issues in context-aware systems based on a survey and service scenarios. In: Software engineering, artificial intelligences, networking and parallel/distributed computing
Vara JLDL, Ali R, Dalpiaz F, Sanchez J, Giorgini P (2010) Business processes contextualization via context analysis. Concept Model ER 6412:471–476
Boutanmina S, Maamri R (2015) A survey on context-aware workflow systems. In: Intelligent information processing, security and advanced communication
Ejigu D, Scuturici M, Brunie L (2007) An ontology-based approach to context modelling and reasoning in pervasive computing. In: Pervasive computing and communications workshops
Tan PS, Goh AES, Lee SSG (2010) An ontology to support context-aware B2B services. In: Services computing
Leppanen M (2007) A context-based enterprise ontology. In: Abramowicz W (ed) Business information systems. Springer, Berlin
Dinh LTN, Karmakar G, Kamruzzaman J, Stranieri A (2015) Business context in big data analytics. In: International conference on information, communications and signal processing (ICICS)
Kroschel I (2010) On the notion of context for business process use. In: ISSS/BPSC
Brown PJ, Bovey JD, Chen X (1997) Context-aware applications: from the laboratory to the marketplace. Pers Commun 4(5):58–64
Ploesser K, Peleg M, Soffer P, Rosemann M, Recker JC (2009) Learning from context to improve business processes. BPTrends 6(1):1–7
Bai J, Nie JY, Cao G, Bouchard H (2007) Using query contexts in information retrieval. In: The 30th annual international ACM SIGIR conference on research and development in information retrieval
Cao H, Hu DH, Shen D, Jiang D, Sun JT, Chen E, Yang Q (2009) Context-aware query classification. In: International ACM SIGIR conference on research and development in information retrieval
Dey AK (2001) Understanding and using context. Pers Ubiquit Comput 5(1):4–7
Coutaz J, Crowley JL, Dobson S, Garlan D (2005) Context is key. Commun ACM 48(3):49–53
Wirth R, Hipp J (2000) CRISP-DM: towards a standard process model for data mining. In: International conference on the practical applications of knowledge discovery and data mining
Big data—a new world of opportunities (2012). http://www.nessi-europe.eu/Files/Private/NESSI_WhitePaper_BigData.pdf. Accessed 15 Jan 2016
Turkel WJ, Crymble A (2012) Keywords in context (using n-grams) with Python. The Programming Historian 1
Tan PS, Goh AES, Lee SSG (2010) A context model to support B2B collaboration. In: Sheng QZ, Yu J, Dustdar S (eds) Enabling context-aware web services: methods, architectures, and technologies. CRC Press, Boca Raton, pp 243–271
Tan PS, Lee SSG, Goh AES, Lee EW (2007) Context-enabled B2B collaborations. In: International conference on services computing (SCC)
Saidani O, Nurcan S (2007) Towards context aware business process modeling. In: Workshop on business process modeling, development, and support (BPMDS’07), CAiSE
Rosemann M, Recker J, Flender C (2008) Contextualisation of business processes. Int J Bus Process Integr Manag 3(1):47–60
Ruthven I (2011) Information retrieval in context. Adv Top Inf Retr 33:187–207
Mostéfaoui GK, Brézillon P (2003) A generic framework for context-based distributed authorizations. In: International and interdisciplinary conference on modeling and using context. Springer, Berlin
Ali R, Dalpiaz F, Giorgini P (2010) A goal-based framework for contextual requirements modeling and analysis. Requir Eng 15(4):439–458
Kitchenham B, Charters S (2007) Guidelines for performing systematic literature reviews in software engineering. EBSE Tech Rep 2(3):1–65
Hendricks KB, Singhal VR, Stratman JK (2007) The impact of enterprise systems on corporate performance: a study of ERP, SCM, and CRM system implementations. J Oper Manag 25(1):65–82
Daneshgar F (2005) Context-aware framework for ERP. In: Khosrow-Pour M (ed) Encyclopedia of information science and technology, vol 27. IGI Global, Pennsylvania, pp 105–117
Rajan CA, Baral R (2015) Adoption of ERP system: an empirical study of factors influencing the usage of ERP and its impact on end user. IIMB Manag Rev 27(2):105–117
Bradford M, Florin J (2003) Examining the role of innovation diffusion factors on the implementation success of enterprise resource planning systems. Int J Account Inf Syst 4(3):205–225
Babu MSP, Sastry SH (2014) Big data and predictive analytics in ERP systems for automating decision making process. In: IEEE 5th international conference on software engineering and service science, Beijing
Shi Z, Wang G (2018) Integration of big-data ERP and business analytics (BA). J High Technol Manag Res 29:141–150
Vasarhelyi MA, Kogan A, Tuttle BM (2015) Big data in accounting: an overview. Account Horiz 29(2):381–396
Angrave D, Charlwood A, Kirkpatrick I, Lawrence M, Stuart M (2016) HR and analytics: why HR is set to fail the big data challenge. Hum Resour Manag J 26(1):1–11
Jain N (2018) Big data and predictive analytics: a facilitator for talent management. In: Munshi U, Verma N (eds) Data science landscape. Studies in big data, vol 38. Springer, Singapore
Liu F, Guo W, Wang H, Li X (2019) Data science and big data technology professional talent demand and training system construction. In: 9th international conference on education and social science (ICESS 2019)
Khazaeli M, Javadpour L, Knapp GM (2015) ERP adoption in enterprises with emerging big data. In: IIE annual conference, institute of industrial and systems engineers (IISE)
Park SC, Im KH, Suh JH, Kim CY, Kim JW (2007) Ubiquitous customer relationship management (uCRM). In: International conference on rough sets and knowledge technology. Springer, Berlin
Geihs K, Reichle R, Wagner M, Khan MU (2009) Modeling of context-aware self-adaptive applications in ubiquitous and service-oriented environments. In: Cheng BHC, de Lemos R, Giese H, Inverardi P, Magee J (eds) Software engineering for self-adaptive systems. Springer, Berlin, pp 146–163
Architecture for a context-aware CRM. http://www.intuital.com/
Nguyen T, Zhou L, Spiegler V, Ieromonachou P, Lin Y (2018) Big data analytics in supply chain management: a state-of-the-art literature review. Comput Oper Res 98:254–264
Mishra D, Gunasekaran A, Papadopoulos T, Childe SJ (2018) Big data and supply chain management: a review and bibliometric analysis. Ann Oper Res 270(1–2):313–336
Choi Y, Lee H, Irani Z (2018) Big data-driven fuzzy cognitive map for prioritising IT service procurement in the public sector. Ann Oper Res 270(1–2):75–104
Tan MH, Lee WL (2015) Evaluation and improvement of procurement process with data analytics. Int J Adv Comput Sci Appl 6(8):70
Zhang Y, Ren S, Liu Y, Si S (2017) A big data analytics architecture for cleaner manufacturing and maintenance processes of complex products. J Clean Prod 142:626–641
Helo P, Hao Y (2017) Cloud manufacturing system for sheet metal processing. Prod Plan Control 28(6–8):524–537
Krumeich J, Werth D, Loos P (2016) Prescriptive control of business processes. Bus Inf Syst Eng 58(4):261–280
Li J, Moghaddam M, Nof SY (2016) Dynamic storage assignment with product affinity and ABC classification—a case study. Int J Adv Manuf Technol 84(9–12):2179–2194
Li B, Ch’ng E, Chong AYL, Bao H (2016) “Predicting online e-marketplace sales performances: a big data approach. Comput Ind Eng 101:565–571
Walker G, Strathie A (2016) Big data and ergonomics methods: a new paradigm for tackling strategic transport safety risks. Appl Ergon 53:298–311
Ting SL, Tse YK, Ho GTS, Chung SH, Pang G (2014) Mining logistics data to assure the quality in a sustainable food supply chain: a case in the red wine industry. Int J Prod Econ 152:200–209
Mehmood R, Meriton R, Graham G, Hennelly P, Kumar M (2017) Exploring the influence of big data on city transport operations: a Markovian approach. Int J Oper Prod Manag 37(1):75–104
Chong AYL, Li B, Ngai EWT, Ch’ng E, Lee F (2016) Predicting online product sales via online reviews, sentiments, and promotion strategies: a big data architecture and neural network approach. Int J Oper Prod Manag 36(4):358–383
Salehan M, Kim DJ (2016) Predicting the performance of online consumer reviews: a sentiment mining approach to big data analytics. Decis Support Syst 81:30–40
Wu KJ, Liao CJ, Tseng ML, Lim MK, Hu J, Tan K (2017) Toward sustainability: using big data to explore the decisive attributes of supply chain risks and uncertainties. J Clean Prod 142:663–676
Papadopoulos T, Gunasekaran A, Dubey R, Altay N, Childe SJ, Fosso-Wamba S (2017) The role of big data in explaining disaster resilience in supply chains for sustainability. J Clean Prod 142:1108–1118
Moss LT, Atre S (2003) Business intelligence roadmap: the complete project life cycle for decision-support applications. Addison-Wesley Professional, Boston
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Dinh, L.T.N., Karmakar, G. & Kamruzzaman, J. A survey on context awareness in big data analytics for business applications. Knowl Inf Syst 62, 3387–3415 (2020). https://doi.org/10.1007/s10115-020-01462-3
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DOI: https://doi.org/10.1007/s10115-020-01462-3