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

Mariano d’Amore (), Rodolfo Baggio and Enrico Valdani ()
Additional contact information
Mariano d’Amore: Bocconi University
Enrico Valdani: Bocconi University

A chapter in Information and Communication Technologies in Tourism 2015, 2015, pp 169-181 from Springer

Abstract: Abstract 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.

Keywords: Raspberry Pi; Big data; Online social networks; Tourism organisations (search for similar items in EconPapers)
Date: 2015
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-14343-9_13

Ordering information: This item can be ordered from
http://www.springer.com/9783319143439

DOI: 10.1007/978-3-319-14343-9_13

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().