A Practical Approach to Big Data in Tourism: A Low Cost Raspberry Pi Cluster

Big Data is the contemporary hype. However, not many companies or organisations have the resources or the capabilities to collect the huge amounts of data needed for a significant and reliable analysis. The recent introduction of the Raspberry Pi, a low-cost, low-power single-board computer gives an affordable alternative to traditional workstations for a task that requires little computing power but immobilises a machine for long elapsed times. Here we present a flexible solution, devised for small and medium sized organisations based on the Raspberry Pi hardware and open source software which can be employed with relatively little effort by companies and organisations for their specific objectives. A cluster of six machines has been put together and successfully used for accessing and downloading the data available on a number of social media platforms.

[1]  Erik Cambria,et al.  Big Social Data Analysis , 2013 .

[2]  Katharine Armstrong,et al.  Big data: a revolution that will transform how we live, work, and think , 2014 .

[3]  R. Law,et al.  Social Media in Tourism and Hospitality: A Literature Review , 2013 .

[4]  Han Liu,et al.  Challenges of Big Data Analysis. , 2013, National science review.

[5]  Stephen Denning.  The management revolution's growing army of rebel voices , 2013 .

[6]  Erik Brynjolfsson,et al.  Big data: the management revolution. , 2012, Harvard business review.

[7]  Viktor Mayer-Schnberger,et al.  Big Data: A Revolution That Will Transform How We Live, Work, and Think , 2013 .

[8]  Aristides Gionis,et al.  Social Network Analysis and Mining for Business Applications , 2011, TIST.

[9]  Athanasios V. Vasilakos,et al.  Big data: From beginning to future , 2016, Int. J. Inf. Manag..

[10]  Alex Priem,et al.  Innovation of tourism statistics through the use of new big data sources , 2014 .

[11]  Gareth Halfacree,et al.  Raspberry Pi User Guide , 2012 .

[12]  D. Lazer,et al.  The Parable of Google Flu: Traps in Big Data Analysis , 2014, Science.

[13]  Rajendra Akerkar,et al.  Big Data Computing , 2013 .

[14]  Ben Shneiderman,et al.  Analyzing Social Media Networks with NodeXL: Insights from a Connected World , 2010 .

[15]  Fung Po Tso,et al.  The Glasgow Raspberry Pi Cloud: A Scale Model for Cloud Computing Infrastructures , 2013, 2013 IEEE 33rd International Conference on Distributed Computing Systems Workshops.

[16]  A. Guerry,et al.  Using social media to quantify nature-based tourism and recreation , 2013, Scientific Reports.

[17]  Andreas Jungherr,et al.  Forecasting the pulse: How deviations from regular patterns in online data can identify offline phenomena , 2013, Internet Res..

[18]  Florian Daniel,et al.  Current Trends in Web Engineering , 2010, Lecture Notes in Computer Science.

[19]  Ben Shneiderman,et al.  Analyzing Social Media Networks with NodeXL: Insights from a Connected World , 2010 .

[20]  Charles R. Severance,et al.  Eben Upton: Raspberry Pi , 2013, Computer.

[21]  Dimitrios Buhalis,et al.  Social media as a destination marketing tool: its use by national tourism organisations , 2013 .

[22]  Benxiang Zeng.  Social Media in Tourism , 2013 .

[23]  Rudolph Bedeley,et al.  Big Data Analytics: A Key Capability for Competitive Advantage , 2014 .

[24]  Xing Cai,et al.  On the performance of the Python programming language for serial and parallel scientific computations , 2005, Sci. Program..

[25]  Dongman Lee,et al.  Identifying User Interests from Online Social Networks by Using Semantic Clusters Generated from Linked Data , 2013, ICWE Workshops.

[26]  Alessandro Lovari,et al.  Social media tourism , 2012 .

[27]  D. Boyd,et al.  CRITICAL QUESTIONS FOR BIG DATA , 2012 .

[28]  Ewan Klein,et al.  Natural Language Processing with Python , 2009 .