Big Data as an Information Source in the Decision Making-Processes of the E-Commerce Companies

• Autorzy: Dariusz Weiland
• Języki publikacji: EN

Abstrakty

EN

This article discusses the opportunities offered by the use of Big Data in e-commerce and presents this tool as a source of information affecting the decision-making process. Some sections are devoted to introducing and presenting the perspective on information as a resource, while others attempt to define Big Data and outline the way in which Big Data may be utilised as a source of information supply in e-commerce; further parts elaborate on the challenges that information logistics has to face in order to make Big Data more adaptable in e-commerce. (original abstract)

Słowa kluczowe

EN

e-commerce; Logistics; Information; Big Data

PL

Handel elektroniczny; Logistyka; Informacja; Big Data

• Czasopismo: Research Journal of the University of Gdańsk. Transport Economics and Logistics
• Rocznik: 2017
• Tom: 71 Modeling of Logistic Processes and Systems
• Strony: 179--190

Twórcy

autor

Dariusz Weiland

• University of Gdańsk, Poland

Bibliografia

• Brown, B., Chul, M. and Manyika, J. (2011), Are you ready for the era of 'big data'?, McKinsey Quarterly, October.
• Bukowski, L. (2004), Problemy przetwarzania informacji logistycznych w zintegrowanych systemach produkcyjnych. Wybrane zagadnienia logistyki stosowanej, Materiały VII Konferencji Logistyki Stosowanej - Total Logistic Management, Oficyna Wydawnicza TEST, Kraków.
• Chaberek, M. (2002), Makro- i mikroekonomiczne aspekty wsparcia logistycznego, Wydawnictwo Uniwersytetu Gdańskiego, Gdańsk, p. 15.
• Chaberek, M. (2010), Praktyczne i teoretyczne aspekty kontaminacji i atomizacji logistyki i informatyki ekonomicznej, In: Chaberek, M. and Jezierski, A. (Eds.), Informatyczne narzędzia procesów logistycznych, CeDeWu, Warszawa, p. 13.
• Chang, C., Kayed, M., Girgis, M.R. and Shaalan, K.F. (2006), A survey of web information extraction systems, IEEE Transactions on Knowledge and Data Engineering, 18(10), pp. 1411-1428.
• Cho, Y.H. and Kim, J.K. (2004), Application of Web usage mining and product taxonomy to collaborative recommendations in e-commerce, Expert systems with Applications, 26(2), pp. 233-246.
• Davenport, T.H., Barth, P. and Bean, R. (2012), How Big Data Is Different, MIT Sloan Management Review, 54(1), pp. 43-46.
• Doug, L. (2001), Data Management: Controlling Data Volume, Velocity, and Variety, "Application Delivery Strategies", META Group (currently with Gartner).
• Fosso Wamba, S., Akter, S., Edwards, A., Chopin, G. and Gnanzou, D. (2015), How 'big data' can make big impact: findings from a systematic review and a longitudinal case study, International Journal of Production Economics, 165, pp. 234-246.
• Forrester (2012), The Big Deal About Big Data For Customer Engagement. Available from https://www.forrester.com/report/The+Big+Deal+About+Big+Data+For+Customer+Engagement/-/ E-RES72241 [Accessed 15 September 2017].
• Gartner (2017). Available from http://www.gartner.com/it-glossary/big-data/ [Accessed 15 September 2017].
• Greenberg, A., Hamilton, J., Maltz, D.A. and Patel, P. (2008), The cost of a cloud: research problems in data center networks, ACM SIGCOMM Computer Communication Review, 39(1), pp. 68-73.
• Heracleous, L. (1998), Better than the rest: Making Europe the leader in the next wave of innovation and performance, Long Range Planning, February.
• IBM (2011). Available from https://www-01.ibm.com/software/in/data/bigdata/ [Accessed 28 April 2017].
• IDC (2012). Available from https://www.emc.com/collateral/analyst-reports/idc-digital-univers e-united-states.pdf [Accessed 15 September 2017].
• Jinchuan, C., Yueguo, C., Xiaoyong, D., Cuiping, L., Jiaheng, L., Suyun, Z. and Xuan, Z. (2013), Big data challenge: a data management perspective, Frontiers of Computer Science, 7(2), pp. 157-164.
• Johnson, B.D. (2012), The Secret Life of Data, The Futurist, 46(4), pp. 20-23.
• Kanellos, M. (2016), Amount of Data Created Annually to Reach 180 Zettabytes in 2025. Available from https://whatsthebigdata.com/2016/03/07/amount-of-data-created-annually-to-reach-180-zettabytes-in-2025/ [Accessed 28 April 2017].
• Kozłowski, R. and Sikorski, A. (2013), Nowoczesne rozwiązania w logistyce, Wolters Kluwer business, Warszawa.
• Kwon, O. and Sim, J.M. (2012), Effects of data set features on the performances of classification algorithms, Expert Systems with Applications, 40(5), pp. 1847-1857.
• Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C. and Byers, A.H. (2011), Big data: the next frontier for innovation, competition and productivity, Report McKinsey Global Institute, May.
• McAfee, A. and Brynjolfsson, E. (2012), Big data: the management revolution, Harvard Business Review, 1.
• McGrath, R.G. (2013), Transient Advantage, Harvard Business Review, 91(6), pp. 62-70.
• Oracle (2013). Available from http://www.oracle.com/us/products/database/big-data-forenterprise-519135.pdf [Accessed 15 September 2017].
• Polańska, K. and Wassilew, A. (2015), Analizy "big data" w serwisach społecznościowych, Nierówności Społeczne a Wzrost Gospodarczy, 44(4), p. 118.
• Rahm, E. and Do, H.H. (2000), Data cleaning: Problems and current approaches, IEEE Data Engineering Bulletin Issues, 23(4), pp. 3-13.
• Rouse, M. (2011), big data (Big Data). Available from http://searchcloudcomputing.techtarget.com/definition/big-data-Big-Data [Accessed 15 September 2017].
• Russom, P. (2011), The Three Vs of Big Data Analytics, TDWI.
• SAS (2014). Available from https://www.sas.com/en_us/insights/big-data/what-is-big-data.html [Accessed 28 April 2017].
• Szmelter, A. (2013), Business Intelligence jako element systemu zaopatrzenia informacyjnego, Roczniki Wyższej Szkoły Bankowej w Toruniu, 12, p. 129.
• Vorhies, W. (2014), How many V's in Big Data? The Characteristics that Define Big Data. Available from http://www.datasciencecentral.com/profiles/blogs/how-many-v-s-in-big-data-thecharacteristics-that-define-big-data [Accessed 28 April 2017].
• Ward, J.S. and Barker, A. (2013), Undefined by Data: A Survey of Big Data Definitions. Available from https://arxiv.org/pdf/1309.5821v1.pdf [Accessed 28 April 2017].
• Wei, L., Xiaofeng, M. and Weiyi, M. (2010), ViDE: A Vision-Based Approach for Deep Web Data Extraction, IEEE Transactions on Knowledge and Data Engineering, 22(3), pp. 447-460.
• What is Apache Hadoop? (2014). Available from https://hadoop.apache.org [Accessed 28 April 2017].
• Wit, B. (2008), Electronic commerce - budowanie konkurencyjności przedsiębiorstwa w Internecie, Wydawnictwo Politechniki Lubelskiej, Lublin.

Identyfikatory

DOI

DOI 10.5604/01.3001.0010.5734