WikipediaFS explained
WikipediaFS is a virtual filesystem which allows users to view and edit the articles of any MediaWiki-based site as if they were real files on a local disk drive. This enables a user to edit articles directly with any text editor.[2] WikipediaFS is developed primarily by Mathieu Blondel on SourceForge.net.[3]
WikipediaFS is implemented in Python and uses the FUSE kernel module. The file system works by lazily downloading and uploading article sourcetexts - only sending HTTP requests to the selected site when a file is accessed. (Reading a file corresponds to a GET HTTP request, writing to a POST HTTP request.)
Advantages
- Editing a long Wikipedia article can sometimes turn out to be painful and time-consuming due to web-forms limitations. Instead, when WikipediaFS is mounted on a directory, articles become like real files in that directory: it is thus possible to use a text-editor to edit files. Text-editors are generally more stable and less sluggish than browsers and have useful features such as spell checking and wiki syntax highlighting.
- It is possible to write programs or bots transparently as if they dealt with simple files because WikipediaFS takes care of the HTTP layer. For example, WikipediaFS could be used to perform a massive content migration from one MediaWiki site to another.
Disadvantages
- The project is no longer maintained as of 2007; the software has since been deprecated.
- It is difficult to go from-page-to-page; users must know the exact name of a page in order to edit it, as WikipediaFS has no local knowledge of what pages exist and which don't.
See also
External links
Notes and References
- Web site: WikipediaFS / Code Commit Log.
- Web site: Jason . Striegel . WikipediaFS – a Linux MediaWiki file-system . MAKE . 6 May 2007 . 2012-02-10.
- Web site: WikipediaFS 0.3 released . https://web.archive.org/web/20120218223109/http://www.mblondel.org/journal/2007/05/27/wikipediafs-03-released/ . 2007-05-27 . 2012-02-18 . 2016-04-17 . www.mblondel.org/journal/ Mathieu's log: Machine Learning, Data Mining, Natural Language Processing...... .
