[雙語(yǔ)翻譯]大數(shù)據(jù)外文翻譯--中小企業(yè)增長(zhǎng)的大數(shù)據(jù)概述（英文）

1、 Procedia - Social and Behavioral Sciences 235 ( 2016 ) 159 – 167 Available online at www.sciencedirect.com ScienceDirect1877-0428 © 2016 Published by Elsevier Ltd. This is an open access article under the CC

2、 BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of ISMC 2016. doi: 10.1016/j.sbspro.2016.11.011 12th International Strategic Managemen

3、t Conference, ISMC 2016, 28-30 October 2016, Antalya, Turkey An Overview of Big Data for Growth in SMEs Doruk Sena, Melike Ozturkb , Ozalp Vayvayc,? a,b,c Marmara University, Istanbul, 34722, Turkey Abstract Potentials a

4、nd promises of Big Data are significant for SMEs. Big Data can nurture alliance in SMEs by creating real-time solutions to challenges in every industry. This can be achieved by utilizing the openness for decision-making

5、. SMEs are specifically selected within context for two main reasons: a) A small change in SMEs can have larger macro level effect due to their overall position in the economy; b) They have the advantage and flexibilit

6、y for quicker adaptation to changes towards efficiency. The Big Data context, however, still have significant contentious issues as; storage, capabilities of the companies in terms of processing and generating sensible

7、 information from it, and last but not least, security and privacy. This article is aimed to propose grounds for future Big Data research for SMEs by examining main potentials and threats that have to be addressed. Fina

8、lly, we recommend potential practices that will help SMEs and researchers for capturing the full potential of Big Data. © 2016 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the organi

9、zing committee of ISMC 2016. Keywords: Big Data ; SME ; Growth 1. Introduction We are having a huge information explosion across the world. Before communication era –often stated as 20-30 years ago– the amount of informa

10、tion was increasing arithmetically. Today, information is expanding in geometric series. 10 years ago, telescopes were able to draw the map of the universe with 50% confidence. Today, this number is increased to 90%. I

11、n 2000, the most developed telescope in the world was located in New Mexico, USA – Apache Point Observatory-. Within a month time, it was able to provide more data than all historical data of modern astronomy. Nowadays

12、, something different is being faced. ALMA Observatory -located in Atacama Desert, Chile- will produce more data than astronomy history in every five days. ALMA is also 10 times stronger than Hubble in ? Corresponding a

13、uthor. Tel. +90-216-347-1360 Email address: ozalp@marmara.edu.tr © 2016 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). P

14、eer-review under responsibility of the organizing committee of ISMC 2016.161Doruk Sen et al. / Procedia - Social and Behavioral Sciences 235 ( 2016 ) 159 – 167 the reason, it is fair to say that people are search

15、ing for it on Google more and more to have an insight about it as shown in figures 1. Fig 1. Google Trends for Big Data Source: Google Huge amount of data is produced with smartphones’ increased role in our daily live

16、s. E-mails sent, posts on social media (text, image, and video), credit card purchases, phone calls, places we have been being all parts of big data. When the data gathered by machines (i.e. smart counters, sensors etc

17、.) are included, data sets reach enormous sizes. This concept brings the potential of improved decision making and performance outputs alongside with smart decision support systems (McAfee and Brynjolfsson, 2012). Not

18、only the science itself but also ‘we’ as human beings are forming the Big Data as well. To expand it further, Google can detect the location of potential epidemic diseases earlier than states, and international organiza

19、tions and warns them afterwards by using Big Data (i.e. Google Flu Trends). This is done by observing Google searches and resulting with prediction of the disease earlier than them. In addition to this, researchers ide

20、ntified 45 search terms by testing 450 million models that predict the flu spread more rapidly than the Centres for Disease Control (Lenard, 2014). Furthermore, insurance companies may realize the illness potential of

21、a person by checking the amount spent for alcohol with credit card purchases. Signals sent by mobile network operators (MNOs) are other significant data sources. Both health organization and insurance company of a pers

22、on may become aware of his/her potential cancer disease with mobile signals if a person usually visits locations near nuclear power plants. Moreover, traffic jam can be predicted with a similar way by receiving mobile

23、signals of people who are on the same direction (Simcoe, 2015). The study of Lenard (2014), the big data analysis as demonstrated by Google Flu, involves patterns and correlations typically involves data use that were n

24、ot predicted at the time the data were collected. Many scholars emphasize that the age of big data couldn’t have been imagined before the use of the first data collected. Hence, big data is expanded the primary purpose

25、 of the valuable information. What’s more, distinctive approaches are used in London and some cities in the USA. In those countries, police system warns with the detection of anomalous activities of criminals who were p

26、reviously committed serious crimes. Ambulance and police can be allocated to that region in advance to prevent a possible crime and minimize the risk if many ex-convicts go towards the same direction. This example can

27、be considered as the use of Big Data for public welfare. In energy industry, smart grid user can be a buyer or seller in the electricity market, a participant to help low carbon-emission targets, or a stakeholder in in